Metaheuristic-Based RNN for Manipulability Optimization of Redundant Manipulators

Manipulability optimization plays a crucial role in the kinematic control of redundant manipulators, as it reduces their risks of entering a singular state. However, manipulability is a nonlinear and nonconvex function with respect to joint angles. The existing kinematic schemes either do not consider the manipulability optimization or require transforming the nonconvex problem into a convex one, which may affect achieving the optimal value of manipulability. Furthermore, obstacle avoidance is rarely considered in the existing manipulability optimization methods. To address these limitations, this article proposes a manipulability optimization with obstacle avoidance constraints (MOOAC) scheme. Subsequently, a metaheuristic-based recurrent neural network (MRNN) model is constructed, which can directly handle a nonlinear and nonconvex problem with constraints and ensure achieving the global optimal with probability 1. In addition, the proposed MOOAC scheme is solved by the MRNN model at the joint angle level, which can handle the limits of joint angle and joint velocity without reducing the feasible region of decision variables. Computer simulations and physical experiments are provided to demonstrate the accuracy and superiority of the proposed scheme.