Improving the Feasibility of DS-based Collision Avoidance Using Non-Linear Model Predictive Control

In this paper we present a novel strategy for reactive collision-free feasible motion planning for robotic manipulators operating inside an environment populated by moving obstacles. The proposed strategy embeds the Dynamical System (DS) based obstacle avoidance algorithm into a constrained non-linear optimization problem following the Model Predictive Control (MPC) approach. The solution of the problem allows the robot to avoid undesired collision with moving obstacles ensuring at the same time that its motion is feasible and does not overcome the designed constraints on velocity and acceleration. Simulations demonstrate that the introduction of the MPC prediction horizon helps the optimization solver in finding the solution leading to obstacle avoidance in situations where a non predictive implementation of the DS-based method would fail. Finally, the proposed strategy has been validated in an experimental work-cell using a Franka-Emika Panda robot.