Application of distributed predictive control to motion and coordination problems for unicycle autonomous robots

This paper presents a Distributed Predictive Control (DPC) approach for the solution of a number of motion and coordination problems for autonomous robots. The proposed scheme is characterized by a multilayer structure: at the higher layer the reference trajectories of the robots are computed as the solution of suitable optimization problems. It is shown that, at this level, the definition of the cost function to be minimized allows to consider many different problems, such as formation control, coverage and optimal sensing, containment control, inter-robot and obstacle collision avoidance, and patrolling in an unknown environment. At the lower layers of the control structure, proper state and control reference trajectories are defined and a robust Model Predictive Control (MPC) problem is solved by each robot. To reduce the computational burden required by the algorithm, collision and obstacle avoidance constraints are reformulated in linear terms, so that the optimization problem to be solved on-line is a Quadratic Programming (QP) one. A number of experimental and simulation results are reported to witness the flexibility and performances of the method.