Abstract

Path planning for minimum time motion of manipulators along specified path amounts to design the motion in a way that end-effector moves along the specified trajectory as fast as possible. It is shown that such a motion is bang-bang in terms of the tangential acceleration of end-effector along the path. The large amount of computation necessary for calculation of maximum and minimum acceleration and switches has made it impossible to introduce an on line time optimal path planning algorithm. Recently a learning algorithm was proposed for finding the switching points. The method, which can be used for both serial and parallel manipulators, is based on a two-switch algorithm with three segments of motion with maximum acceleration, constant velocity and maximum deceleration along the path. This paper presents further investigations on non symmetric trajectories along which manipulator shows different ability in acceleration and deceleration, and introduces some modifications in leaning rules to account for this phenomenon. Two new learning rules are introduced and their performances in different situations are compared through numerical simulations.

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