Coordinated Recharging of Heterogeneous Mobile Robot Teams during Continuous Large Scale Missions

Abstract

Currently, the coordinated use of autonomous mobile robot teams seems to be one of the most promising and ambitious technologies to provide an efficient solution to a range of economic, military, and environmental problems. Complex large-scale operations in different vast and dynamic environments usually involve long stay activities of vehicles under the limited battery capacity. Since it is not always possible to replace low-battery robots with functionally equivalent, but fully recharged vehicles on schedule, available charging stations on-site are required for long-term mission success. In this context, a reasonable recharging schedule must be generated to maintain continuous and uninterruptible mission implementation. It should be taken into account, that the real-world multi-vehicle systems are partially self-contained and could be subjected to any malfunctions or unforeseen events and thus should be able to adjust the current strategy autonomously if needed. Summarizing the above, two following problems have to be handled at once: to determine the reasonable recharging order for the team over the extended time and to ensure the possibility of regular communication sessions within the group.In this paper, we propose the high-level dynamic mission planner based on the rendezvous point-selection scheme. The idea is to represent a mission as a sequence of time-limited operating periods with final group rendezvous at the end of each period. The planning problem here is to allocate each rendezvous point and assign it to an event of either communication or regrouping (robots leaving the group for recharging or joining the group after recharging) nature to achieve regular recharging in preferably alternate order. While doing this, the group’s parametric and functional heterogeneity should be taken into account to ensure the working squad’s maximal performance capabilities. We propose the hybrid evolutionary approach to implement the suggested planner using specialized heuristics, advanced solution improvement techniques, and original chromosome coding schemes.