This paper addresses the problem of motion planning for a multirobot system in a partially known environment where conditions such as uncertainty about robots' positions and communication delays are real. In particular, we detail the use of a Distributed Receding Horizon Approach that guarantees collision avoidance with static obstacles and between robots communicating with each other. Underlying optimization problems are solved by using a Sequential Least Squares Programming algorithm. Experiments with real nonholonomic mobile platforms are performed. The proposed framework is compared with the Dynamic Window approach to motion planning in a single robot setup. A second experiment shows results for a multirobot case using two robots where collision is avoided even in presence of significant localization uncertainties.

This paper proposes an improvement of a motion planning approach and a modified model predictive control (MPC) for solving the navigation problem of a team of dynamical wheeled mobile robots in the presence of obstacles in a realistic environment. Planning is performed by a distributed receding horizon algorithm where constrained optimization problems are numerically solved for each prediction time-horizon. This approach allows distributed motion planning for a multi-robot system with asynchronous communication while avoiding collisions and minimizing the travel time of each robot. However, the robots dynamics prevents the planned motion to be applied directly to the robots. Using unicycle-like vehicles in a dynamic simulation, we show that deviations from the planned motion caused by the robots dynamics can be overcome by modifying the optimization problem underlying the planning algorithm and by adding an MPC for trajectory tracking. Results also indicate that this approach can be used in systems subjected to real-time constraint.