无人机与地面机器人协作探索，边充电边最大化信息获取。
Energy-Aware Collaborative Exploration for a UAV-UGV Team
- 无人机按续航限制分段飞行，地面车同步探索并充当移动充电站。
- 通过时间约束强制会合，每轮探索后无人机必须返回充电，保障续航。
- 构建空地联合路径图，优化探索路线以获取最大信息量，适合复杂环境巡检。
我们提出一种面向未知环境的无人机-地面机器人协同探索框架，其中无人机的能量约束建模为最大飞行时间限制。无人机执行一系列受能量限制的探索航程，而地面机器人同步在地面探索，并作为移动充电站。所有车辆在共享时间预算内强制会合，确保无人机在飞行时限到达前完成每轮探索并返回。我们采用密度感知的分层概率路径图（PRM）构建稀疏耦合的空地路网，并将航程选择建模为受会合约束的耦合定向旅行问题（OP），以在满足时间约束下最大化信息增益。最终生成的航程基于碰撞验证的路网边构建。通过仿真、基准对比和真实实验验证了方法的有效性。
原文摘要 · Abstract (English)
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.
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