A Neural Network Based Collision Detection Engine for Multi-Arm Robotic Systems
Rana, A.S. and Zalzala, A.M.S.
(1996)
A Neural Network Based Collision Detection Engine for Multi-Arm Robotic Systems.
Research Report.
ACSE Research Report 615
.
Department of Automatic Control and Systems Engineering
Abstract
A neural ntwork is proposed for collision detection among multiple robotic arms sharing a common workspace. The structure of the neural network is a hybrid between Guassian Radial Basis Function (RBF) neural networks and Multi-layer perceptron back-propagation (BP) neural networks. This network is used to generate potential fields in the configuration space of the robotic arms. A path planning algorithm based on heuristics is presented. It is shown that this algorithm works better than the conventional potential field methods which carry out the planning in the operational space of robots. To show the effectiveness of the algorithm, simulation results are presented for a single 2-DOF robotic arm in presence of a static obstacle and then for two plannar manipulator sharing a common workspace. The algorithm is then extended to the case of 3-DOF arms moving in 3-D space.
Metadata
Item Type:
Monograph
Authors/Creators:
Rana, A.S.
Zalzala, A.M.S.
Copyright, Publisher and Additional Information:
The Department of Automatic Control and Systems Engineering research reports offer a forum for the research output of the academic staff and research students of the Department at the University of Sheffield. Papers are reviewed for quality and presentation by a departmental editor. However, the contents and opinions expressed remain the responsibility of the authors. Some papers in the series may have been subsequently published elsewhere and you are advised to cite the later published version in these instances.
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