无人机巡检时能实时应对风险，兼顾安全、速度与能耗。
MOAR Planner: Multi-Objective and Adaptive Risk-Aware Path Planning for Infrastructure Inspection with a UAV
- 用风险感知成本函数融合障碍物与动态风险，自适应调整路径。
- 仿真与实飞测试显示，90%的评估指标表现优于主流算法。
- 适合高危场景下需要自主决策的无人机巡检任务。
无人机自主巡检仍具挑战性，需在靠近障碍物时有效导航，并考虑天气、通信可靠性及电池续航等动态风险因素。本文提出MOAR路径规划器，可应对任务中不断变化的风险。该方法支持实时轨迹调整，同时优化安全性、时间和能量消耗。规划器采用风险感知成本函数，整合预计算的成本图、新的损伤与插入成本概念，以及自适应速度规划框架。最优路径通过离散状态与动作空间的图搜索获得。在仿真和真实飞行测试中验证，结果表明该方法能生成覆盖主流算法约90%评估范围的实时轨迹。所提框架提升了无人机在关键任务中的自主性与可靠性。
原文摘要 · Abstract (English)
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.
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