Compatible Convex-Nonconvex Constrained QP-Based Dual Neural Networks for Motion Planning of Redundant Robot Manipulators

Redundant robot manipulators possess huge potential of applications because of their superior flexibility and outstanding accuracy, but their real-time control is a challenging problem. In this brief, a novel compatible convex-nonconvex constrained quadratic programming (CCNC-QP)-based dual neural network (DNN) scheme is proposed for motion planning of redundant robot manipulators. The proposed CCNC-QP-DNN scheme not only has the advantages of DNN, e.g., parallel processing and real-time control, but also possesses the advantages of CCNC-QP, such as the zeroing initial error, considering convex or nonconvex constraints. Being different from most neural networks, the proposed approach is training-free and is able to track reference signals with superior accuracy and speedability. The detailed derivation process and theoretical analysis are presented. Computer simulations with five end-effector tasks verify the effectiveness and accuracy of the proposed control method in both the convex constraints condition and nonconvex constraints condition whether an initial error exists or not.