Design and control of intelligent heterogeneous multi-configurable chained microrobotic modular systems

Texto completo

(1)UNIVERSIDAD POLITÉCNICA DE MADRID ESCUELA TÉCNICA SUPERIOR DE INGENIEROS INDUSTRIALES. Design and Control of Intelligent Heterogeneous Multi-configurable Chained Microrobotic Modular Systems. PhD Thesis. Alberto Brunete González Ingeniero de Telecomunicación. 2010.

(2)

(3) DEPARTAMENTO DE AUTOMÁTICA, INGENIERÍA ELECTRÓNICA E INFORMÁTICA INDUSTRIAL ESCUELA TÉCNICA SUPERIOR DE INGENIEROS INDUSTRIALES. Design and Control of Intelligent Heterogeneous Multi-configurable Chained Microrobotic Modular Systems PhD Thesis. Alberto Brunete González Ingeniero de Telecomunicación Supervisors Ernesto Gambao Galán Doctor Ingeniero Industrial Miguel Hernando Gutiérrez Doctor Ingeniero Industrial. 2010.

(4)

(5) Tı́tulo: Design and Control of Intelligent Heterogeneous Multi-configurable Chained Microrobotic Modular Systems Autor: Alberto Brunete González Ingeniero de Telecomunicación. (D-15). Tribunal nombrado por el Magfco. y Excmo. Sr. Rector de la Universidad Politécnica de Madrid, el dı́a de de 2010. Presidente: Vocal: Vocal: Vocal: Secretario: Suplente: Suplente:. Realizado el acto de lectura y defensa de la tesis el dı́a en la E.T.S.I. / Facultad. El Presidente:. Los Vocales:. El Secretario:. de. de.

(6)

(7) Dedication Version 0.95. vii.

(8) viii.

(9) Abstract The objective of this thesis is the “Design and Control of Intelligent Heterogeneous Multiconfigurable Chained Microrobotic Modular Systems”. That is, the development of modular microrobots composed of different types of modules able to perform different types of movements (gaits), that can have different (chained) configurations depending on the task to perform. Heterogenous is the key word in this thesis. It is possible to find in literature many designs concerning modular robots, but almost all of them are homogenous: all are composed of the same modules except for some designs having two different modules but one of them passive. In this thesis, several active modules are proposed (rotation, support, extension, helicoidal, etc.) that can be combined and execute different gaits. The original idea was to make the robots as smaller as possible, reaching in the end a final diameter of 27mm. Although they are not really microrobots, they are in the mesoscale (from hundreds of microns to tens of centimeters) and in literature they are called for simplicity minirobots or microrobots. Several modules have been developed: the rotation module (indeed it is a double rotation module, but for simplicity it is called rotation module) v1 and v2, the helicoidal module v1 and v2, the support module v1, v1.1 and v2, the extension module v1 and v2, the camera module v1 and v2, the contact module (it is included in the camera module v2) and the battery module. Some others are still in the design or conceptual phase, but they can be simulated. They are the SMA-based module (there is already a prototype), the traveler module (in the design phase) and the sensor module (in a conceptual phase). All modules have been designed with the idea to miniaturized them in the future, and so both the electronic and the embedded control programs are as simple as possible (maintaining the planned functionality). Parallel to the construction of the modules a simulator has been developed to provide a very efficient way of prototyping and verification of control algorithms, hardware design, and exploring system deployment scenarios. It is built upon an existing open source implementation of rigid body dynamics, the Open Dynamics Engine (ODE). Simulated modules have been designed as simple as possible (using simple primitives) to make simulation fluid, but trying to reflect as much as possible its real physic conditions and parameters, its electronics and communication buses, and the software embedded in the modules. The simulator has been validated using the information gathered from real modules experiments and this has helped to adjust the parameters of the simulator to have an accurate model. Although the first idea was to develop the microrobot for pipe inspection, the experience acquired with the first prototypes causes to realize that locomotion systems used inside pipes could also be suitable outside them, and that the prototypes and the control. ix.

(10) architecture were useful in open spaces. In this way, research was extended to open spaces and the ego-positioning system was added. The EGO-positioning system is a method that allows all individual robots of a swarm to know their own positions and orientations based in the projection of sequences of coded images composed of horizontal and vertical stripes over photodiodes placed on the robots. This concept can also be applied to the modules in order for them to know their position and orientation, and to send commands to all of them at the same time. To manage all of this a control architecture based on behaviors has been developed. Since the modules cannot have a big processor, a central control is included in the architecture to take the high level control. The central control has a model-based subpart and another part based on behaviors. The embedded control in the modules is entirely behavior-based. Between this two there is an heterogenous agent (layer) that allows the central control to treat all modules in the same way, since the heterogenous layer translates its commands into module specific commands. A behavior-based architecture has been chosen because it is specifically appropriate for designing and controlling biologically inspired robots, it has proven to be suitable for modular systems and it integrates very well both low and high level control. In order to communicate all actors (behaviors, modules and central control), a communication protocol based on I2C has been developed. It allows to send messages from the operator to the central control, from central control to the modules and between behaviors. A Module Description Language (MDL) has been designed, a language that allows modules to transmit their capabilities to the central control, so it can process this information and choose the best configuration and parameters for the microrobot. Inside the control architecture an offline genetic algorithm has been developed in order to: first, determine the modules to use to have an optimal configuration for an specific task (configuration demand), and second, determine the optimum parameters for best performance for a given module configuration (parameter optimization). Thus, the main contributions that can be found in this thesis are: the design and construction of an Heterogeneous Modular Multi-configurable Chained Microrobot able to perform different gaits (snake-like, inch-worm, helicoidal, combination), the design of a common interface for the modules, a behavior-based control architecture for heterogenous chained modular robot, a simulator for the physics and dynamics (including the design of a servo model), electronics, communications and embedded software routines of the modules, and finally, the enhancement of the ego-positioning system.. x.

(11) Resumen El objetivo de esta tesis es el diseño y control de microrobots inteligentes modulares heterogéneos multiconfigurables de tipo cadena. Es decir, el desarrollo de microrobots modulares compuestos por diferentes tipos de módulos capaces de realizar diferentes tipos de movimientos (gaits en inglés), que pueden ser dispuestos en diferentes configuraciones (siempre en cadena) dependiendo de la tarea a realizar. Heterogéneo es la palabra clave en esta tesis. Es posible encontrar en la literatura muchos diseños sobre robots modulares, pero casi todos ellos son homogéneos: todos se componen de los mismos módulos, excepto en algunos diseños que tienen dos módulos diferentes, pero uno de ellos pasivo. En esta tesis, se proponen varios módulos activos (rotación, soporte, extensión, helicoidales, etc) que se pueden combinar y ejecutar diferentes movimientos, además de otros pasivos (baterı́as, sensores, medición de la distancia recorrida) como complemento a los primeros. La idea original era hacer los robots lo más pequeños posible, alcanzando finalmente un diámetro de 27 mm. Aunque no se puedan considerar técnicamente como microrobots, están en la mesoescala (entre cientos de micras y decenas de centı́metros) y en la literatura se les suele llamar por simplicidad minirrobots o microrrobots. Durante el desarrollo de esta tesis, varios módulos han sido desarrollados: el módulo de rotación (en realidad se trata de un módulo de doble rotación, pero por simplicidad se le llama módulo de rotación) v1 y v2, el módulo helicoidal v1 y v2, el módulo de soporte v1, v1.1 y v2, el módulo de extensión v1 y v2, el módulo de cámara v1 y v2, el módulo de contacto (que está incluido en el módulo de la cámara v2) y el módulo de baterı́a. Algunos otros están todavı́a en fase de diseño o conceptual, pero pueden ser utilizados en la simulación. Son el módulo basado en SMA (ya existe un prototipo), el módulo de medición de distacia recorrida (en fase de diseño) y el módulo de sensores (en fase conceptual). Todos los módulos han sido diseñados con la idea de ser miniaturizados en el futuro, por lo que tanto la electrónica como los programas de control integrados se han hecho tan simples como es posible (manteniendo por supuesto la funcionalidad prevista). Paralelamente a la construcción de los módulos se ha desarrollado un simulador para proporcionar un medio eficaz de creación de prototipos y de verificación de los algoritmos de control, diseño de hardware, y exploración de escenarios de despliegue del sistema. Está construido sobre un software (libre y de código abierto) de simulación de dinámica de cuerpos rı́gidos, el Open Dynamics Engine (ODE). Los módulos simulados se han diseñado de la forma más simple posible (usando primitivas simples) para hacer fluida la simulación, pero tratando de reflejar lo más posible sus condiciones reales y los parámetros fı́sicos, sus componentes electrónicos y buses de comunicación, y el software incluido en los módulos. El simulador ha sido validado con la información obtenida en experimentos con módulos reales, y esto ha ayudado a ajustar los parámetros del simulador para tener. xi.

