Multicriteria optimization for coordination of redundant robots using a dual neural network

A dual neural-network method for the coordination of kinematically redundant robots is proposed in this paper. The performance criteria for single robots provided by Nedungadi and Kazerounian are generalized to a multicriteria form for the coordinated-manipulation system composed of multiple serial manipulators. By optimizing the local joint torques and generalized forces applied on the object/workpiece using a designed weighting matrix, the proposed method achieves the global stability during the coordinated-manipulation process. Moreover, the proposed algorithm has an explicit physical meaning, i.e., both the global kinetic energy of the coordination system and the two-norm of the generalized forces applied on the object are minimized simultaneously. In addition, the physical limits of both joint torques and the generalized forces applied on the object are considered, which makes the original coordination problem become a complicated optimization problem subject to both equality and inequality constraints. Compared with numerical optimization algorithms used in existing literatures, the dual neural-network method has better computational capability to deal with the complicated optimization problem. Finally, illustrative examples are given to show that the proposed method is effective and efficient for the multirobot coordinated-manipulation system.