A Recursive Algorithm for On-line Clustering Obstacles Cluttered in Dynamic Environments
Journal of Intelligent and Robotic Systems
Fuzzy-Logic Based Navigation of Underwater Vehicles
Journal of Intelligent and Robotic Systems
Obstacle Avoidance Path Planning for Mobile Robot Based on Ant-Q Reinforcement Learning Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
An integrated approach to high-level information fusion
Information Fusion
International Journal of Robotics Research
Reactive path planning in a dynamic environment
IEEE Transactions on Robotics
Real-time robot path planning based on a modified pulse-coupled neural network model
IEEE Transactions on Neural Networks
Visual navigation of mobile robot using optical flow and visual potential field
RobVis'08 Proceedings of the 2nd international conference on Robot vision
Computers and Electrical Engineering
Appearance-based navigation and homing for autonomous mobile robot
Image and Vision Computing
Machine Vision and Applications
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Proposes a solution to the two-dimensional (2-D) collision fee path planning problem for an autonomous mobile robot utilizing an electrostatic potential field (EPF) developed through a resistor network, derived to represent the environment. No assumptions are made about the amount of information contained in the a priori environment map (it may be completely empty) or the shape of the obstacles. The well-formulated and well-known laws of electrostatic fields are used to prove that the proposed approach generates an approximately optimal path (based on cell resolution) in a real-time frame. It is also proven through the classical laws of electrostatics that the derived potential function is a global navigation function (as defined by Rimon and Koditschek, 1992), that the field is free of all local minima and that all paths necessarily lead to the goal position. The complexity of the EPF generated path is shown to be O(mnM), where m is the total number of polygons in the environment and nM is the maximum number of sides of a polygonal object. The method is tested both by simulation and experimentally on a Nomad200 mobile robot platform equipped with a ring of sixteen sonar sensors