Path Following by Formations of Agents with Collision Avoidance Guarantees using Distributed Model Predictive Control
Ye Wang, Yujia Yang, Ye Pu, Chris G. Manzie · 2021
In this paper, we present a distributed model predictive control (MPC) strategy for path following and formation control of multi-agent systems with collision avoidance guarantees. A distributed MPC for tracking piecewise constant references is formulated. Collision avoidance constraints are employed in the distributed MPC formulation. This formulation can deal with static polytopic obstacles and dynamic obstacles formed by neighbouring agents. Consequently, the overall setup leads to a nonconvex optimization problem but is then shown to be transformed into a mixed-integer quadratic problem that can be implemented efficiently. Finally, some simulation results with three autonomous ground vehicles are provided to demonstrate the proposed distributed MPC strategy.