In this paper, the path following problem of an omnidirectional mobile robot has been studied. Given the error dynamic model derived from the robot state vector and the path state vector, model predictive control (MPC) is employed to design the control law, which can deal explicitly with the rate of progression of a virtual vehicle to be followed along the path. The distinct advantage over other control strategies is that input and system constraints are able to be handled straightforwardly in the optimization problem so that the robot can travel safely with a high velocity. Unlike nonholonomic mobile robots, omnidirectional mobile robots, which we focus on in this paper, have simultaneously and independently controlled rotational and translational motion capabilities. Then, our purposed MPC controller was validated by experiments with a real omnidirectional mobile robot.

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Abstract-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 leader's 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 leader's 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 leader's 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.

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Abstract:In this paper, we present a novel solution for a path following problem in partially-known static environments. Given linearized error dynamic equations, model predictive control (MPC) is employed to produce a sequence of angular velocities. Since the forward velocity of the robot has to be adapted to environmental constraints and robot dynamics while the robot is following a path, we propose an optimal solution to generate the velocity profile. Furthermore, we integrate an obstacle-avoidance behavior using local sensor information with a path-following behavior based on global knowledge. To achieve this, we introduce new waypoints in order to move the robot away from obstacles while the robot still keeps following the desired path. Extensive simulations and experiments with a physical unicycle mobile robot have been conducted to illustrate the effectiveness of our path following control framework.