A model-predictive approach to formation control of omnidirectional mobile robots

This paper presents a solution to the problem of steering a group of real omnidirectional mobile robots along a given path, while maintaining a desired formation pattern. This problem can be divided into a leader agent subproblem and a follower agent subproblem such that a leader agent follows a given path and each follower agent tracks a trajectory, estimated by using the leaderpsilas information. In this paper, we exploit nonlinear model predictive control (NMPC) as a local control law for real-world experiments due to its advantages of taking the robot constraints and future information into account. To solve the path following problem for the leader agent, we propose to integrate the rate of progression of a virtual vehicle to be followed along that path into the local cost function of NMPC. After the open-loop optimization problem is solved, the optimal rate of progression at each time step in the future is obtained. This information and the leaderpsilas current state are broadcasted to all follower agents. With respect to a desired formation configuration and a reference path, each follower agent can estimate its own reference trajectory by using the leaderpsilas information and its time stamp. NMPC is also employed as a local control law to steer the follower agent to track that reference trajectory. Our approach was validated by experiments using three omnidirectional mobile robots.