Intelligent planning of trajectories for pick-and-place operations

Intelligent industrial robots, unlike their current re-programmable counterparts, should be able to work in a time varying environment and must have the capability to respond to unanticipated situations. The current work is concentrated on the trajectory planning phase of lan on-line prediction planning execution strategy for such situations. A neural network based solution for the generation of time-based control set points is proposed. This solution uses a feedback neural network, which can be trained using the extensively used backpropagation algorithm. The proposed neural-based trajectory generator ensures a real-time solution consistent with electric actuator safety requirements. The generator is evaluated for pick-and-place operations of a RRR manipulator.