Real-time obstacle avoidance for manipulators and mobile robots
International Journal of Robotics Research
The new ERA in supervised learning
Neural Networks
Computational principles of mobile robotics
Computational principles of mobile robotics
An Behavior-based Robotics
Robot Motion Planning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Forward chaining for robot and agent navigation using potential fields
ACSC '04 Proceedings of the 27th Australasian conference on Computer science - Volume 26
Robust navigation in an unknown environment with minimal sensing and representation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
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The problem we address is adaptive obstacle navigation for autonomous robotic agents in an unknown or dynamically changing environment with a 2-D travel surface without the use of a global map Two well known but hitherto apparently antithetical approaches to the problem, potential fields and BUG algorithms, are synthesised here The best of both approaches is attempted by combining a Mind's Eye with dynamic potential fields and BUG-like travel modes The resulting approach, using only sensed goal directions and obstacle distances relative to the robot, is compatible with a wide variety of robots and provides robust BUG-like guarantees for successful navigation of obstacles Simulation experiments are reported for both near-sighted (POTBUG) and far-sighted (POTSMOOTH) robots The results are shown to support the theoretical design's intentions that the guarantees persist in the face of significant sensor perturbation and that they may also be attained with smoother paths than existing BUG paths.