Real-Time Dynamic Obstacle Avoidance for Robot Manipulators Based on Cascaded Nonlinear MPC With Artificial Potential Field

Nowadays, the realization of obstacle avoidance for robot manipulators are generally based on offline path planning, which may be insufficient for real-time dynamic obstacle avoidance scenarios. In order to achieve safe and smooth avoidance of dynamic obstacles, a low-latency motion planning algorithm, which takes into account the dynamic planning is of practical significance. Toward this end, this article proposes a cascaded nonlinear model predictive control (MPC) assigned with a two-stage optimization problem. Specially, the high-level MPC combines artificial potential field as a motion planner to generate foresight smooth trajectories. The low-level MPC acts as a trajectory tracker and a safety protector, taking along hard constraints to avoid collisions and singularities. In addition, a super-twisting observer is deployed to enhance the motion estimation accuracy of dynamic obstacles. Experimental results show that the proposed approach is beneficial to achieve safe and smooth dynamic obstacle avoidance in real-world scenarios.