An energy-efficient and self-triggered control method for robot swarm networking systems

Abstract

Robot swarm networking systems (RSNS) exhibit complex emergent behavior by using local control laws based on spatial information from nearby environment and adjacent robot agents. The consensus behavior of the RSNS depends on a set of parameters of robot agent algorithms or system parameters for their operation, issued mainly by the operator. The challenge in the RSNS is developing techniques for the operator to interact with the RSNS in order to make system behavior adaptive to changes in system configuration and for operator commands without having to handle them individually. Another challenge is saving energy consumption over the robot agents in the RSNS by reducing the number of information exchange between robot agents when the system configuration is spread out the network from the operator. To address these issues, this paper presents an energy-efficient control approach for system configuration propagation with self-triggering control. The proposed method controls the RSNS operation by indirectly propagating the system configuration within the framework of local rules. Moreover, a self-triggered propagation model is designed according to the convergence rate of configuration propagation in order to save and to balance energy consumption among robot agents in the RSNS. This model is then extended to an optimal timing control, where the operator determines its next input time without having to keep track of all the states of robot agents. Theoretical analysis and simulation results are performed to demonstrate the superiority of the proposed method.