Wall Following and Obstacle Avoidance Control in Roisc-v1.0 (Robotic Disinfectant) using Behavior Based Control

Author(s):  
Yudha Sadewa ◽  
Eko Henfri Binugroho ◽  
Nofria Hanafi ◽  
Ir. Dadet Pramadihanto ◽  
Achmad Fauzi ◽  
...  
1999 ◽  
Vol 11 (6) ◽  
pp. 502-509 ◽  
Author(s):  
Palitha Dassanayake ◽  
◽  
Keigo Watanabe ◽  
Kiyotaka Izumi ◽  
◽  
...  

Our objective is for a 3-link manipulator to reach a target while avoiding obstacles with online information using a fuzzy-behavior-based control approach. Control applied to mobile robots elsewhere is modified to suit to the manipulator. Fuzzy behavior elements are trained using a genetic algorithm. A component apart from the basic concept is introduced to overcome gravitation. Result shows the manipulator reaches the target with an acceptable solution for 3 simulations, so the proposed approach is suitable to multilink manipulator task control.


2016 ◽  
Vol 9 (1) ◽  
pp. 9 ◽  
Author(s):  
Andi Adriansyah ◽  
Shamsudin H. Mohd. Amin

Behavior-based control architecture has been broadly recognized due to their compentence in mobile robot development. Fuzzy logic system characteristics are appropriate to address the behavior design problems. Nevertheless, there are problems encountered when setting fuzzy variables manually. Consequently, most of the efforts in the field, produce certain works for the study of fuzzy systems with added learning abilities. This paper presents the improvement of fuzzy behavior-based control architecture using Particle Swarm Optimization (PSO). A wall-following behaviors used on Particle Swarm Fuzzy Controller (PSFC) are developed using the modified PSO with two stages of the PSFC process. Several simulations have been accomplished to analyze the algorithm. The promising performance have proved that the proposed control architecture for mobile robot has better capability to accomplish useful task in real office-like environment.


Author(s):  
Baoyu Shi ◽  
Hongtao Wu

Path planning technology is one of the core technologies of intelligent space robot. Combining image recognition technology and artificial intelligence learning algorithm for robot path planning in unknown space environment has become one of the hot research issues. The purpose of this paper is to propose a spatial robot path planning method based on improved fuzzy control, aiming at the shortcomings of path planning in the current industrial space robot motion control process, and based on fuzzy control algorithm. This paper proposes a fuzzy obstacle avoidance method with speed feedback based on the original advantages of the fuzzy algorithm, which improves the obstacle avoidance performance of space robot under continuous obstacles. At the same time, we integrated the improved fuzzy obstacle avoidance strategy into the behavior-based control technology, making the avoidance become an independent behavioral unit. Divide the path planning into a series of relatively independent behaviors such as fuzzy obstacle avoidance, cruise, trend target, and deadlock by the behavior-based method. According to the interaction information between the space robot and the environment, each behavior acquires the dominance of the robot through the competition mechanism, making the robot complete the specific behavior at a certain moment, and finally realize the path planning. Furthermore, to improve the overall fault tolerance of the space, robot we introduced an elegant downgrade strategy, so that the robot can successfully complete the established task in the case of control command deterioration or failure of important information, avoiding the overall performance deterioration effectively. Therefore, through the simulation experiment of the virtual environment platform, MobotSim concluded that the improved algorithm has high efficiency, high security, and the planned path is more in line with the actual situation, and the method proposed in this paper can make the space robot successfully reach the target position and optimize the spatial path, it also has good robustness and effectiveness.


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