Actual Shape-Based Obstacle Avoidance Synthesized by Velocity–Acceleration Minimization for Redundant Manipulators: An Optimization Perspective

From the optimization perspective, this article proposes a novel actual shape-based obstacle avoidance synthesized by velocity–acceleration minimization (ASOA-VAM) scheme that performs operational tasks safely in a complex environment utilizing redundant manipulators. Concretely, an actual shape-based obstacle avoidance (ASOA) strategy with a variable magnitude escape acceleration using the Gilbert–Johnson–Keerthi distance algorithm is presented. Trajectory tracking, the end-effector's errors feedback, and the joint multilevel physical limits (joint angle, -velocity, and -acceleration limits) avoidance are also incorporated into this optimization scheme. Meanwhile, the velocity–acceleration minimization (VAM) measure is developed. Combining the ASOA strategy with the VAM measure, the ASOA-VAM scheme is formed and further reformulated as a quadratic program (QP). Moreover, a recurrent neural network with theoretically provable convergence is designed to solve the QP online. Finally, simulations, comparisons, and experiments of a 7-degree-of-freedom manipulator with engineering applications illustrate the ASOA-VAM scheme's effectiveness, accuracy, superiority, and physical realizability.