An Orthogonal Repetitive Motion and Obstacle Avoidance Scheme for Omnidirectional Mobile Robotic Arm

Abstract

Obstacle avoidance is essential for an omnidirectional mobile robotic arm (OMRA) to accomplish a given task in a complex environment. A hybrid orthogonal repetitive motion and obstacle avoidance (HORMAOA) scheme is developed and analyzed to address the problem of the OMRA not being able to accurately return to the starting position after completing the obstacle avoidance task. Compared with the traditional obstacle avoidance schemes, the HORMAOA scheme decouples joint space error and Cartesian space error, which enables the OMRA to achieve obstacle avoidance accurately, physical limit avoidance, and repetitive motion tasks. Moreover, the HORMAOA scheme is transformed into a piecewise linear projection equation (PLPE) and solved using a linear-variational-inequality-based primal-dual neural network (LVI-PDNN), which can effectively obtain the optimal solution of the HORMAOA scheme. The validity and accuracy of the scheme are verified through illustrative examples, experiments, and comparisons.