Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Fuzzy drive control of an autonomous mobile robot
Fuzzy Sets and Systems - Special issue on applications of fuzzy systems theory, Iizuka '88
Theory of T-norms and fuzzy inference methods
Fuzzy Sets and Systems - Special memorial volume on fuzzy logic and uncertainly modelling
Fuzzy systems theory and its applications
Fuzzy systems theory and its applications
Gross motion planning—a survey
ACM Computing Surveys (CSUR)
Artificial Intelligence Methods and Applications
Artificial Intelligence Methods and Applications
Robot Motion Planning
Intelligent Robotic Systems
Fuzzy and Neural Approaches in Engineering
Fuzzy and Neural Approaches in Engineering
Optimized-Motion Planning: Theory and Implementation
Optimized-Motion Planning: Theory and Implementation
Language and Learning for Robots
Language and Learning for Robots
Advanced Guided Vehicles: Aspects of the Oxford AGV Project
Advanced Guided Vehicles: Aspects of the Oxford AGV Project
Intelligent Robotic Planning Systems
Intelligent Robotic Planning Systems
Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators
Journal of Intelligent and Robotic Systems
Journal of Intelligent and Robotic Systems
Neurofuzzy Learning of Mobile Robot Behaviours
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
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A neurofuzzy methodology is presented for motion planning in semi-autonomous mobile robots. The robotic automata considered are devices whose main feature is incremental learning from a human instructor. Fuzzy descriptions are used for the robot to acquire a repertoire of behaviors from an instructor which it may subsequently refine and recall using neural adaptive techniques. The robot is endowed with sensors providing local environmental input and a neurofuzzy internal state processing predictable aspects of its environment. Although it has no prior knowledge of the presence or the position of any obstructing objects, its motion planner allows it to make decisions in an unknown terrain. The methodology is demonstrated through a robot learning to travel from some start point to some target point without colliding with obstacles present in its path. The skills acquired are similar to those possessed by an automobile driver. The methodology has been successfully tested with a simulated robot performing a variety of navigation tasks.