(12) un modelo preciso. Aunque la primera idea fue desarrollar el microrobot para la inspección de tuberı́as, la experiencia adquirida con los primeros prototipos mostró que los sistemas de locomoción utilizados en el interior de tuberı́as también podrı́an ser adecuados fuera de ellas, y que los prototipos y la arquitectura de control son útiles en espacios abiertos. De esta manera, la investigación se extendió a los espacios abiertos y se añadió el sistema de “ego-positioning”. El sistema de “ego-positioning” es un método que permite a los robots de un enjambre conocer su posición y orientación basadas en la proyección de secuencias de imágenes codificadas compuesto por rayas horizontales y verticales sobre fotodiodos colocados en los robots. Este concepto también puede aplicarse a los módulos de un microrobot para que puedan conocer su posición y orientación, y para enviar comandos a todos ellos al mismo tiempo. Para gestionar todo esto se ha desarrollado una arquitectura de control basada en comportamientos. Dado que los módulos no pueden tener un procesador de grandes capacidades, se incluye en la arquitectura un control central para proporcionar control de alto nivel. El control central tiene una parte basada en modelos y otra parte basada en comportamientos. El control integrado en los módulos está totalmente basado en comportamientos. Entre los dos hay un agente heterogéneo (o capa) que permite que el control central trate a todos los módulos de la misma manera, ya que la capa heterogénea traduce sus órdenes a comandos especı́ficos del módulo. Esta arquitectura basada en comportamientos ha sido elegida porque es especialmente adecuada para el diseño y control de robots inspirados en sistemas biológicos, ha demostrado ser adecuada para sistemas modulares e integra muy bien niveles altos y bajos de control. Con el fin de comunicar a todos los actores (los comportamientos, los módulos y el control central), se ha desarrollado un protocolo de comunicación basado en I 2 C. Este protocolo permite enviar mensajes del operador al control central, desde el control central a los módulos y entre comportamientos. Dentro de la arquitectura también se ha desarrollado un “Lenguaje de Descripción de Modulos”(MDL por sus siglas en inglés “Module Description Language”), un lenguaje que permite a los módulos transmitir sus capacidades al control central, para que pueda procesar esta información y elegir la mejor configuración y los parámetros del microrobot. Dentro de la arquitectura de control se ha desarrollado un algoritmo genético con el fin de: primero, determinar los módulos a utilizar para tener una configuración óptima para una tarea especı́fica (petición de configuración), y segundo, determinar los parámetros óptimos para el mejor funcionamiento de un módulo dada una configuración (optimización de parámetros). Como resumen, las principales contribuciones que se pueden encontrar en esta tesis son: el diseño y la construcción de un microrobot modular heterogéneo multiconfigurable de tipo cadena capaz de llevar a cabo diferentes sistemas de locomoción (de tipo serpiente, gusano, helicoidal y combinación de los anteriores), el diseño de un interfaz común para los módulos, una arquitectura de control basada en comportamientos para robots modulares heterogéneos de tipo cadena, un simulador de la fı́sica y la dinámica (incluyendo el diseño de un modelo de servo), electrónica, comunicaciones y rutinas embebidas de software de los módulos y finalmente, la mejora del sistema de “ego-positioning”.. xii.

(13) Contents Abstract. ix. Resumen. xi. Contents. xiii. List of Figures. xvii. List of Tables. xxiii. Acknowledgements. xxvii. 1 Introduction 1.1 Motivation and framework of the thesis 1.2 Topics of the thesis . . . . . . . . . . . . 1.2.1 About Microrobotics . . . . . . . 1.2.2 About Modular Robots . . . . . 1.2.3 About Pipe Inspection Robots . 1.3 Objectives of the thesis . . . . . . . . . 1.4 Overview of the thesis . . . . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. 2 Review on Modular, Pipe Inspection and Micro Robotic 2.1 The origins . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Modular robots . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 PolyBot and PolyPod . . . . . . . . . . . . . . . . . 2.2.2 M-TRAN . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 CONRO . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.4 Molecube . . . . . . . . . . . . . . . . . . . . . . . . 2.2.5 Crystalline and Molecule robots . . . . . . . . . . . . 2.2.6 Telecube and Proteo (Digital Clay) . . . . . . . . . . 2.2.7 Chobie . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.8 ATRON . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.9 Active Cord Mechanism (ACM) . . . . . . . . . . . 2.2.10 WormBot . . . . . . . . . . . . . . . . . . . . . . . . 2.2.11 Others . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Microrobots . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Micro size modular machine using SMAs . . . . . . . 2.3.2 Denso Corporation . . . . . . . . . . . . . . . . . . .. xiii. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. . . . . . . .. Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . .. 1 1 2 2 3 3 4 6. . . . . . . . . . . . . . . . .. 9 10 13 13 15 18 19 22 24 27 29 31 32 34 35 36 36.

