Finding minimum-time strategies for point-to-point operation of a robot manipulator is algorithmically difficult and com putationally very intensive, even when the dynamic equations and parameters of the manipulator are precisely known. As a result, the practical applicability of available methods currently is very limited. In this research, fast algorithms to generate near-minimum-time point-to-point motions of a robot manip ulator are proposed, using a “two-step” Lyapunov approach. While not producing exact minimum-time solutions, the algo rithms can be easily used on-line to approximate minimum-time trajectories. The proposed method focuses on changes in the manipulator's kinetic energy during time-optimal motion, in stead of concentrating on the system's state variables, as is usually done in conventional approaches. Its performance is compared in simulation with true optimal solutions and the computed torque method with actuator bounds. The results also suggest that the method may actually compare favorably with “exact” time-optimal solutions when parameter uncertainty is present.
All Science Journal Classification (ASJC) codes
- Modelling and Simulation
- Mechanical Engineering
- Artificial Intelligence
- Electrical and Electronic Engineering
- Applied Mathematics