Decentralized navigation of multiple agents based on ORCA and model predictive control

Abstract

This paper presents a decentralized strategy for collision-free navigation of multiple agents. This strategy combines the Optimal Reciprocal Collision Avoidance (ORCA) algorithm and Model Predictive Control (MPC). Concretely, each agent applies the decentralized ORCA algorithm to compute the collision-avoiding velocities with respect to its neighbors. The derived velocities serve as constraints of a MPC problem whose solution provides the optimal control input that can ensure optimal motion of the agent. The states predicted from the agents' dynamic models are used in the ORCA algorithm to compute the ORCA velocity regions in future steps. This ORCA-MPC combined approach doesn't need a priori the preferred velocity of each agent in comparison to the traditional ORCA algorithm and its existing variants. Simulation results illustrate the effectiveness of the proposed method, and show that this new algorithm can reduce velocity vibrations in the traditional ORCA algorithm.