MOAR规划器：用于无人机基础设施检查的多目标与自适应风险感知路径规划
MOAR Planner: Multi-Objective and Adaptive Risk-Aware Path Planning for Infrastructure Inspection with a UAV
AI总结：
提出MOAR路径规划器，通过风险感知成本函数和自适应速度规划，在接近障碍物时实时优化安全、时间和能量，实现无人机自主导航。
摘要：
无人机巡检的自主导航问题仍然具有挑战性，因为它需要在靠近障碍物的情况下有效导航，同时考虑天气条件、通信可靠性和电池自主性等动态风险因素。本文介绍了MOAR路径规划器，它解决了任务中风险演变的复杂性。它提供实时轨迹调整，同时优化安全性、时间和能量。该规划器采用风险感知成本函数，集成了预计算成本图、新的损伤和插入成本概念以及自适应速度规划框架。由此，使用状态和动作空间的离散表示在图中搜索最优路径。通过仿真和实际飞行测试评估该方法。结果表明，该方法能够生成覆盖广泛评估指标的实时轨迹：约为流行算法覆盖范围的90%。所提出的框架有助于无人机在关键任务中更自主、更可靠地导航。
英文摘要：
The problem of autonomous navigation for UAV inspection remains challenging as it requires effectively navigating in close proximity to obstacles, while accounting for dynamic risk factors such as weather conditions, communication reliability, and battery autonomy. This paper introduces the MOAR path planner which addresses the complexities of evolving risks during missions. It offers real-time trajectory adaptation while concurrently optimizing safety, time, and energy. The planner employs a risk-aware cost function that integrates pre-computed cost maps, the new concepts of damage and insertion costs, and an adaptive speed planning framework. With that, the optimal path is searched in a graph using a discrete representation of the state and action spaces. The method is evaluated through simulations and real-world flight tests. The results show the capability to generate real-time trajectories spanning a broad range of evaluation metrics: around 90% of the range occupied by popular algorithms. The proposed framework contributes by enabling UAVs to navigate more autonomously and reliably in critical missions.
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。
