无人机群通过拍卖机制自动重组，故障下仍保持93%搜救成功率。
Market-Based Replanning for Safety-Critical UAV Swarms in Search and Rescue Missions
- 用反向拍卖让无人机竞标任务，按距离加权分配
- 25%无人机失效时，任务重分配延迟低，成功率93%
- 适合高风险搜救场景的自主容错无人机集群
搜救任务中，可靠的自主无人机群需具备在个体退化情况下仍能持续运行的容错协同能力。本文提出智能重规划无人机群（IRDS），一种专为资源受限环境设计的分布式协同架构。该框架采用反向拍卖市场机制，各无人机根据距离加权成本函数竞标搜索区域服务权，并结合几何一致性协议进行目标验证。通过物理仿真（N=8架无人机，8×8网格）并注入随机故障进行评估。结果表明，当部分无人机失效时，集群可低延迟自动重新分配任务，总任务周期内维持93%的救援成功率。所提框架展现出一种鲁棒且经实证验证的空中机器人自愈协同方法。
原文摘要 · Abstract (English)
Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent degradation. This paper introduces the Intelligent Replanning Drone Swarm (IRDS), a distributed coordination architecture designed for resource-constrained environments. The proposed framework employs a Reverse-Auction market mechanism where agents bid to service search sectors based on a distance-weighted cost function, coupled with a geometric consensus protocol for target verification. We evaluate the approach through physics-based simulations (N=8 agents, 8x8 grid) subjected to stochastic fault injection. Results indicate that the swarm autonomously reallocates tasks from failed agents with low latency relative to the total mission duration, maintaining a mission success rate of 93% under 25% workforce degradation. The proposed framework demonstrates a robust, empirically tested method for self-healing aerial robotic coordination.
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。