ARENA：面向无人机多目标三维基础设施巡检的自适应风险感知与节能导航
ARENA: Adaptive Risk-aware and Energy-efficient NAvigation for Multi-Objective 3D Infrastructure Inspection with a UAV
AI总结：
本文提出ARENA方法，一种用于无人机三维基础设施巡检的自适应风险感知与节能多目标路径规划方法，通过4D NURBS和遗传算法生成Pareto前沿，实现在线轨迹优化，仿真与实测验证了其覆盖95%以上单目标范围及14%功率估计误差的性能。
摘要：
自主机器人巡检任务需要在靠近昂贵障碍物导航时平衡多个相互冲突的目标。当前的多目标路径规划（MOPP）方法难以适应诸如定位误差、天气、电池状态和通信问题等不断演变的风险。本文提出了一种用于复杂三维环境中无人机的自适应风险感知与节能导航（ARENA）MOPP方法。我们的方法通过使用4D NURBS表示和基于遗传的算法生成Pareto前沿，优化安全性、时间和能量，实现在线轨迹自适应。一种新颖的风险感知投票算法确保了自适应性。仿真和实际测试表明，该规划器能够生成覆盖单目标基准所定义范围95%或更多的多样化优化轨迹，并且其功率消耗估计的平均误差为全功率范围的14%。ARENA框架增强了无人机在关键、不断演变的三维任务中的自主性和可靠性。
英文摘要：
Autonomous robotic inspection missions require balancing multiple conflicting objectives while navigating near costly obstacles. Current multi-objective path planning (MOPP) methods struggle to adapt to evolving risks like localization errors, weather, battery state, and communication issues. This letter presents an Adaptive Risk-aware and Energy-efficient NAvigation (ARENA) MOPP approach for UAVs in complex 3D environments. Our method enables online trajectory adaptation by optimizing safety, time, and energy using 4D NURBS representation and a genetic-based algorithm to generate the Pareto front. A novel risk-aware voting algorithm ensures adaptivity. Simulations and real-world tests demonstrate the planner's ability to produce diverse, optimized trajectories covering 95% or more of the range defined by single-objective benchmarks and its ability to estimate power consumption with a mean error representing 14% of the full power range. The ARENA framework enhances UAV autonomy and reliability in critical, evolving 3D missions.
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