A GNN for repetitive motion generation of four-wheel omnidirectional mobile manipulator with nonconvex bound constraints

This paper proposes a gradient neural network (GNN) to solve the repetitive motion gen-eration scheme of the omnidirectional four-wheel mobile manipulator. The overall kine-matics model of the omnidirectional mobile platform and the manipulator fixed on omnidirectional platform are established. First, the analysis of the current repetitive move-ment generation (RMG) scheme for the kinematic control of the manipulator can find that the position error does not theoretically converge to zero and fluctuates. This paper ana-lyzes the phenomenon from a theoretical viewpoint and reveals that the current RMG scheme has position errors associated with joint errors. Then, to solve the shortcomings of the current solution, an orthogonal projection repetitive motion generation (OPRMG) method is proposed, which theoretically eliminates position errors and decouples joint space and Cartesian space. Using the gradient descent method to establish the correspond -ing GNN aided with the speed compensation, and provide theoretical analysis to reflect the stability. Moreover, the joint speed limit in the RMG scheme is extended to nonconvex con-straints. The advantages of the OPRMG scheme are demonstrated by the simulation results of the omnidirectional mobile manipulator (OMM) synthesized by the current GNNRMG and the proposed GNNOPRMG. In addition, by adjusting the feedback coefficient, the high performance of the OPRMG scheme can be verified by simulation and comparison of the position error (PE) and joint error (JE) of the OMM.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper proposes a gradient neural network (GNN) to solve the repetitive motion generation scheme of the omnidirectional four-wheel mobile manipulator. The current solution is analyzed and found to have position errors associated with joint errors. To address this issue, an orthogonal projection repetitive motion generation (OPRMG) method is proposed, and a corresponding GNN model is established using the gradient descent method. Simulation results demonstrate the advantages of the OPRMG scheme.