A Bi-Criteria Kinematic Strategy for Motion/Force Control of Robotic Manipulator

Different from conventional motion/force control strategies based on robotic dynamics, this paper presents a kinematic perspective to convert the motion/force control problem into a bi-criteria optimization problem. Specifically, the motion and force errors are formulated as an equality constraint at the kinematics level. Through a weight coefficient, the minimum infinite norm of joint velocity and the alternative kinematic index are integrated as a bi-criteria objective function. On this basis, a bi-criteria hybrid motion/force control (BHMFC) strategy is proposed with kinematic analyses on robotic manipulators. This bi-criteria kinematic strategy fulfills the potentials of robotic manipulators involving the functions of hybrid index optimization, hybrid control of motion and force, end-effector posture maintaining, and physical constraints. Furthermore, the related dynamic neural network (DNN) with theoretical analyses is presented to explore the optimal solution to the BHMFC strategy. Finally, computer simulations, physical experiments, and strategy comparisons are conducted to demonstrate the feasibility, efficiency, and superiority of the proposed BHMFC strategy. This work presents an efficient kinematic approach to address robot motion/force control problems with promising research prospects. Note to Practitioners-This paper is motivated by potential improvements of motion/force hybrid control schemes of robotic manipulators in a kinematic manner. Existing motion/force control methods typically rely on robot dynamics, which are difficult to satisfy kinematic task requirements, such as physical constraints and task optimizations. To this end, a bi-criteria hybrid motion/force control (BHMFC) strategy is proposed to achieve kinematic performance improvements in a quadratic program framework. Specifically, the designed constraints exploit the functions of hybrid control of motion and force, physicalconstraints, and end-effector posture maintaining. Besides, the kinematic optimization and joint velocity reduction are implemented by a bi-criteria objective function. Besides, we propose a dynamic neural network (DNN) based on Karush-Kuhn-Tucker conditions to solve the BHMFC strategy and theoretically analyze its global convergence ability and convergence rate. Simulative and experimental results show that the proposed method outperforms the traditional pseudoinverse method in terms of accurate position/force control performance and end-effector posture maintenance. In addition, computational analysis of control signals and comparisons with existing technologies highlight the feasibility and superiority of the proposed method.