Discrete event theory for the monitoring and control of robotic systems

Abstract

Discrete event systems are presented as a powerful framework for a large number of robot control tasks. Their advantage lies in the ability to abstract a complex problem to the essential elements needed for task level control. Discrete event control has proven to be successful in numerous robotic applications, including assembly, on-line training of robots, mobile navigation, control of perception capabilities, and human-robot shared control. This chapter presents a general description of the discrete event modelling and control synthesis for constrained motion systems. Additionally, methods for the effective monitoring of the process based on the detection and identification of discrete events are given. In each of these areas, open research questions are discussed. Advances in the discrete event modelling of robotic systems, especially event definitions based on a solid mathematical formulation, are essential for broadening the range of applications. Robust process monitoring techniques, as well as sensory fusion methods, are needed for successful practical implementations. Control synthesis methods which are truly hybrid, incorporating both the continuous and the discrete event control, would significantly advance the use of discrete event theory. Lastly, convergence proofs under practical assumptions relevant to robotics are needed.