发表机构
University at Buffalo（布法罗大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对人类-UGV伤员疏散，提出可通行性感知协同路径规划，通过优化切换点利用互补通行能力，平均任务时间较基线降低5.3%，能耗降低17.8%。
AI 中文摘要
异构多机器人路径规划是一个被广泛研究的问题，其中具有不同运动学和动力学模型的智能体必须协调以实现共同目标。然而，这些公式将所有智能体视为机器人——其成本模型是机械性的，其可通行性由传感器推导。在人机协作中，人类伙伴仍被降级为指挥和监督角色，而非被建模为具有独特移动约束和动态能量储备的物理共同导航者。本研究探讨了在搜救伤员检索场景中，由人类和UGV组成的双智能体团队的联合路径规划。我们使用Pandolf-Santee代谢成本模型对人类智能体进行建模，该模型具有疲劳调节速度；使用滚动阻力能量模型对UGV进行建模，该模型具有地形相关速度限制。通过利用每个智能体的互补可通行性——人类穿越茂密植被和浅水的能力与UGV在开阔地形和道路上的优越速度——我们优化伤员转移位置（称为切换点），以最小化总任务时间。在多个合成的1km2环境（具有程序化生成的高程和土地覆盖数据）中评估，优化策略相对于仅人类基线将平均任务时间减少5.3%，相对于无切换点优化的朴素人类-UGV策略减少7.0%，同时相对于基线将人类能量消耗减少17.8%。值得注意的是，朴素策略将人类能量消耗减少更多（22.4%），但相对于基线导致任务时间增加2%，说明切换点优化对于实现人类-UGV协作的时间节省是必要的。
英文摘要
Heterogeneous multi-robot path planning is a well-studied problem in which agents with disparate kinematic and dynamic models must coordinate to achieve shared objectives. These formulations, however, treat all agents as robotic-their cost models are mechanical and their traversability is sensor-derived. In human-robot teaming, the human partner remains relegated to command and supervisory roles rather than being modeled as a physical co-navigator with distinct mobility constraints and dynamic energy reserves. This work investigates joint path planning for a two-agent human-UGV team in search-and-rescue casualty retrieval scenarios. We model the human agent using the Pandolf-Santee metabolic cost model with fatigue-modulated speed, and the UGV using a rolling-resistance energy model with terrain-dependent speed limits. By exploiting the complementary traversability of each agent-the human's ability to traverse dense vegetation and shallow water versus the UGV's superior speed on open terrain and roads-we optimize casualty transfer locations, termed switch points, to minimize total mission time. Evaluated across multiple synthetic 1km2 environments with procedurally generated elevation and land-cover data, the optimized strategy reduces mean mission time by 5.3% relative to a human-only baseline and by 7.0% relative to a naive human-UGV strategy without switch point optimization, while reducing human energy expenditure by 17.8% relative to baseline. Notably, the naive strategy reduces human energy expenditure by a larger margin (22.4%) but incurs a 2% increase in mission time relative to baseline, illustrating that switch point optimization is necessary to realize time savings from human-UGV teaming.
Comments6 pages, 4 figures