(14) . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. 38 38 41 41 42 42 42 43 44 44. 3 Review on Control Architectures for Modular 3.1 Classification of control architectures . . . . . . 3.2 Behaviour-Based Systems . . . . . . . . . . . . 3.2.1 What is a behavior? . . . . . . . . . . . 3.2.2 Behavior-based systems . . . . . . . . . 3.2.3 Behavior representation . . . . . . . . . 3.2.4 Behavioral encoding . . . . . . . . . . . 3.2.5 Emergent behavior . . . . . . . . . . . . 3.2.6 Behavior coordination . . . . . . . . . . 3.3 Behavior-Based Architectures . . . . . . . . . . 3.3.1 Subsumption Architecture . . . . . . . . 3.3.2 Motor Schemas . . . . . . . . . . . . . . 3.3.3 Activation Networks . . . . . . . . . . . 3.3.4 DAMN . . . . . . . . . . . . . . . . . . 3.3.5 CAMPOUT . . . . . . . . . . . . . . . . 3.4 Hybrid Deliberate-Reactive Architectures . . . 3.4.1 3-Tiered (3T) . . . . . . . . . . . . . . . 3.4.2 Aura . . . . . . . . . . . . . . . . . . . . 3.4.3 Atlantis . . . . . . . . . . . . . . . . . . 3.4.4 Saphira . . . . . . . . . . . . . . . . . . 3.4.5 DD&P . . . . . . . . . . . . . . . . . . . 3.5 Modular Robot Architectures . . . . . . . . . . 3.5.1 CONRO . . . . . . . . . . . . . . . . . . 3.5.2 M-TRAN . . . . . . . . . . . . . . . . . 3.5.3 Polybot . . . . . . . . . . . . . . . . . . 3.6 Adaptive Behavior . . . . . . . . . . . . . . . . 3.6.1 Reinforcement Learning . . . . . . . . . 3.6.2 Neural Networks . . . . . . . . . . . . . 3.6.3 Fuzzy Behavioral Control . . . . . . . . 3.6.4 Genetic Algorithms . . . . . . . . . . . . 3.7 Conclusions . . . . . . . . . . . . . . . . . . . .. Microrobots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 49 50 54 54 55 55 58 59 60 63 64 65 67 69 69 72 73 73 74 75 77 78 78 80 81 83 83 83 84 85 87. 4 Electromechanical design 4.1 Developed modules hardware description 4.1.1 Rotation Module . . . . . . . . . 4.1.2 Support and Extension modules 4.1.3 Helicoidal drive module . . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. 89 90 90 95 101. 2.4. 2.5 2.6. 2.3.3 Endoscope microrobots . . . . . . . 2.3.4 LMS, LAB and LAI microrobots . . 2.3.5 12-legged endoscopic capsular robot Pipe Inspection robots . . . . . . . . . . . . 2.4.1 MRInspect . . . . . . . . . . . . . . 2.4.2 FosterMiller . . . . . . . . . . . . . . 2.4.3 Helipipe . . . . . . . . . . . . . . . . 2.4.4 Theseus . . . . . . . . . . . . . . . . Robot Summary . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . .. xiv. . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . . . . . . . .. . . . .. . . . ..

(15) 4.1.4 Camera module . . . . . . . . . . . . . 4.1.5 Batteries module . . . . . . . . . . . . Other modules . . . . . . . . . . . . . . . . . 4.2.1 SMA-based module . . . . . . . . . . . 4.2.2 Traveler module . . . . . . . . . . . . 4.2.3 Sensor module . . . . . . . . . . . . . Embedded electronics description . . . . . . . 4.3.1 Common interface . . . . . . . . . . . 4.3.2 Actuator control . . . . . . . . . . . . 4.3.3 Sensor management . . . . . . . . . . 4.3.4 I 2 C communication . . . . . . . . . . 4.3.5 Synchronism lines communication . . 4.3.6 Auto protection and adaptable motion 4.3.7 Self orientation detection . . . . . . . Chained configurations . . . . . . . . . . . . . 4.4.1 Homogeneous configurations . . . . . . 4.4.2 Heterogeneous configurations . . . . . Conclusions . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . .. 104 105 106 106 106 107 108 108 108 109 109 109 110 111 113 113 121 122. 5 Simulation Environment 5.1 Physics and dynamics simulator . . . . . . . . 5.1.1 Open Dynamics Engine (ODE) . . . . 5.1.2 Servomotor model . . . . . . . . . . . 5.1.3 Modules physical model . . . . . . . . 5.1.4 Environment model . . . . . . . . . . 5.2 Electronic and control simulator . . . . . . . 5.2.1 Software description . . . . . . . . . . 5.2.2 Actuator control . . . . . . . . . . . . 5.2.3 Sensor management . . . . . . . . . . 5.2.4 I 2 C communication . . . . . . . . . . 5.2.5 Synchronism lines communication . . 5.2.6 Simulation of the power consumption 5.3 Class implementation . . . . . . . . . . . . . 5.3.1 I 2 C classes . . . . . . . . . . . . . . . 5.3.2 Servo class . . . . . . . . . . . . . . . 5.3.3 Module classes . . . . . . . . . . . . . 5.3.4 Central Control class . . . . . . . . . . 5.3.5 Robot class . . . . . . . . . . . . . . . 5.3.6 Graphical User Interface classes . . . . 5.4 Heterogenous modular robot . . . . . . . . . 5.5 Conclusions . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . .. 125 126 126 127 129 133 133 133 134 135 136 136 136 137 137 138 138 141 141 141 141 144. . . . . .. 147 . 147 . 148 . 150 . 150 . 152. 4.2. 4.3. 4.4. 4.5. 6 Positioning System for Mobile Robots: Ego-Positioning 6.1 Brief on Positioning Systems for Mobile Robots . . . . . . 6.1.1 IR light emission-detection . . . . . . . . . . . . . 6.1.2 Electrical fields . . . . . . . . . . . . . . . . . . . . 6.1.3 Wireless Ethernet . . . . . . . . . . . . . . . . . . 6.1.4 Ultrasound systems . . . . . . . . . . . . . . . . .. xv. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . ..

(16) 6.2 6.3. 6.4. 6.5. 6.6. 6.1.5 Electromagnetic . . . . . . . . . . . . . . . . . . . . . 6.1.6 Pressure sensors . . . . . . . . . . . . . . . . . . . . . 6.1.7 Visual systems . . . . . . . . . . . . . . . . . . . . . . Introduction to EGO-positioning . . . . . . . . . . . . . . . . Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Sensing devices . . . . . . . . . . . . . . . . . . . . . . 6.3.2 Beamer . . . . . . . . . . . . . . . . . . . . . . . . . . Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.4.1 EGO-positioning procedures: theory and performances 6.4.2 I-Swarm considerations . . . . . . . . . . . . . . . . . 6.4.3 Image Sequence Programming . . . . . . . . . . . . . 6.4.4 Alice software . . . . . . . . . . . . . . . . . . . . . . . 6.4.5 I-Swarm software . . . . . . . . . . . . . . . . . . . . . Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.5.1 Transmission of commands . . . . . . . . . . . . . . . 6.5.2 Programming robots . . . . . . . . . . . . . . . . . . . Results and conclusions . . . . . . . . . . . . . . . . . . . . .. 7 Control Architecture 7.1 Description . . . . . . . . . . . . . . . 7.2 Communication protocol . . . . . . . . 7.2.1 Layer structure . . . . . . . . . 7.2.2 Command messages structure . 7.2.3 Low level commands (LLC) . . 7.2.4 High level commands (HLC) . 7.3 Module Description Language (MDL) 7.4 Working modes . . . . . . . . . . . . . 7.5 Onboard control . . . . . . . . . . . . 7.5.1 Embedded Behaviors . . . . . . 7.5.2 Behavior fusion . . . . . . . . . 7.6 Heterogeneous layer . . . . . . . . . . 7.6.1 Communications . . . . . . . . 7.6.2 Configuration check . . . . . . 7.6.3 MDL phase . . . . . . . . . . . 7.7 Central control . . . . . . . . . . . . . 7.7.1 Rules . . . . . . . . . . . . . . 7.7.2 Inference Engine . . . . . . . . 7.7.3 Central control Behaviors . . . 7.7.4 Behavior fusion . . . . . . . . . 7.8 Offline Control . . . . . . . . . . . . . 7.8.1 Brief on genetic algorithms . . 7.8.2 Codification and set up . . . . 7.8.3 Phases of the GAs . . . . . . . 7.9 Conclusions . . . . . . . . . . . . . . .. xvi. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. 152 153 153 154 156 156 158 163 163 165 166 167 168 168 168 169 169. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . .. 173 174 176 176 176 178 180 182 183 184 185 194 195 196 196 197 197 198 199 200 204 205 206 209 211 216.

