Estimation of inertial parameters of manipulator loads and links
International Journal of Robotics Research
Robotics: control, sensing, vision, and intelligence
Robotics: control, sensing, vision, and intelligence
Multilayer feedforward networks are universal approximators
Neural Networks
Real-time software for control: program examples in C
Real-time software for control: program examples in C
Manipulator motion planning in the presence of obstacles and dynamic constraints
International Journal of Robotics Research
Biologically inspired neural network approaches to real-time collision-free robot motion planning
Biologically inspired robot behavior engineering
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Planning appropriate trajectories can significantly increase the productivity of robot systems. To plan realistic time-optimal trajectories, the robot dynamics have to be described precisely. In this paper, a neural network based algorithm for tim e-optimal trajectory planning is introduced. This method utilises neural networks for representing the inverse dynamics of the robot. As the proposed neural networks can be trained with data obtained from exciting the robot with given torque inputs, they will capture the complete dynamics of the robot system. Threfore, the trajectories generated will be mo re realistic than those obtained by using nominal dynamic equations based on nominal parameters. Time-optimal trajectories are generated for a PUMA robot to demonstrate the proposed method.