标题： 灾难响应中时间依赖的无人机、工人和车辆的自主协同调度用于群感测 显示英文标题

标题： Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response

摘要： 自然灾害给人类社会造成了重大损失，及时高效地获取灾后环境信息对于有效实施救援行动至关重要。由于灾后环境的复杂性，现有的传感技术面临着环境适应性弱、专业感知能力不足以及传感解决方案实用性有限等问题。本文探讨了异构多智能体在线自主协作调度算法 HoAs-PALN，旨在实现灾后环境信息的有效采集。HoAs-PALN 通过匹配过程中的自适应降维和局部纳什均衡博弈来实现，促进了时间相关的无人机、工人和车辆之间的自主协作，从而提高感知调度效率。(1) 在匹配过程中的自适应降维方面，HoAs-PALN 将五维匹配过程转化为两类三维匹配过程，显著减少了调度决策时间；(2) 关于局部纳什均衡博弈，HoAs-PALN 结合 softmax 函数优化行为选择概率，并引入局部纳什均衡确定机制以确保调度决策性能。最后，我们基于广泛的现实世界和模拟数据进行了详细实验。与基线算法（GREEDY、K-WTA、MADL 和 MARL）相比，HoAs-PALN 的任务完成率平均提高了 64.12%、46.48%、16.55% 和 14.03%，并且每次在线调度决策耗时不到 10 秒，证明了其在动态灾后环境中的有效性。 显示英文摘要

摘要： Natural disasters have caused significant losses to human society, and the timely and efficient acquisition of post-disaster environmental information is crucial for the effective implementation of rescue operations. Due to the complexity of post-disaster environments, existing sensing technologies face challenges such as weak environmental adaptability, insufficient specialized sensing capabilities, and limited practicality of sensing solutions. This paper explores the heterogeneous multi-agent online autonomous collaborative scheduling algorithm HoAs-PALN, aimed at achieving efficient collection of post-disaster environmental information. HoAs-PALN is realized through adaptive dimensionality reduction in the matching process and local Nash equilibrium game, facilitating autonomous collaboration among time-dependent UAVs, workers and vehicles to enhance sensing scheduling. (1) In terms of adaptive dimensionality reduction during the matching process, HoAs-PALN significantly reduces scheduling decision time by transforming a five-dimensional matching process into two categories of three-dimensional matching processes; (2) Regarding the local Nash equilibrium game, HoAs-PALN combines the softmax function to optimize behavior selection probabilities and introduces a local Nash equilibrium determination mechanism to ensure scheduling decision performance. Finally, we conducted detailed experiments based on extensive real-world and simulated data. Compared with the baselines (GREEDY, K-WTA, MADL and MARL), HoAs-PALN improves task completion rates by 64.12%, 46.48%, 16.55%, and 14.03% on average, respectively, while each online scheduling decision takes less than 10 seconds, demonstrating its effectiveness in dynamic post-disaster environments.