(17) 8 Test and Results 8.1 Real tests . . . . . . . . . . . . . 8.1.1 Camera/Contact Module 8.1.2 Helicoidal . . . . . . . . . 8.1.3 Worm-like . . . . . . . . . 8.1.4 Snake-like . . . . . . . . . 8.2 Validation tests . . . . . . . . . . 8.2.1 Servomotor tests . . . . . 8.2.2 Inchworm tests . . . . . . 8.2.3 Helicoidal module test . . 8.2.4 Snake-like gait tests . . . 8.3 Simulation tests . . . . . . . . . . 8.3.1 Locomotion tests . . . . . 8.3.2 Control tests . . . . . . . 9 Conclusions and Future Works 9.1 Conclusions . . . . . . . . . . . 9.2 Main contributions of the thesis 9.3 Publications and Merits . . . . 9.3.1 Publications . . . . . . . 9.3.2 Merits . . . . . . . . . . 9.4 Future Work . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. . . . . . . . . . . . . .. 219 219 220 220 220 223 223 223 231 232 232 236 236 242. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. 247 . 247 . 248 . 249 . 249 . 251 . 251. A Fabrication technologies A.1 Stereolithography . . . . . . . . . . . . . . . . . A.1.1 Part generation mechanics . . . . . . . . A.1.2 Images from real work process . . . . . A.1.3 Advantages, drawbacks and limitations A.2 Micro-milling . . . . . . . . . . . . . . . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. B Terms and Concepts C Equipment used C.1 Hardware . . . . . . . . . . . . C.2 Software . . . . . . . . . . . . . C.2.1 Modelling . . . . . . . . C.2.2 Simulation . . . . . . . C.2.3 Microchip programming C.2.4 Editing . . . . . . . . .. 253 253 253 254 255 257 261. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. . . . . . .. 267 267 269 269 269 270 270. Glossary. 273. Bibliography. 275. xvii.

(18) xviii.

(19) List of Figures 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37. Tetrobot: a parallel Stewart platform. . . . . . . . . . . . . . . . . . . . . . Real picture of CEBOT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Fracta robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Metamorphic robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Polypod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different configurations of PolyBot . . . . . . . . . . . . . . . . . . . . . . . Different versions of PolyBot main modules . . . . . . . . . . . . . . . . . . Overview of M-TRAN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . M-TRAN main module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different configurations of M-TRAN . . . . . . . . . . . . . . . . . . . . . . Main module of CONRO . . . . . . . . . . . . . . . . . . . . . . . . . . . . Different configurations of CONRO . . . . . . . . . . . . . . . . . . . . . . . Example of reconfiguration in Molecube . . . . . . . . . . . . . . . . . . . . Molecubes new design (2007) . . . . . . . . . . . . . . . . . . . . . . . . . . Crystalline robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Molecule robot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Telecube . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Digital Clay Modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Slide motion mechanism of Chobie II . . . . . . . . . . . . . . . . . . . . . . Chobie reconfiguration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ATRON . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Active Cord Mechanism (ACM): version III (a), R3 (b), R4 (c) and R5 (d) WormBot: CPG-driven Autonomous Robot . . . . . . . . . . . . . . . . . . Prototype from the University of Camberra . . . . . . . . . . . . . . . . . . Superbot modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MAAM and Vertical Modules . . . . . . . . . . . . . . . . . . . . . . . . . . I-Cubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic motion of Micro SMA . . . . . . . . . . . . . . . . . . . . . . . . . . . Estructure and real module of Micro SMA . . . . . . . . . . . . . . . . . . . Denso microrobot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Endoscope microrobots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LAI, LMS and LAB microrobots . . . . . . . . . . . . . . . . . . . . . . . . 12-legged endoscopic capsular robot . . . . . . . . . . . . . . . . . . . . . . MRInspect pipe inspection robot . . . . . . . . . . . . . . . . . . . . . . . . Foster Miller pipe inspection robot . . . . . . . . . . . . . . . . . . . . . . . Helipipe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Thes-I pipe inspection robot . . . . . . . . . . . . . . . . . . . . . . . . . . .. xix. 10 11 12 13 14 15 15 16 17 17 18 19 20 21 22 24 25 26 28 29 29 31 33 34 35 36 37 38 38 39 39 40 41 42 43 44 45.

(20) 2.38 Thes-III pipe inspection robot . . . . . . . . . . . . . . . . . . . . . . . . . . 45 3.1 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 3.10 3.11 3.12 3.13 3.14 3.15 3.16 3.17 3.18 3.19 3.20 3.21 3.22 3.23. AI models: a) Deliberative b) Reactive c) Hybrid d) Behavior-based NASREM architecture . . . . . . . . . . . . . . . . . . . . . . . . . . Example of stimulus response diagram . . . . . . . . . . . . . . . . . FSA encoding a door traversal mechanisms . . . . . . . . . . . . . . Potential fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Basic block in subsumption architecture . . . . . . . . . . . . . . . . Fuzzy command fusion example . . . . . . . . . . . . . . . . . . . . . Example of structure in subsumption architecture . . . . . . . . . . . Subsumption AFSM of a Three Layered Robot . . . . . . . . . . . . Structure of Motor Schemas . . . . . . . . . . . . . . . . . . . . . . . Activation Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . DAMN architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . CAMPOUT: block diagram . . . . . . . . . . . . . . . . . . . . . . . 3T intelligent controll architecture . . . . . . . . . . . . . . . . . . . Aura Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atlantis Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . Saphira system architecture . . . . . . . . . . . . . . . . . . . . . . . DD&P Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control Architecture of M-TRAN . . . . . . . . . . . . . . . . . . . . Polybot control scheme . . . . . . . . . . . . . . . . . . . . . . . . . Neural Networks Scheme . . . . . . . . . . . . . . . . . . . . . . . . . Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . GA scheme in M-TRAN . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . .. 50 52 56 57 59 61 63 64 65 66 68 69 71 74 75 76 77 78 81 82 84 85 86. 4.1 4.2 4.3 4.4 4.5 4.6 4.7 4.8 4.9 4.10 4.11 4.12 4.13 4.14 4.15 4.16 4.17 4.18 4.19 4.20 4.21 4.22. Detail of a wheel of the helicoidal module . . . . . . . . . . . Gearhead design . . . . . . . . . . . . . . . . . . . . . . . . . Rotation module V1 . . . . . . . . . . . . . . . . . . . . . . . Rotation module v2 plus camera . . . . . . . . . . . . . . . . Snake configuration plus camera . . . . . . . . . . . . . . . . Reference system for Denavit-Hartenberg . . . . . . . . . . . Worm-like microrobot V1 . . . . . . . . . . . . . . . . . . . . Support module 1.1 . . . . . . . . . . . . . . . . . . . . . . . Support module v2.0 . . . . . . . . . . . . . . . . . . . . . . . Inchworm configuration based on v2.1 modules plus camera . Extension module detailed mechanism . . . . . . . . . . . . . Coordinate system for the kinematics of the support module . Kinematics diagrams of the extension module . . . . . . . . . Helicoidal module v1 . . . . . . . . . . . . . . . . . . . . . . . Helicoidal module V2 plus camera . . . . . . . . . . . . . . . Camera module v1 . . . . . . . . . . . . . . . . . . . . . . . . Camera module v2 . . . . . . . . . . . . . . . . . . . . . . . . Batteries Module . . . . . . . . . . . . . . . . . . . . . . . . . SMA-based modules . . . . . . . . . . . . . . . . . . . . . . . Traveler Module . . . . . . . . . . . . . . . . . . . . . . . . . Common interface . . . . . . . . . . . . . . . . . . . . . . . . Camera electronic circuits . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. 90 91 92 92 93 94 97 97 98 98 99 100 101 102 103 104 104 105 107 107 108 109. xx. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . ..

