A Vector-Based Constrained Obstacle Avoidance Scheme for Wheeled Mobile Redundant Robot Manipulator
Zhijun Zhang, Song Yang, Siyuan Chen, Yamei Luo, Hui Yang, Yu Liu
Abstract
Obstacle avoidance is an important issue when a wheeled mobile redundant robot manipulator (WMRRM) completes given tasks in complex environment. In this article, a novel vector-based constrained obstacle avoidance (VOA) scheme is designed and analyzed for motion planning of the WMRRM. Specifically, the VOA scheme is first described as a quadratic programming (QP) problem subject to an equality constraint, an inequality constraint, and a bound constraint. Compared with the traditional obstacle avoidance scheme, the proposed VOA scheme can not only achieve subtasks, such as obstacle avoidance and physical limit avoidance when the robot completes the main end-effector task, but also greatly expands the range of feasible spaces. In addition, the VOA scheme is transformed into piecewise linear projection equations (PLPEs) and solved by a linear-variational-inequality-based primal-dual neural network (LVI-PDNN), which can efficiently obtain the optimal solutions to the VOA scheme. Computer simulations demonstrate the reliability, accuracy, and effectiveness of the proposed VOA scheme.