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This research addresses a critical bottleneck in autonomous systems by optimizing energy consumption for UAV-UGV teams through mobile charging strategies and probabilistic roadmap planning. The study focuses on maximizing information gain within strict flight-time constraints, offering significant implications for the scalability of multi-agent robotic deployments where battery life is a primary limiting factor. While rooted in robotics control theory, the core innovation lies in applying advanced optimization algorithms to extend operational endurance without relying solely on larger batteries or fixed charging infrastructure.
Energy-Aware Collaborative Exploration for a UAV-UGV Team
arXiv:2603.22507v2 Announce Type: replace Abstract: We present an energy-aware collaborative exploration framework for a UAV-UGV team operating in unknown environments, where the UAV's energy constraint is modeled as a maximum flight-time limit. The UAV executes a sequence of energy-bounded exploration tours, while the UGV simultaneously explores on the ground and serves as a mobile charging station. Rendezvous is enforced under a shared time budget so that the vehicles meet at the end of each tour before the UAV reaches its flight-time limit. We construct a sparsely coupled air-ground roadmap using a density-aware layered probabilistic roadmap (PRM) and formulate tour selection over the roadmap as coupled orienteering problems (OPs) to maximize information gain subject to the rendezvous constraint. The resulting tours are constructed over collision-validated roadmap edges. We validate our method through simulation studies, benchmark comparisons, and real-world experiments.
Abstract:We present an energy-aware collaborative exploration framework for a UAV-UGV team operating in unknown environments, where the UAV's energy constraint is modeled as a maximum flight-time limit. The UAV executes a sequence of energy-bounded exploration tours, while the UGV simultaneously explores on the ground and serves as a mobile charging station. Rendezvous is enforced under a shared time budget so that the vehicles meet at the end of each tour before the UAV reaches its flight-time limit. We construct a sparsely coupled air-ground roadmap using a density-aware layered probabilistic roadmap (PRM) and formulate tour selection over the roadmap as coupled orienteering problems (OPs) to maximize information gain subject to the rendezvous constraint. The resulting tours are constructed over collision-validated roadmap edges. We validate our method through simulation studies, benchmark comparisons, and real-world experiments.
Robotics (cs.RO); Multiagent Systems (cs.MA)
arXiv:2603.22507 [cs.RO]
(or arXiv:2603.22507v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.22507 Focus to learn more arXiv-issued DOI via DataCite
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