Kinematic Control and Obstacle Avoidance for Redundant Manipulators Using a Recurrent Neural Network
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Computers & Mathematics with Applications
Multicriteria optimization for coordination of redundant robots using a dual neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Robotics and Computer-Integrated Manufacturing
A hierarchical optimization neural network for large-scale dynamic systems
Automatica (Journal of IFAC)
Journal of Intelligent and Robotic Systems
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This paper presents two neural network approaches to real-time joint torque optimization for kinematically redundant manipulators. Two recurrent neural networks are proposed for determining the minimum driving joint torques of redundant manipulators for the eases without and with taking the joint torque limits into consideration, respectively. The first neural network is called the Lagrangian network and the second one is called the primal-dual network. In both neural-network-based computation schemes, while the desired accelerations of the end-effector for a specific task are given to the neural networks as their inputs, the signals of the minimum driving joint torques are generated as their outputs to drive the manipulator arm. Both proposed recurrent neural networks are shown to be capable of generating minimum stable driving joint torques. In addition, the driving joint torques computed by the primal-dual network are shown never exceeding the joint torque limits