让无人机和地面车在随机环境中安全节能地协同规划路径。
PRO-SPECT: Probabilistically Safe Scalable Planning for Energy-Aware Coordinated UAV-UGV Teams in Stochastic Environments
- 用概率模型约束能耗风险，确保任务失败概率低于设定值
- 算法可在有限时间内生成满足风险约束的路径计划
- 支持离线规划与在线重规划，适合动态环境下的协同任务
本文研究在随机环境中，无人机（UAV）与地面车（UGV）团队的节能协同路径规划问题。无人机需在最短时间内访问一组空中点，同时遵守能量约束，并依赖地面车作为移动充电站。不同于以往假设行程时间确定或使用固定鲁棒性裕度的方法，本文将行程时间建模为随机变量，将整个任务中能量耗尽的失败概率控制在用户指定的风险水平以下。我们将其建模为混合整数规划问题，并提出PRO-SPECT算法，该算法在多项式时间内生成满足风险边界的路径计划。算法支持离线规划与在线重规划，使团队能够在遭遇扰动时自适应调整，同时保持风险约束。我们提供了关于解可行性与时间复杂度的理论结果，并通过数值对比与仿真验证了方法的有效性。
原文摘要 · Abstract (English)
We consider energy-aware planning for an unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV) team operating in a stochastic environment. The UAV must visit a set of air points in minimum time while respecting energy constraints, relying on the UGV as a mobile charging station. Unlike prior work that assumed deterministic travel times or used fixed robustness margins, we model travel times as random variables and bound the probability of failure (energy depletion) across the entire mission to a user-specified risk level. We formulate the problem as a Mixed-Integer Program and propose PRO-SPECT, a polynomial-time algorithm that generates risk-bounded plans. The algorithm supports both offline planning and online re-planning, enabling the team to adapt to disturbances while preserving the risk bound. We provide theoretical results on solution feasibility and time complexity. We also demonstrate the performance of our method via numerical comparisons and simulations.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。