提出一套实时避障算法，让无人机和无人车在森林火灾中自主安全导航。
Real-Time Obstacle Avoidance Algorithms for Unmanned Aerial and Ground Vehicles
- 分阶段设计2D融合导航与3D反应式避障策略，适应复杂环境。
- 在森林火灾仿真中实现无碰撞飞行，提升救援响应速度。
- 首次统一控制无人机与地面车，适用于灾害场景协同救援。
移动机器人在汽车、农业和救援等领域的应用日益广泛，反映出机器人与自主技术的进步。尽管无人机（UAV）研究多集中于视觉SLAM、传感器融合与路径规划，但在灾难现场如森林火灾中的自主导航仍研究不足。本报告针对复杂三维环境下的实时安全飞行需求，开发了无人机在森林火灾等危险场景中的自主导航方法。研究分为三个阶段：首先探索适用于移动机器人的2D融合导航策略，支持动态环境下的安全移动；其次提出一种新型3D反应式导航策略，用于森林火灾模拟中无碰撞飞行；最后提出统一控制框架，实现无人机与无人地面车辆（UGVs）在森林救援任务中的协同作业。每阶段均构建控制模型，并通过数学分析与仿真验证其有效性。研究成果对提升灾害救援效率与安全性具有实用价值与学术意义。
原文摘要 · Abstract (English)
The growing use of mobile robots in sectors such as automotive, agriculture, and rescue operations reflects progress in robotics and autonomy. In unmanned aerial vehicles (UAVs), most research emphasizes visual SLAM, sensor fusion, and path planning. However, applying UAVs to search and rescue missions in disaster zones remains underexplored, especially for autonomous navigation. This report develops methods for real-time and secure UAV maneuvering in complex 3D environments, crucial during forest fires. Building upon past research, it focuses on designing navigation algorithms for unfamiliar and hazardous environments, aiming to improve rescue efficiency and safety through UAV-based early warning and rapid response. The work unfolds in phases. First, a 2D fusion navigation strategy is explored, initially for mobile robots, enabling safe movement in dynamic settings. This sets the stage for advanced features such as adaptive obstacle handling and decision-making enhancements. Next, a novel 3D reactive navigation strategy is introduced for collision-free movement in forest fire simulations, addressing the unique challenges of UAV operations in such scenarios. Finally, the report proposes a unified control approach that integrates UAVs and unmanned ground vehicles (UGVs) for coordinated rescue missions in forest environments. Each phase presents challenges, proposes control models, and validates them with mathematical and simulation-based evidence. The study offers practical value and academic insights for improving the role of UAVs in natural disaster rescue operations.
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