标题： 危险环境中的自主空地车辆操作优化：一种多臂老虎机方法 显示英文标题

标题： Autonomous Air-Ground Vehicle Operations Optimization in Hazardous Environments: A Multi-Armed Bandit Approach

摘要： 危险环境，如化学品泄漏、放射性区域和生物污染现场，对人类安全和公共基础设施构成重大威胁。 在这些环境中，快速且可靠的危险缓解对人类来说通常是不安全的，因此需要能够适应性地感知和应对不断变化的风险的自主系统。 本文提出了一种用于危险环境中自主车辆调度的决策框架，该环境具有不确定且不断变化的风险水平。 该系统将贝叶斯上限置信度（BUCB）感知策略与带收益的任务特定车辆路径问题（VRPP）相结合，实现了无人机（UAVs）在危险感知方面的自适应协调以及地面无人车（UGVs）在清理方面的自适应协调。 使用VRPP可以在资源限制下通过为每个站点分配一个访问值来选择性地进行站点访问，该值反映了感知或清理的优先级。 通过时间加权贝叶斯更新来维护站点级别的危险信念。 BUCB得分指导无人机路径规划，在不确定性下平衡探索与利用，而无人机路径则在资源限制下优化以最大化预期危险减少。 模拟结果表明，与基线调度策略相比，我们的框架平均将解决危险所需的调度周期数减少了约30%，突显了感知不确定性的车辆调度在可靠危险缓解中的价值。 显示英文摘要

摘要： Hazardous environments such as chemical spills, radiological zones, and bio-contaminated sites pose significant threats to human safety and public infrastructure. Rapid and reliable hazard mitigation in these settings often unsafe for humans, calling for autonomous systems that can adaptively sense and respond to evolving risks. This paper presents a decision-making framework for autonomous vehicle dispatch in hazardous environments with uncertain and evolving risk levels. The system integrates a Bayesian Upper Confidence Bound (BUCB) sensing strategy with task-specific vehicle routing problems with profits (VRPP), enabling adaptive coordination of unmanned aerial vehicles (UAVs) for hazard sensing and unmanned ground vehicles (UGVs) for cleaning. Using VRPP allows selective site visits under resource constraints by assigning each site a visit value that reflects sensing or cleaning priorities. Site-level hazard beliefs are maintained through a time-weighted Bayesian update. BUCB scores guide UAV routing to balance exploration and exploitation under uncertainty, while UGV routes are optimized to maximize expected hazard reduction under resource constraints. Simulation results demonstrate that our framework reduces the number of dispatch cycles to resolve hazards by around 30% on average compared to baseline dispatch strategies, underscoring the value of uncertainty-aware vehicle dispatch for reliable hazard mitigation.