Robust Supervisory-Based Control Strategy for Mobile Robot Navigation

This work introduces a novel control strategy to allow a class of mobile robots to robustly navigate in a dynamic and potentially cluttered environment. The proposed approach combines a high-level motion planner, designed considering the supervisory control theory, and a low-level stabilizing feedback control law. Taking advantage of a symbolic description of the vehicle dynamics and of the environment, the supervisor reactively selects the current motion primitive to be executed so as to reach the desired target location optimally with respect to a given index cost. Sufficient conditions ensuring boundedness of the tracking error are derived in order to handle the interaction between the discrete-time dynamics of the supervisor and the continuous-time dynamics of the low-level control loop in charge of tracking the desired reference. The resulting approach allows to employ supervisory control tools online without affecting the stability properties of the continuous-time low-level control loop. The results are demonstrated by considering, as application, the kinematic model of an aerial vehicle navigating in a cluttered environment.