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.

翻译：可靠的自主无人机群在执行搜索救援（SAR）任务时，需要具备容错协调能力，以在个体性能退化的情况下维持任务执行。本文提出了一种智能重规划无人机群（IRDS）分布式协调架构，专为资源受限环境设计。该框架采用反向拍卖市场机制，各智能体基于距离加权成本函数竞标搜索区域服务权，并配以几何共识协议进行目标验证。我们通过物理仿真（8个智能体，8×8网格）并注入随机故障来评估该方法。结果表明，无人机群能以相对任务总时长较低的延迟自主重分配来自失效智能体的任务，在25%劳动力退化情况下仍保持93%的任务成功率。该框架为自愈式空中机器人协调提供了一种经过实证检验的稳健方法。