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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 work presented is concentrated on the trajectory planning phase of an online 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.