Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
Reliable goal-directed reactive control of autonomous mobile robots
Reliable goal-directed reactive control of autonomous mobile robots
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Advances in Intelligent Autonomous Systems
Advances in Intelligent Autonomous Systems
Robot Motion Planning
Fuzzy Sets Engineering
Motion Planning in Dynamic Environments
Motion Planning in Dynamic Environments
Industrial Robot Navigation and Obstacle Avoidance Employing Fuzzy Logic
Journal of Intelligent and Robotic Systems
A Syntactical Approach to Data Fusion
ECSQARU/FAPR '97 Proceedings of the First International Joint Conference on Qualitative and Quantitative Practical Reasoning
Modeling and control of an omnidirectional mobile robot
Proceedings of the IFIP TC5/WG5.3 Forth IFIP/IEEE International Conference on Information Technology for Balanced Automation Systems in Manufacture and Transportation: Advanced Network Enterprises, Virtual Organizations, Balanced Automation, and Systems Integration
NeuroFAST: on-line neuro-fuzzy ART-based structure and parameterlearning TSK model
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A new mobile robot navigation method using fuzzy logic and a modified Q-learning algorithm
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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One of the ultimate goals of mobile robotics research is to build robots that can safely carry out missions in hazardous and populated environments. Most of today's commercial mobile devices scale poorly along this dimension. Their motion planning relies on accurate, static models of the environments, and therefore they often fail their mission if humans or other unpredictable obstacles block their path. To build autonomous mobile robots one has to build systems that can perceive their environments, react to unforeseen circumstances, and plan dynamically in order to achieve their mission. Thus, the objective of the motion planning and control problem is to find collision-free trajectories, in static or dynamic environments containing some obstacles, between a start and a goal configuration. It has attracted much research in recent years. In this context the term control has a broad meaning that includes many different controls, such as low-level motor control, and behaviour control, where behaviour represents many complicated tasks, like obstacle avoidance and goal seeking. This article describes an intelligent motion planning and navigation system for omnidirectional mobile robots based on fuzzy logic. Owing to its simplicity and hence its short response time, the fuzzy navigator is especially suitable for on-line applications with strong real-time requirements. On-line planning is an on-going activity. The planner receives a continuous flow of information about occurring events and generates new commands in response to the incoming events, while previously planned motions are being executed. The fuzzy-rule-base of the proposed system combines the repelling influence, which is related to the distance and the angle between the robot and nearby obstacles, with the attracting influence produced by the distance and the angular difference between the actual direction and position of the robot and the final configuration, to generate actuating commands for the mobile platform. It can be considered as an on-line local navigation method for omnidirectional mobile robots for the generation of instantaneous collision-free motions. This reactive system is especially suitable for real-time applications. The use of fuzzy logic leads to a transparent system which can be tuned by hand or by a set of learning rules. Furthermore, this approach allows obstacle avoidance and navigation in dynamic environments. The functioning of the fuzzy motion planner with respect to omnidirectional mobile robots and results of simulated experiments are presented.