Energy-Aware Collaborative Exploration for a UAV-UGV Team
This work addresses energy-efficient exploration for autonomous vehicle teams in robotics, though it appears incremental as it builds on existing methods like probabilistic roadmaps and orienteering problems.
The paper tackles the problem of collaborative exploration for a UAV-UGV team in unknown environments by developing an energy-aware framework that models the UAV's energy constraint as a flight-time limit, with the UGV serving as a mobile charging station and enforcing rendezvous under a shared time budget; it validates the method through simulations, benchmarks, and real-world experiments, showing improved performance in exploration tasks.
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.