Optimal Marching of Autonomous Networked Robots

The recent advances in sensors, actuators, robots, and mobile wireless communication technologies have acceleratedinterest in autonomous networked robots (ANRs), where theindividual robots coordinate among themselves to complete atask, e.g., to explore or monitor a Field of Interest (FoI). Byteamwork, which is especially important in complex tasks, ANRsystem expresses much more capacity than traditional staticsensor networks. Existing work focuses on improving the coverageperformance of a group of ANRs within a single FoI. In thisresearch, we consider a group of ANRs that are instructed toexplore a number of FoIs. After they complete a task at currentFoI, they move to the next one, which may be far away fromcurrent one and the shape can also vary dramatically. Ourresearch focuses on how to efficiently enable such transition. TheANRs must be able to redeploy themselves to desired positionsin the new FoI based on distributed algorithms. Besides, to avoidunexpected event breaks network's integrity, the ANRs shouldpreserve their local connectivities as much as they can andorganize themselves as a whole network without any isolatednodes during the transition. Furthermore, considering energyconsumption, such relocation algorithm should work at the costof reasonable total moving distance. We study this problemand call it optimal marching of autonomous networked robots. The proposed algorithms guarantee global connectivity, andpreserve local connectivities as much as possible at negligiblecost of moving distance. Additionally, ANRs can automaticallyadjust their deployment density in the new FoI based on therequirements of various tasks or regions.