A Knee-Guided Evolutionary Computation Design for Motor Performance Limitations of a Class of Robot With Strong Nonlinear Dynamic Coupling

Robots for high-speed manipulation require to produce motions beyond the performance limitations set by the traditional approaches. Recent results integrate the properties associated with dynamic coupling driving and structural mechanics to compute optimal smooth arm motions; however, when accelerating the convergence speed of potential solutions, those approaches cannot avoid premature convergence. In this article, we propose an autonomous motion planning method at the torque level for a class of robots considering multiple conflicting performance metrics. Specifically, we focus on the hyper dynamic manipulation of a golf swing robot using a knee-guided multiobjective optimization algorithm. Compared with traditional planning methods in position or velocity level, it can study motor performance limitations with strong nonlinear dynamic coupling beyond the motion limits designed by the manufacturers. First, the robot’s joint torque is approximated by the B-spline method using the solution at each iteration. Then, we transform the motion planning problem into a multiobjective optimization problem with soft constraints of torque limits and hard constraints of joint stops, and develop a knee-guided evolutionary algorithm to find the optimization solution with the quality tradeoffs between the scale of parameters and metrics. Finally, we conduct the simulation to demonstrate the dynamics performance of the golf swing robot. The results indicate that our approach can generate superior dynamics performance beyond limits with low energy consumption and high precision.