Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Toward efficient trajectory planning: the path-velocity decomposition
International Journal of Robotics Research
Path planning using a tangent graph for mobile robots among polygonal and curved obstacles
International Journal of Robotics Research
Heuristic fuzzy-neuro network and its application to reactive navigation of a mobile robot
Fuzzy Sets and Systems
Robot Motion Planning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Dynamic Motion Planning for Mobile Robots Using Potential Field Method
Autonomous Robots
International Journal of Hybrid Intelligent Systems
Path planning through time and space in dynamic domains
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 2
Camera calibration with genetic algorithms
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning
Artificial Intelligence Review
Evolving robotic path with genetically optimised fuzzy planner
International Journal of Computational Vision and Robotics
Robotics and Computer-Integrated Manufacturing
Path planning strategy for autonomous mobile robot navigation using Petri-GA optimisation
Computers and Electrical Engineering
Robotic path planning using hybrid genetic algorithm particle swarm optimisation
International Journal of Information and Communication Technology
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A comparative study of various robot motion planning schemes has been made in the present study. Two soft computing (SC)-based approaches, namely genetic-fuzzy and genetic-neural systems and a conventional potential field method (PFM) have been developed for this purpose. Training to the SC-based approaches is given off-line and the performance of the optimal motion planner has been tested on a real robot. Results of the SC-based motion planners have been compared between themselves and with those of the conventional PFM. Both the SC-based approaches are found to perform better than the PFM in terms of traveling time taken by the robot. Moreover, the performance of fuzzy logic-based motion planner is seen to be comparable with that of neural network-based motion planner. Comparisons among all these three motion planning schemes have been made in terms of robustness, adaptability, goal reaching capability and repeatability. Both the SC-based approaches are found to be more adaptive and robust compared to the PFM. It may be due to the fact that there is no in-built learning module in the PFM and consequently, it is unable to plan the velocity of the robot properly.