Distributed MPC for Formation Path-Following of Multi-Vehicle Systems

The paper considers the problem of formation path-following of multiple vehicles and proposes a solution based on combining distributed model predictive control with parametrizations of the trajectories of the vehicles using polynomial splines. Introducing such parametrization leads indeed to two potential benefits: A) reducing the number of optimization variables, and b) enabling enforcing constraints on the vehicles in a computationally efficient way. Moreover, the proposed solution formulates the formation path-following problem as a distributed optimization problem that may then be solved using the alternating direction method of multipliers (ADMM). The paper then analyzes the effectiveness of the proposed method via numerical simulations with surface vehicles and differential drive robotspublishedVersio

Distributed MPC for Formation Path-Following of Multi-Vehicle Systems

The paper considers the problem of formation path-following of multiple vehicles and proposes a solution based on combining distributed model predictive control with parametrizations of the trajectories of the vehicles using polynomial splines. Introducing such parametrization leads indeed to two potential benefits: A) reducing the number of optimization variables, and b) enabling enforcing constraints on the vehicles in a computationally efficient way. Moreover, the proposed solution formulates the formation path-following problem as a distributed optimization problem that may then be solved using the alternating direction method of multipliers (ADMM). The paper then analyzes the effectiveness of the proposed method via numerical simulations with surface vehicles and differential drive robot

Distributed MPC for Formation Path-Following of Multi-Vehicle Systems

The paper considers the problem of formation path-following of multiple vehicles and proposes a solution based on combining distributed model predictive control with parametrizations of the trajectories of the vehicles using polynomial splines. Introducing such parametrization leads indeed to two potential benefits: a) reducing the number of optimization variables, and b) enabling enforcing constraints on the vehicles in a computationally efficient way. Moreover, the proposed solution formulates the formation path-following problem as a distributed optimization problem that may then be solved using the alternating direction method of multipliers (ADMM). The paper then analyzes the effectiveness of the proposed method via numerical simulations with surface vehicles and differential drive robots

Unifying Reactive Collision Avoidance and Control Allocation for Multi-Vehicle Systems

To enable autonomous vehicles to operate in cluttered and unpredictable environments with numerous obstacles, such vehicles need a collision avoidance system that can react to and handle sudden changes in the environment. In this paper, we propose an optimization-based reactive collision avoidance system that uses control barrier functions integrated into the control allocation. We demonstrate the effectiveness of our method through numerical simulations with autonomous surface vehicles. The simulated vehicles track their reference waypoints while maintaining safe distances. The proposed method can be readily implemented on vehicles that already use an optimization-based control allocation method