A punishment neural network-based acceleration-level joint drift-free scheme for solving constrained motion planning problem of redundant robotic manipulators.

Abstract

To solve the repetitive motion problem of redundant robotic manipulators, a punishment neural network-based acceleration-level joint drift-free (PNN-ALJDF) scheme is designed. Traditional joint physical limits constraints are fixed and lack margin. Thus, a novel joint acceleration time-varying constraint is considered in the PNN-ALJDF scheme to avoid the joint state exceeding the physical limits. In addition, to ensure that redundant robotic manipulators can periodically return to the initial pose, a joint drift-free criterion is designed. Furthermore, the joint drift-free criterion, kinematics equation and joint acceleration time-varying constraint are formulated globally as an acceleration-level joint drift-free (ALJDF) scheme by a time-varying quadratic programming approach. Then, the ALJDF scheme is solved by the designed punishment neural network. Thus, the proposed PNN-ALJDF scheme is composed of the ALJDF scheme and punishment neural network. Finally, the simulations demonstrate that the PNN-ALJDF scheme avoids joints from drifting, and the states of joints are all within the acceleration time-varying constraint. In addition, the proposed PNN-ALJDF has higher solution accuracy than the linear variational inequalities-based primal-dual neural network.