(21) 4.23 4.24 4.25 4.26 4.27 4.28 4.29 4.30 4.31 4.32 4.33 4.34 4.35 4.36 4.37. Auto-protection control scheme . . . . . . . . . . . Auto-protection circuits . . . . . . . . . . . . . . . Consumption output . . . . . . . . . . . . . . . . . Accelerometer tests: still module . . . . . . . . . . Module moving along a linear trajectory in the XY Servo moving from 30 ◦ to 150 ◦ with no load . . . Servo moving from 150 ◦ to 30 ◦ loaded . . . . . . . Snake-like configuration . . . . . . . . . . . . . . . Snake movements . . . . . . . . . . . . . . . . . . . Snake-like configurations . . . . . . . . . . . . . . . Snake-like microrobot inside pipes . . . . . . . . . Graphical User Interface . . . . . . . . . . . . . . . Worm-like module: Sequence of movement . . . . . Helicoidal configuretion . . . . . . . . . . . . . . . Multi-modular configuration . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . plane . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. 110 110 112 114 115 115 116 116 117 118 119 120 121 121 122. 5.1 5.2 5.3 5.4 5.5 5.6 5.7 5.8 5.9. Simulation Environment . . . . . . . . . Mathematical model of the servomotor . Rotation Module and Helicoidal Module Inchworm Modules . . . . . . . . . . . . Touch Module and Traveler Module . . Accelerometer axis sketch . . . . . . . . Class diagram . . . . . . . . . . . . . . . Class interaction . . . . . . . . . . . . . Elbow Negotiation . . . . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. 126 127 130 131 133 135 137 139 143. 6.1 6.2 6.3 6.4 6.5 6.6. Experimental setup of iGPS . . . . . . . . . . . . . . . . . . . . . . . . . . Behavior of the system for irregular floors . . . . . . . . . . . . . . . . . . NorthStar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Indoor positioning network . . . . . . . . . . . . . . . . . . . . . . . . . . Illustration of time difference of arrival (TDOA) localization . . . . . . . . Example of wireless ethernet distribution of five base stations (enumerated small circles) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . MotionStar system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Smart Floor plate (left) and load cell (right) . . . . . . . . . . . . . . . . . Ego-positioning system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Position and orientation calculation (a) and ”Alice” robot (b) . . . . . . . Ego-positioning extension to chained modular robots . . . . . . . . . . . . BPW34 main features (a) and photodiodes board (b) . . . . . . . . . . . . Optimal RC Filter (a) and Spectral sensitivity of aSi:H (b) . . . . . . . . Current comparator for I-SWARM . . . . . . . . . . . . . . . . . . . . . . Color wheel of the DLP beamer . . . . . . . . . . . . . . . . . . . . . . . . Response of the beamer to a white image . . . . . . . . . . . . . . . . . . Response of the beamer (without color wheel) to a white image . . . . . . Response of the photodiode to a red image (a) and a yellow image (b) . . Response of the photodiode to a projection of sequences of black and white images at 60 Hz (a) and 85 Hz (b) . . . . . . . . . . . . . . . . . . . . . . Response of the photodiode to a grey image . . . . . . . . . . . . . . . . .. . . . . .. 148 149 149 150 151. . . . . . . . . . . . . .. 151 153 154 154 155 156 157 158 158 159 159 160 160. 6.7 6.8 6.9 6.10 6.11 6.12 6.13 6.14 6.15 6.16 6.17 6.18 6.19 6.20. xxi. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . . . . . . . . .. . 161 . 161.

(22) 6.21 Response of the photodiode to a projection of sequences of 3 (a) and 4 (b) different grey scale images at 60 Hz . . . . . . . . . . . . . . . . . . . . . . . 6.22 Distribution of intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.23 Output voltage for a black and white sequence at the point of higher (a) and lower (b) illumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.24 Binary (a) and Gray (b) code . . . . . . . . . . . . . . . . . . . . . . . . . . 6.25 Sampling time to get the RGB values of the projected image . . . . . . . . 6.26 Interruption Service Routine ”Photodiodes” (a) and function ”SequenceTest” (b) pseudocode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.27 Sampling procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.28 Function ”EGO Position” (a) and Main program (b) pseudocode . . . . . . 6.29 Gray to Binary conversion scheme . . . . . . . . . . . . . . . . . . . . . . . 6.30 Success - error rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 162 162 163 164 165 168 169 170 171 172. 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13 7.14 7.15 7.16 7.17. Control Scheme . . . . . . . . . . . . . . . . . . . . . . Control Layers . . . . . . . . . . . . . . . . . . . . . . Behavior sketch . . . . . . . . . . . . . . . . . . . . . . HLC and LLC commands . . . . . . . . . . . . . . . . Communication Layers . . . . . . . . . . . . . . . . . . I 2 C frames . . . . . . . . . . . . . . . . . . . . . . . . Behavior scheme . . . . . . . . . . . . . . . . . . . . . Heat dissipation sketch . . . . . . . . . . . . . . . . . . Maximun servomotor consumption with blocking . . . Extension module at its higher and lower position . . Behavior fusion scheme . . . . . . . . . . . . . . . . . Configuration check sequence diagram . . . . . . . . . Ext / Contraction capabilites: a) grade 3 and b) grade Behavior fusion scheme for Central Control behaviors Roulette probabilty . . . . . . . . . . . . . . . . . . . . Single point crossover example . . . . . . . . . . . . . Mutation example . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . 1 . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . .. 174 175 175 176 177 178 185 187 189 190 195 196 198 205 213 214 215. 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 8.10 8.11 8.12 8.13 8.14 8.15 8.16. Images taken form the camera inside a pipe Camera Interface . . . . . . . . . . . . . . . Helicoidal module inside a pipe . . . . . . . Worm module tests . . . . . . . . . . . . . . Snake-like movement over undulated terrain Corner negotiation . . . . . . . . . . . . . . 30◦ to 120◦ unloaded: rotation angle . . . . . 30◦ to 120◦ unloaded: intensity . . . . . . . . 30◦ to 120◦ unloaded: torque . . . . . . . . . 30◦ to 120◦ loaded: rotation angle . . . . . . 30◦ to 120◦ loaded: intensity . . . . . . . . . 30◦ to 120◦ loaded: tau . . . . . . . . . . . . 90◦ to 30◦ unloaded: rotation angle . . . . . 90◦ to 30◦ unloaded: intensity . . . . . . . . . 90◦ to 30◦ unloaded: tau . . . . . . . . . . . 90◦ to 30◦ unloaded: rotation angle . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. 220 221 221 222 223 224 225 225 226 226 227 227 228 228 229 229. xxii. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . ..

