Multirobot motion planning and control has been investigated for decades and is still an
active research area due to the growing demand for both performance optimality and safety …

This work presents a method for multi-robot coordination based on a novel distributed
nonlinear model predictive control (NMPC) formulation for trajectory optimization and its …

This paper proposes Distributed Model Predictive Covariance Steering (DMPCS), a novel
method for safe multi-robot control under uncertainty. The scope of our approach is to blend …

The implementation of optimization-based motion coordination approaches in real world
multi-agent systems remains challenging due to their high computational complexity and …

Data-based machine learning methods have been successfully applied to control system
design in recent years. However, safety during the learning and control process is difficult to …

This work is concerned with the problem of planning trajectories and assigning tasks for a
Multi-Agent System (MAS) comprised of differential drive robots. We propose a multirate …

When considering a distributed control framework for a multi-agent system, care has to be
taken to respect the limited information available to each agent as well as the amount of …

For the tracking and formation problem of multi-agent systems with collision avoidance, a
synchronous distributed model predictive control (DMPC) algorithm is proposed. We …

In this work, we consider the problem of decentralized multi-robot target tracking and
obstacle avoidance in dynamic environments. Each robot executes a local motion planning …

We present a distributed model predictive control (DMPC) algorithm to generate trajectories
in real-time for multiple robots. We adopted the on-demand collision avoidance method …