DEL RECURSO DE INCONFORMIDAD

1.1.3 Safety requirements for collaborative robots . . . 8 1.1.3.1 Safety standards and related studies . . . 8 1.1.3.2 Injury risk reduction measures. . . 14 1.2 Motivations . . . 19 1.2.1 Problem statement . . . 19 1.2.2 Contributions of the thesis. . . 21 1.3 Outline . . . 23

In an industrial context or for service applications involving human-robot collaboration, this thesis proposes new insights on the detection and characterization of contact situations between a robot and its environment without the use of extra sensors. The collided obstacles are of an unknown geometry and location in the environment and may occur at any point along the robot arm. The framework and challenges of this work are presented in this first chapter.

1.1

Introduction

1.1.1 Towards industry 4.0

Over the last centuries, technological evolution has made it possible for human beings to rev- olutionize their industry, reinventing it through the exploitation of new energy resources but also adapting it to their evolving needs and constraints. In particular, since the 18th century, mechanization and mass production have profoundly transformed the industry in its quest for greater productivity and competitiveness.

While the idea of automated beings was already generating fear and fascination in literature [Shelley 1818] and theater [Capek 1920], the first industrial robot to be deployed on an assembly line appeared in the 60’s at the dawn of the third industrial revolution (seeFigure 1.1). Defined as an automated and programmable system capable of moving in three or more directions [ISO 8373: 2012], industrial robots were conceived to perform repetitive tasks at high speed and with good accuracy. These tasks are precisely the ones that are likely to be the least attractive for human operators since they do not require any added value or particular skills. With a new division of labor, robotic automation has opened up new opportunities in industry (e. g. in the space, medical and military sectors), but also in service robotics.

Although the trend is towards ever-increasing automation of manufacturing processes, there are however other operations for which human dexterity and decision-making skills remain until now irreplaceable by a traditional industrial robot. Motivated by technological and economical reasons, in this case the human operator is kept in the loop as an experienced active agent but is assisted by a robotic system. This form of synergy takes advantage on the one hand of the robot’s precision, repeatability and strength and on the other hand of the human expertise, perception and adaptation skills.

1.1. Introduction 3

Figure 1.2: Production costs according to the volume and the type of automation of the work- station [Hägele 2002].

This working partnership is particularly suitable for tasks carried out on very variable prod- ucts (e. g. agri-food industry), applications that require frequent interventions or reconfigura- tions (e. g. multiple product variants) or for any meticulous operation (e. g. surgical procedure). Small and medium-sized production plants have especially benefited from this first step towards automated processes since their volume of production does not usually justify the high costs of research and integration of a specialized robot (see Figure 1.2). The trend towards greater product customization and shorter product life cycles also explain this search for flexibility in manufacturing processes. As a physical assistance system, collaborative robots have also found a particular interest in reducing work-related musculoskeletal disorders due for instance to repetitive gestures, excessive effort or non-ergonomic posture maintained over time (see Fig- ure 1.3). These disorders can lead to significant compensation costs for companies (in 2017, work-related musculoskeletal disorders represented more than 87% of the occupational diseases in France [Assurance Maladie 2017]).

In the attractive concept of collaborative robotics, cobots (for collaborative robots) are generally recognized for their versatility and intuitive programming as they benefit from the latest ad- vances in robotics, just as traditional industrial robots could be if they were designed in this way. What really distinguishes a collaborative robot from a traditional robot is the collaborative

application. The latter defines the compatibility between a human and a robot to collaborate

under safe conditions in an unstructured environment, that is not totally known and potentially variable. This new framework is made possible by recent advances in the fields of mechanics, electronics, materials and computer science, associated with progress in computational capa- bilities. They have been beneficial to robotics by increasing robot capabilities in perception (advanced instrumentation and sensors), information processing (improved microprocessors and algorithms) and actuation (enhanced actuators and materials). With a broader awareness of its environment, the robot’s actions can be appropriately derived from what it perceives through external stimuli and no longer from a pre-established implemented program.

(a) In public works, painful work- ing posture and discomfort caused by noise and vibrations of tools.

(b) In the food industry, handling of cutting tools with high hygiene and temperature constraints.

(c) In the car industry, working with arms raised on elevated pro- duction lines at a sustained pace. Figure 1.3: Harsh working conditions with high risk of developing work-related musculoskeletal disorders.

This pursuit of flexibility and efficiency in manufacturing processes is part of the strategy of what some call the industry 4.0. Within the smart factories, all means of production have to be connected together, including robot and human operator (seeFigure 1.4). They form a network of cyber-physical systems1 _{that use information and communication technologies to allow their}

interaction in real time. In the case of robotics, many interconnections are being explored, for example with virtual and augmented reality, big data or artificial intelligence, driven by fantasies but at the same time framed by ethical principles.

Figure 1.4: Description of the key components of the smart factory [Fostec & Company 2019]: the industry of the future will benefit from advances in each of these areas, which, when inter- connected, offer greater flexibility and efficiency in the manufacturing processes.

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The term cyber-physical systems refers to engineered systems that are built from, and depend upon, the seamless integration of computation and physical components [National Science Foundation 2019].

1.1. Introduction 5