Kinematic Control and Obstacle Avoidance for Redundant Manipulators Using a Recurrent Neural Network

Authors:
Wai Sum Tang;Cherry Miu Ling Lam;Jun Wang
Affiliations:
-;-;-
Venue:
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Year:
2001

Citing 3
Cited 2

Real-time obstacle avoidance for manipulators and mobile robots

International Journal of Robotics Research
Two recurrent neural networks for local joint torque optimizationof kinematically redundant manipulators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Lagrangian network for kinematic control of redundant robot manipulators

IEEE Transactions on Neural Networks

Obstacle avoidance of redundant manipulators using neural networks based reinforcement learning

Robotics and Computer-Integrated Manufacturing
Adaptive neural controller for redundant robot manipulators and collision avoidance with mobile obstacles

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, a recurrent neural network called the Lagrangian network is applied for obstacle avoidance in kinematically redundant manipulators. Conventional numerical methods implemented in digital computers for obstacle avoidance redundancy resolution calculation could only compute the solution in milliseconds while neural network realized by hardware could complete the computation in microseconds, which is more desirable in real-time control of manipulators. By giving the desired end-effector velocities and the obstacle location, the neural network could generate the joint velocity vector which drives the manipulator to avoid obstacles and tracks the desired end-effector trajectory simultaneously. Simulation results show that the neural network is capable of computing the redundancy resolution for obstacle avoidance.