基于市场重规划的搜救任务中安全关键无人机群
Market-Based Replanning for Safety-Critical UAV Swarms in Search and Rescue Missions
AI总结：
提出一种分布式协调架构IRDS，通过反向拍卖市场机制和几何共识协议，在无人机故障下自主重分配任务，在25%退化下保持93%任务成功率。
摘要：
搜救任务中可靠自主无人机群需要能够容忍代理退化并维持操作的容错协调。本文介绍了智能重规划无人机群（IRDS），一种为资源受限环境设计的分布式协调架构。所提出的框架采用反向拍卖市场机制，其中代理基于距离加权成本函数竞标服务搜索区域，并结合几何共识协议进行目标验证。我们通过物理仿真（N=8个代理，8x8网格）评估该方法，并施加随机故障注入。结果表明，无人机群能够以相对于总任务持续时间较低的延迟自主重新分配来自故障代理的任务，在25%劳动力退化下保持93%的任务成功率。所提出的框架展示了一种稳健的、经过实证测试的空中机器人自愈协调方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
