Energy-Aware Collaborative Exploration for a UAV-UGV Team
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866914416479436800 |
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| author | Er, Cahit Ikbal Juttu, Saikiran Yazicioglu, Yasin |
| author_facet | Er, Cahit Ikbal Juttu, Saikiran Yazicioglu, Yasin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22507 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Energy-Aware Collaborative Exploration for a UAV-UGV Team Er, Cahit Ikbal Juttu, Saikiran Yazicioglu, Yasin Robotics Multiagent Systems 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. |
| title | Energy-Aware Collaborative Exploration for a UAV-UGV Team |
| topic | Robotics Multiagent Systems |
| url | https://arxiv.org/abs/2603.22507 |