无人机群协同巡检灾场，智能避障还优化覆盖路径。
Optimized Area Coverage in Disaster Response Utilizing Autonomous UAV Swarm Formations
- 用局部欧氏符号距离场实现避障与队形保持
- 结合旅行商问题优化重点区域覆盖，提升响应效率
- 支持不同规模机群仿真，兼顾安全与覆盖最大化
本文提出一种用于火灾等灾害场景的无人机群系统，通过多机分布式传感器延长续航并提升数据可用性，降低因碰撞导致任务失败的风险。为增强安全性，引入基于局部欧氏符号距离场（ESDF）的自主导航框架，在保持机群队形的同时最小化路径偏移。同时，采用改进的旅行商问题（TSP）模型，根据环境特性与关键基础设施预设权重，优先覆盖重点区域。系统在多种规模机群的仿真中验证，有效实现高覆盖率与无碰撞飞行。
原文摘要 · Abstract (English)
This paper presents a UAV swarm system designed to assist first responders in disaster scenarios like wildfires. By distributing sensors across multiple agents, the system extends flight duration and enhances data availability, reducing the risk of mission failure due to collisions. To mitigate this risk further, we introduce an autonomous navigation framework that utilizes a local Euclidean Signed Distance Field (ESDF) map for obstacle avoidance while maintaining swarm formation with minimal path deviation. Additionally, we incorporate a Traveling Salesman Problem (TSP) variant to optimize area coverage, prioritizing Points of Interest (POIs) based on preassigned values derived from environmental behavior and critical infrastructure. The proposed system is validated through simulations with varying swarm sizes, demonstrating its ability to maximize coverage while ensuring collision avoidance between UAVs and obstacles.
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