Trajectory planning and tracking control for cable-driven continuum robots with dynamic obstacles

Abstract

To obtain safe and optimal trajectory planning and high-performance tracking control for cable-driven continuum robots (CDCRs) with dynamic obstacles, a novel two-layer model predictive control (MPC) based trajectory planning and tracking controller is designed. In the outer layer, a soft constraint is incorporated into the cost function via a penalty term and integrated into MPC, thereby ensuring the safety of CDCR obstacle avoidance while effectively mitigating the issue of infeasible solutions that arise from hard constraints. Additionally, an improved grey wolf optimizer (IGWO) is introduced into MPC-based trajectory planner to achieve optimal obstacle avoidance. Particularly, to obtain the motion estimation of dynamic obstacles, a super-twisting observer (STO) is combined with the MPC-based trajectory planner to predict the state of moving obstacles. In the inner layer, the IGWO is merged into MPC to attain high precision and rapid convergence in trajectory tracking control. Both simulation and experimental results show that the proposed two-layer MPC achieves excellent effectiveness in trajectory planning and tracking for the CDCR navigating dynamic obstacles within uncertain environments.