智能调度无人机、人员与车辆，快速获取灾后环境信息。
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response
- 通过降维匹配与局部纳什博弈实现多主体自主协作
- 任务完成率提升超64%，单次调度决策低于10秒
- 适合应急救援中复杂动态环境下的实时感知需求
自然灾害给社会带来重大损失，及时高效获取灾后环境信息对救援行动至关重要。由于灾后环境复杂，现有感知技术存在适应性差、专业感知能力不足及实用性有限等问题。本文提出异构多智能体在线自主协同调度算法 HoAs-PALN，旨在实现灾后环境信息的高效采集。该算法通过匹配过程中的自适应降维和局部纳什均衡博弈，促进时间依赖型无人机、工作人员与车辆的自主协作。首先，在匹配过程中采用自适应降维，将五维匹配转化为两类三维匹配，显著降低调度决策时间；其次，结合Softmax函数优化行为选择概率，并引入局部纳什均衡判定机制，保障调度性能。基于真实与模拟数据的实验表明，相较于基线方法（GREEDY、K-WTA、MADL 和 MARL），HoAs-PALN 平均任务完成率分别提升64.12%、46.48%、16.55% 和14.03%，且每次在线调度决策耗时均低于10秒，验证了其在动态灾后环境中的有效性。
原文摘要 · Abstract (English)
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.
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