(23) 8.17 8.18 8.19 8.20 8.21 8.22 8.23 8.24 8.25 8.26 8.27 8.28 8.29 8.30 8.31 8.32 8.33 8.34 8.35. 90◦ to 30◦ unloaded: intensity . . . . . . . . . . . . . . . . . . . . . . . . . . 90◦ to 30◦ unloaded: tau . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rotation module v1 torque test . . . . . . . . . . . . . . . . . . . . . . . . 1D sinusoidal movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . Turning movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rolling movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rotating movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Lateral shifting movement . . . . . . . . . . . . . . . . . . . . . . . . . . . R+H elbow negotiation . . . . . . . . . . . . . . . . . . . . . . . . . . . . R+H elbow negotiation depending on pipe diameter . . . . . . . . . . . . Rotation + passive modules in a vertical sinusoidal movement . . . . . . . Rotation + passive modules negotiating an elbow with and without helicoidal module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Inchworm locomotion composed of several extension and support modules Example of heterogenous configuration . . . . . . . . . . . . . . . . . . . . Configuration check example . . . . . . . . . . . . . . . . . . . . . . . . . Example of orientation behavior . . . . . . . . . . . . . . . . . . . . . . . Contact, Rotation, Helicoidal and Passive . . . . . . . . . . . . . . . . . . Contact and rotation modules . . . . . . . . . . . . . . . . . . . . . . . . . Example of chain splitting . . . . . . . . . . . . . . . . . . . . . . . . . . .. A.1 Stereolithography process . . . . . . . . . . . . . . A.2 Support columns removal . . . . . . . . . . . . . . A.3 Laser trajectory . . . . . . . . . . . . . . . . . . . . A.4 Solidification process . . . . . . . . . . . . . . . . . A.5 Post-cure oven . . . . . . . . . . . . . . . . . . . . A.6 Detail of some parts of the rotation module v1 . . A.7 Micro-milling system . . . . . . . . . . . . . . . . . A.8 Fixation System . . . . . . . . . . . . . . . . . . . A.9 Contouring machining . . . . . . . . . . . . . . . . A.10 Helicoidal module leg generated by micromachining. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . . .. 230 230 231 233 233 234 235 235 237 238 239. . . . . . . . .. 240 241 242 243 244 244 245 246. . . . . . . . . . .. 254 254 255 256 256 257 258 258 259 260. C.1 U2C-12 card . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268 C.2 Communication box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268. xxiii.

(24) xxiv.

(25) List of Tables 1.1. Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 2.1 2.2 2.3. 3-D Robots summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 2-D Robots summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 1-D Robots summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46. 3.1 3.2 3.3 3.4 3.5. Subsumption Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . Motor Schemas Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . Activation Networks Architecture . . . . . . . . . . . . . . . . . . . . . . . DAMN Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Control Architecture for Multi-robot Planetary Outposts (CAMPOUT) Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 72. 4.1 4.2 4.3 4.4 4.5. Modules main characteristics . . . . Denavit-Hartenberg parameters . . . Velocity in a 30cm ø pipe at different Velocity in a 30cm ø pipe at different Power Consumption . . . . . . . . .. . . . . .. 6.1 6.2 6.3. Setup description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156 Color coding table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Programming time and speed . . . . . . . . . . . . . . . . . . . . . . . . . . 170. 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7.10 7.11 7.12 7.13 7.14 7.15. LLC1 commands: sending . . . . . . . . . . . . . . . LLC1 commands: answering . . . . . . . . . . . . . . LLC2 commands: sending . . . . . . . . . . . . . . . LLC2 commands: answering . . . . . . . . . . . . . . HLC commands: sending . . . . . . . . . . . . . . . HLC commands: answering . . . . . . . . . . . . . . Behavior encoding: Avoid overheating . . . . . . . . Behavior encoding: Avoid actuator damage . . . . . Behavior encoding: Avoid mechanical damages . . . Behavior encoding: Self diagnostic . . . . . . . . . . Behavior encoding: Situation awareness . . . . . . . Behavior encoding: Environment diagnostic . . . . . Behavior encoding: Vertical sinusoidal movement . . Behavior encoding: Horizontal sinusoidal movement Behavior encoding: Worm-like movement . . . . . .. xxv. . . . . . . . . angles angles . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . (helicoidal module) . . (2nd helicoidal module) . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . .. . . . . .. . . . . . . . . . . . . . . .. . . . .. . . . . . . . . . . . . . . .. 4. 65 67 69 70. 90 93 103 103 111. 179 179 180 180 181 181 188 188 190 191 191 192 193 193 194.

(26) 7.16 7.17 7.18 7.19 7.20 7.21 7.22 7.23 7.24 7.25. Behavior encoding: Push-Forward movement . . Table of Rules . . . . . . . . . . . . . . . . . . . . Behavior encoding: Balance / Stability . . . . . . Behavior encoding: Straight forward / backwards Behavior encoding: Edge Following . . . . . . . . Behavior encoding: Pipe Following . . . . . . . . Behavior encoding: Obstacle negotiation . . . . . GA Configuration demand genes value range . . GA Configuration demand parameters . . . . . . GA Parameter optimization genes value range . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. . . . . . . . . . .. 194 199 201 202 202 203 203 209 210 211. 8.1 8.2 8.3 8.4. Speed and slope for different configurations Parameters for the servomotor tests . . . . Speed test of the inchworm configuration . Speed test of helicoidal module . . . . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. . . . .. 220 224 231 232. xxvi. . . . .. . . . .. . . . ..

(27) Acknowledgements. xxvii.

(28) xxviii.

(29) Chapter 1. Introduction “When I read a book I seem to read it with my eyes only, but now and then I come across a passage, perhaps only a phrase, which has a meaning for me, and it becomes part of me” W. Somerset Maugham. 1.1. Motivation and framework of the thesis. The idea that has given place to this thesis is the lack of multiconfigurable heterogenous microrobotic systems to inspect the inner part of narrow pipes. There are many robots for pipe inspection, but they are too big. There are a lot of modular systems (both lattice and chain) but they are homogenous and also too wider and box-shaped what makes them not suitable for pipes. And there are microrobots for colonoscopy but they are too slow for pipe inspection. In summary, the idea of the thesis is to put together all these advantages of modules, micro and pipe inspection robots into a “Intelligent Heterogeneous Multi-configurable Chained Microrobotic Modular System” After a rigorous study of the state of the art, it was decided that this thesis should lie amongst three fields: micro-robotics, modular robots and pipe-inspection robots. There are many robots and studies in each of these fields, but there are none that combines all of them. This thesis tries to create a model to develop microrobots capable to move in narrow pipes to explore them. The purpose is to do it by using modular robotic principles. Once the basics of the research were clear, a control scheme had to be built upon the mechanical system. And the selected approach was behavior-based control, for many reasons that will be described in chapter 7. After some time of research, it was necessary to increase the dimensions of the prototypes in order to facilitate the fabrication of the prototypes, so the target pipe diameters moved to 40mm diameter. This made possible to build more robust prototypes and to add. 1.

(30) CHAPTER 1. Introduction some other functionalities. This is the reason why in this thesis it is talked about microrobots: although the measures of the prototypes are a little bit bigger for a microrobot, the concept was created to be applicable to a microrobot. Although the first idea was to develop the microrobot for pipe inspection, the experience acquired with the first prototypes causes to realize that locomotion systems used inside pipes were also suitable outside them, and that the prototypes and the control architecture were useful in open spaces. That is why research was extended to open spaces and the ego-positioning system was added. This thesis has been developed along three projects: MICROROB (TAMAI), MICROMULT (MICROTEC) and I-SWARM. The purpose of the MICROTUB project is the design and construction of a microrobot able to move in pipes and tubes (straight or not) of about 26mm diameter. The development of this micro-robot will guide to the automation of inspection and maintenance of pipes and tubes at a lower cost in for example sewer systems, gas pipelines, water, gas and heating pipes in buildings, etc. MICROMULT stands for Multi-configurable Micro-robotic Systems. It is the subproject 1 in the project MICROTEC (Integration of Micromanufacturing, Microassembly and Microrobotics technologies) The main goals of MICROMULT are: • design and construction of a multi-configurable heterogeneous modular micro-robotic system able to move in narrow environments. • design and construction of a micro-assembly robotic station to develop micro-assembly, micro-gripping and micro-machining techniques. The I- SWARM project intends to lead the way towards the development of an artificial ant and thus make a significant step forward in robotics research by bringing together expertise in micro-robotics, in distributed and adaptive systems as well as in self-organising biological swarm systems. Building on the expertise of two EC-funded projects, MINIMAN and MiCRoN, this project will produce technological advances to facilitate the massproduction of micro-robots, which can then be employed as a “real” swarm consisting of up to 1000 robot clients. These clients will all be equipped with limited, on-board intelligence. Such a robot swarm can perform a variety of applications, including micro assembly, biological, medical or cleaning tasks.. 1.2. Topics of the thesis. 1.2.1. About Microrobotics. Microrobotics (or microbotics) is the field of miniature robotics, in particular mobile robots with characteristic dimensions less than 1 mm. The term can also be used for robots capable of handling micrometer size components, which is the case of the robots developped in this thesis, in which some components are smaller than 1 mm. Generally speaking, the term microrobot is used to described very small robots.. 2.

