Scheme of Operation for Multi-Robot Systems with Decision-Making Based on Markov Chains for Manipulation by Caged Objects

Abstract

This paper presents the design of a new control scheme for a group of omnidirectional robots in a multi-robot system operating in an environment with obstacles. The control scheme uses a decision agent based on discrete-time Markov chains and takes into account the state of the system, obstacle positions, and geometries to manipulate targets, providing robustness against measurement uncertainties. The decision process is dynamic, with state information updating at each time step and tasks being executed based on the hierarchy determined by quadratic hierarchical programming. The system’s stability in the mean-square sense is analyzed through the study of a closed-loop stochastic system, and the effectiveness of the proposed control scheme is demonstrated through numerical simulations, including a comparative analysis with a finite-state machine decision agent.