Tercera marcha gama baja y rango bajo con jacobs

The sensor-based navigation technique for a mobile robot using fuzzy controller has been discussed by Ishikawa [69]. In this technique, he used two functions, one function is used for tracing a planned path and the other is used to avoid stationary and moving obstacles. The effectiveness of the developed method has been discussed with simulation results. Li and Feng [70] have presented a fuzzy logic technique for robot path planning in uncertain environments. They designed a fuzzy controller whose inputs comprise of a heading angle and the separation distances between the obstacles. The outputs are the real wheel velocities of the robot. Fuzzy logic techniques for mobile robot obstacle avoidance have been discussed by Reignier [71]. In this article, he has fuzzy logic controller to solve

various reactive behaviours for the mobile robot. Wu [72] has discussed the navigation of a mobile robot in the 2-dimensional unknown environment using a sensor based fuzzy algorithm. He used an optimal learning algorithm in the fuzzy system to minimize total covering distance of the robot. Beom and Cho [73] have introduced a sensor-based path planning strategy for a mobile robot in unknown environments using fuzzy logic and reinforcement learning. The viability of the developed algorithm has been confirmed by the simulation results. Intelligent collision avoidance by the automated guided vehicle (AGV) using a fuzzy logic approach has been described by Lin and Wang [74]. In this paper, they have mainly emphasized on the sensor modeling and trap recovering situations. Maaref and Barret [75] have presented a new sensor-based fuzzy navigation method of a mobile robot in an indoor environment. The proposed navigation strategy consolidates two sorts of obstacle avoidance behaivour, one for the concave types and other is for the convex. A new navigation strategy for mobile robots in challenging environments, using a fuzzy logic method has been discussed by Seraji and Howard [76]. They introduced a new traverse-terrain behavior that uses the regional traversability index to control the robot to the safest and the most traversable terrain. Navigation for a non- holonomic mobile robot using fuzzy logic approach has been discussed by Abdessemed et al. [77]. They used an evolutionary algorithm to extract the optimized IF-THEN rules and a new fuzzy image concept is presented to avoid any collision with surrounding environment. Navigation of multiple mobile robots using fuzzy logic controller has been described by Parhi [78]. In this model (Figure 2.8), the fuzzy rules are embedded in the robot controller in order to avoid obstacles in cluttered environments. A set of collision prevention rules are designed using Petri-Net model to avoid collision among one another.

Figure 2.8 Fuzzy logic approach for Mobile robot navigation proposed by Parhi [78].

Multi sensor fusion system _Fuzz ifica ti o n Inference Mechanism Rule base De fu zz ifica ti o n Robot Sensor inputs vl vr Fuzzy controller

A behaviour based robotic control using fuzzy discrete event system (FDES) has been employed by Huq et al. [79]. The developed method exploits the features of fuzzy logic and discrete event system to frame the activity behaviour using fuzzy vector. Wang and Liu [80] have discussed fuzzy logic based real time robot navigation in unknown environments with dead end conditions. Navigation control of robotic vehicle based on the fuzzy behaviour system using multi valued logic framework has been addressed by Selekwa et al. [81]. The mobile robot navigation in local environment using a new fuzzy logic algorithm has been presented by Motlagh et al. [82]. The main task of the developed fuzzy controller is to perform obstacle avoidance and target seeking behaviour. Navigation of multiple mobile robots using fuzzy logic approach has been discussed by Pradhan et al. [83]. Fuzzy logic based navigational controller for Khepera-II mobile robot has been addressed by Obe and Dumitrache [84]. Samsudin et al. [85] have outlined an ordinal fuzzy logic controller for obstacle avoidance of Khepera-II portable mobile robot. It is easier to construct high elucidate rules over the conventional controller and genetic algorithm is incorporated to optimize the structure of the ordinal fuzzy controller. The reactive navigation of mobile robots using a new fuzzy control system has been introduced by Motlagh et al. [86]. They used a causal inference mechanism of the fuzzy cognitive map (FCM) to coordinate various motion concepts using input and output factors, and a genetic algorithm is introduced to tune the fuzzy inference system. Mo et al. [87] have presented a new behaviour based fuzzy control method for mobile robot path planning. They developed a fuzzy controller in which angular velocity of driving wheels is used as output of different behaviours. Abdessemed et al. [88] have presented a new navigation model for a mobile robot using fuzzy logic and stereo vision strategy. They found that some of the fuzzy rules are not triggered in the critical situation for which the stereo vision system can be used to navigate the robot successfully. Abdelkrim et al. [89] have presented a fuzzy algorithm using Kinect sensor for mobile robot path planning. The Kinect sensor is used to detect and localize the static and moving the object, and fuzzy controllers are incorporated for target seeking and obstacle avoidance.