(31) 1.2. Topics of the thesis The earliest research and conceptual design of such small robots was conducted in the early 1970s in (then) classified research for U.S. intelligence agencies. Applications envisioned at that time included prisoner of war rescue assistance and electronic intercept missions. The underlying miniaturization support technologies were not fully developed at that time, so that progress in prototype development was not immediately forthcoming from this early set of calculations and concept design. The concept of building very small robots, and benefiting from recent advances in Micro Electro Mechanical Systems (MEMS) was publicly introduced in the seminal paper by Anita M. Flynn, “Gnat Robots (and How They Will Change Robotics)” [Flynn, 1987]. Microbots were born thanks to the appearance of the microcontroller in the last decade of the 20th century, and the appearance of miniature mechanical systems on silicon (MEMS), although many microbots do not use silicon for mechanical components other than sensors. One of the major challenges in developing a microrobot is to achieve motion using a very limited power supply. In this thesis microrobots need a power supply cable to work.. 1.2.2. About Modular Robots. Modular Robotics is an approach to building robots for various complex tasks. Instead of designing a new and different mechanical robot for each task, many copies of one simple module are built. The module can’t do much by itself, but when many of them are connected together, the result is a system that can do complicated things. In fact, a modular robot can even reconfigure itself – change its shape by moving its modules around – to meet the demands of different tasks or different working environments. What are the limitations on the number of modules for a useful modular robotic system? How does the number of modules affect: • Versatility (different shapes) • Robustness (self-repair and redundancy) • Cost (economies of scale?) These are very important questions that should be answered by each project. Scientific papers point out the importance of the modular design as a complementary direction of the integral design. The main benefits of this design method are: minimizing the time of design, increasing the number of configurations, an easy maintenance, a fall in prices... etc. Modularity refers to the user possibility to reconfigure the robot, both in hardware and software aspects, by combining several hard modules as well as redefining the architecture of the control program by using some programs modules.. 1.2.3. About Pipe Inspection Robots. Pipelines increasingly need to be inspected, maintained, and/or repaired in a wide range of industries, such as in petroleum, chemical, nuclear, space/aeronautic, and waste fields.. 3.

(32) CHAPTER 1. Introduction. Robot Configuration Homogeneity Environment Task. Tests. Basic. General. Configuration Demand. Surveillance. Known Homogeneous Known Known. Known Homogeneous Unknown Known. Known Heterogeneous Unknown Known. Unknown Heterogeneous Known Known. Known Heterogeneous Unknown Unknown. Table 1.1: Use Cases Pipe inspection is important not only for optimizing flow efficiency, but it also is critical to prevent failure. The effects of time, corrosion, and damage make pipeline failure an increasing concern with some pipelines being in use for 30 to 40 years. In-pipe inspection robots are needed with a smaller size, longer range, and increased maneuverability. Pipes in heating, water and gas systems, placed in homes, buildings or installations (like swimming pools,tanks...etc), are not usually accessible because they are either hidden or cannot be dismantled for inspection. In addition, some of these pipes are quite narrow, and most of the commercial robots can not get into them. As an example, the inspection of gas transmission mains requires the innovative marriage of a highly adaptable/flexible robotic platform with advanced sensor technologies operating as an autonomous inspection system in a live natural gas environment. Working with New York GAS and the Department of Energy, Foster-Miller has developed and is using a unique robotic system called Pipe Mouse to meet the demanding requirements of gas pipe inspection.. 1.3. Objectives of the thesis. The main objective of this thesis is the design of a multiconfigurable modular heterogeneous microrobot that gathers the advantages of the microrobots, modular robots and pipe inspection robots. This includes the design and fabrication of modules, the design of the control architecture and the development of a simulator. The main objectives are explained in the following sections.. Electromechanical design and construction of an heterogeneous multi-configurable chained microrobot In order to develop an heterogenous modular robot, several heterogenous modules have to be built: Rotation (2 dof ), support, extension, helicoidal, camera plus contact detection and batteries. Modules can be arranged in two different configurations: Homogenous (Worm-like, Snake-like, Helicoidal drive) and Heterogeneous (a composition of all of them) The use cases that the microrobot has been conceived for are shown in table 1.1. Tests The robot will be able to move through tubes between 30 to 50mm diameter, consisting on the following parts:. 4.

(33) 1.3. Objectives of the thesis • horizontal straight sections • vertical straight sections • bends up to 90 degrees both horizontally and vertically • bifurcations up to 90 degrees both horizontally and vertically • moving from a section to another of different diameter. For each of these parts the best configuration and the best sequence of moves will be explored. The robot will also be able to move through the soil (crawl), but only in settings that allow it. It will be determined experimentally the configurations that are capable of doing it, for example the type snake. Preconditions: the robot must be configured. Normal course: the robot will be able to travel the corresponding segment. Basic The robot will be able to move through tubes between 30 to 50mm diameter that are composed of unknown segments. Preconditions: the robot must be configured. Normal course: the robot will be able to travel the corresponding segment. General The operator puts the robot at the entrance of the pipe and will give the order to proceed until further notice. The system verifies the configuration (through the synchronous line) and optimize the sequence of movements to be carried out. Preconditions: The different modules will already be assembled and ready. Normal course: the robot will move forward, adapting to the shape of the pipe and overcoming any unforeseen obstacles. Configuration Demand The operator will specify the path that has to be traveled or the mission that has to be undertaken and the system will output the appropriate modules and their position in the chain. Postconditions: the robot will be prepared for a mission. Surveillance Utopian goal. The robot will move to an unfamiliar environment to monitor the environment and managing the repair and / or surveillance tasks for which it has been designed. Postconditions: the robot will return to the base station for recharging batteries and / or downloading of audio-visual material (photos, video, etc).. Development of a control architecture for heterogeneous modular chain-type microrobots Regarding the control scheme, the microrobot will be a semi-distributed autonomous robot. The control scheme will be divided on three layers:. 5.

