An acceleration-based weighting scheme for minimum-effort inverse kinematics of redundant manipulators

Abstract

The minimum-effort solution to the inverse kinematic problem of a redundant manipulator explicitly minimizes the largest component of the joint velocities in magnitude and is thus desirable in the situation where low individual joint velocity is of primary concern. However, the solution may encounter discontinuities because of its nonuniqueness. The aim of This work is two-fold: (i) to propose an acceleration based weighting scheme for preventing the solution discontinuities instead of a nonuniqueness-based weighting scheme, and (H) to present the LVI-based primal-dual neural network for solving online the weighting scheme rather than a dual neural network. The validity and advantages of the acceleration based neural weighting scheme are substantiated by simulation results performed on the four-link planar robot and the PA10 robot manipulator.