(34) CHAPTER 1. Introduction • Low level: embedded in each module. It will control the movements of the module and the response to external unexpected extimuli. Easy to implement in small modules with limited microcontrollers. • Heterogenous layer: it is the interpreter from the high level control to the low level control of each module. • High level: central control, planning. Thinks of the microrobot as a whole, not each module individually. The control architecture will be enhance with an offline genetic algorithm aimed at improving the configuration of the microrobotic modular chain and to optimize its locomotion parameters.. Development of a simulator for the previous microrobotic systems Due to the limitations in the fabrication process and its high cost, a simulation environment will be created with several purposes: to develop the control architecture without damaging the modules and to developed new prototypes and test them before fabricating them. The physical simulator will include an electronic simulator that emulates the microcontroller program that is running on the modules, including physical signals (synchronization signal), I2C communications, etc. To maintain the independence of each module, each control programs will run in a different thread. This design facilitates the transfer of the code from the simulator to real modules.. Development of systems for position measurement and traveled distance measurement A system will be developed and integrated in the robot that allows to know the position in open spaces and the traveled distance inside pipes.. 1.4. Overview of the thesis. Chapter 2 and Chapter 3 will give an overview of the state of the art in “Modular, Pipe Inspection and Micro Robotic Systems” and “ Control Architectures for Modular Microrobots” Chapter 4 will present the modules developed and its different versions, how they have evolved and the problems that have appeared during its construction. The simulation environment that has been created will be described in Chapter 5. It will explain the physical dynamic engine, the control and electronic simulation and the programming structure.. 6.

(35) 1.4. Overview of the thesis Chapter 6 will be dedicated to a positioning system that allows the robot to know its position in open space, based on the emission of coded images and its reception via photodiodes. The control architecture will be explained in Chapter 7: the behavior-based architecture, the communication system and the Module Description Language (MDL), the layers with the high and low level controls and the offline genetic algorithm for optimization. Chapter 8 will show the test that have been performed and its results, with real modules and in the simulator. Finally, Chapter 9 will show the conclusions, some remarks about the main contribution of the thesis and related publications and the future work.. 7.

(36) CHAPTER 1. Introduction. 8.

(37) Chapter 2. Review on Modular, Pipe Inspection and Micro Robotic Systems ”Everything should be made as simple as possible, but not one bit simpler” Albert Einstein. The key word in modular robot is “module”. But what is a “module”? In this thesis it will be used the following definition1 : “A module is a piece or a set of pieces that are repeated in a construction of any kind, to make it easier, regular and economic”. Thus, a robotic module would be: “A module that performs totally or partially typical tasks of a robot, and that has the possibility to interact with other modules”. Finally, a modular robot is a “robot composed of modules, i.e., a robot composed of parts that have independent functionalities but that are able to interact with each other in one or another way, giving as a result an entity with new capabilities”. What are the advantages of using modular robots? Some of the main advantages are: • Provide the system with configurability: multiconfigurability, reconfigurability and autoconfigurability • Increase fault tolerance: a module can fail without compromising the whole system • Make system scalable: new modules can be added without reconfiguration of the whole system. • Reduce the cost of large production because only one or few modules have to be massively produced and there is no assembly needed between parts. 1. From the “Real Academia Española (RAE)”. 9.

(38) CHAPTER 2. Review on Modular, Pipe Inspection and Micro Robotic Systems. Figure 2.1: Tetrobot: a parallel Stewart platform.. It is possible to classify modular robots according to its configurability capabilities in: reconfigurable (multiconfigurable), autoconfigurable, metamorphic, self-replicant. Multiconfigurability or reconfigurability refers to the property of a system that can be configured in different ways, no matter how. Autoconfigurable robots are able to change its configuration by its own means, while in multiconfigurable robots the reconfiguration has to be done externally (i.e. by the operator). Metamorphic robots are called those that are composed of one repeated module that are able to change its shape. Most of reconfigurable robots are also metamorphic. Selfreplicating robots are able to make a copy of itself (providing they have the necessary modules) by its own means. The state of the art for the type of robot described in the first part of this thesis include several fields: modular robots (lattice and chain) regarding the design and concept, microrobots regarding its size and pipe inspections robots regarding its purpose. In the next sections the state of the art in these fields will be shown, with especial emphasis in the features related to this thesis.. 2.1. The origins. In this section some of the first prototypes that have inspired the development of modular robots are mentioned as a reference to understand the evolution of this kind of robots.. 10.

(39) 2.1. The origins. Figure 2.2: Real picture of CEBOT. TETROBOT [Hamlin and Sanderson, 1996], from the Rensselaer Polytechnic Institute, is a modular system for the design, implementation and control of a class of highly redundant parallel robotic mechanisms developed in 1996 (figure 2.1). It is an actuated robotic structure which may be reassembled into many different configurations while still being controlled by the same hardware and software architecture. Some implementations that can be obtained are a double octahedral platform, a tetrahedral arm and a six–legged walker. Main researchers: G.J. Hamlin and A.C. Sanderson Web: http://www.rpi.edu/dept/cie/faculty_sanderson.html CEBOT (Cellular Robotic System) [Fukuda and Kawauchi, 1990], from Nagoya University, is a dynamically configurable robot that has the capability of self-organizing, self-evolution and functional amplification (ability of a system to coordinate together to accomplish tasks that cannot be performed by the individual units themselves). The CEBOT (figure 2.2) consists of many robotic units with a simple function, named cell. The CEBOT can reconfigure the whole system depending on given tasks and environments and organize collective or swarm intelligence. The concept of the CEBOT is based on biological organization constructed by enormous natural cells. This research project includes mutual communication between cells, the optimum dynamic knowledge allocation among cells, the reconfiguration strategy of the system and the artificial-life such as the cooperative behavior modeling of ants. This invokes many interesting research problems, such as dynamic decentralized planning, dynamic distribution and coordinated control system as well as hardware systems. Experiments in automated re-configuration were carried out, but the robot did not self-reconfigure because a manipulator arm was required for this. Main researcher: T. Fukuda. Web: http://www.mein.nagoya-u.ac.jp/staff/fukuda-e.html Fracta was created at the Murata Laboratory. The Murata Lab has been one of the first in researching modular reconfigurable robots. There, it has been developed from 1998,. 11.

(40) CHAPTER 2. Review on Modular, Pipe Inspection and Micro Robotic Systems. (a) 2D. (b) 3D Universal Structure. Figure 2.3: Fracta robot. the 2D and 3D versions of Fracta [Murata et al., 1998] (fig. 2.3). In the 3D design, it has three symmetric axes with twelve degrees of freedom. A unit is composed of a 265mm cube weighing 7kg with connecting arms attached to each face. Selfreconfiguration is performed by means of rotating the arms and an automatic connection mechanism. Each unit has an on-board microprocessor and communication system. The drawback of this approach is that each module is quite big and heavy. The connection mechanism uses six sensors and encoders, further increasing system complexity. However, this is one of the few systems that can achieve 3D selfreconfiguration. This system perfectly illustrates the problems with a homogeneous design: the modules become big and cumbersome. The 2D design [Tomita et al., 1999] has six arms, three electromagnet male arms and three permanent magnet female arms. Based on simple magnetics, connection occurs when a neighbor (male) has a same polarity of permanent magnet (female). On the other hand, reversing the polarity of the electromagnets causes disconnection. A unit has three ball wheels under a body, its own processor and optical communication. Main researcher: S. Murata Web: http://www.mrt.dis.titech.ac.jp/english.htm The Metamorphic robot [Chirikjian, 1994] was created at the Robot and Protein Kinematics Lab, Johns Hopkins University. The Metamorphic robot (figure 2.4) is a collection of mechatronic modules, each of which has the ability to connect, disconnect, and climb over adjacent modules developed in 1994. It is used to examine the near-optimal reconfiguration of a metamorphic robot from an arbitrary initial configuration to a desired final configuration. Concepts of distance between metamorphic robot configurations are defined, and shown to satisfy the formal properties of a metric. These metrics, called configuration metrics, are then applied to the automatic self-reconfiguration of metamorphic systems in the case when one module is allowed to move at a time. There is no simple method for computing the optimal sequence of moves required to reconfigure. As a result, heuristics which can give a near. 12.