无人机巡检中自适应调整路径，兼顾安全、省电与效率
ARENA: Adaptive Risk-aware and Energy-efficient NAvigation for Multi-Objective 3D Infrastructure Inspection with a UAV
- 用4D NURBS和遗传算法生成多目标最优路径
- 能覆盖单目标基准95%以上可行轨迹，功率估算误差仅占全范围14%
- 适合高风险复杂3D环境下的智能巡检任务
自主机器人巡检任务需在复杂3D环境中平衡多个冲突目标并避开高成本障碍。现有多目标路径规划（MOPP）方法难以应对定位误差、天气变化、电池状态及通信问题等动态风险。本文提出一种自适应风险感知且节能的无人机路径规划方法（ARENA）。该方法通过4D NURBS表示与基于遗传算法的优化，实时生成安全、省时、省电的帕累托前沿路径，并引入新型风险感知投票机制实现在线自适应。仿真与真实测试表明，该规划器可生成覆盖单目标基准95%以上可行范围的多样化轨迹，功率消耗估算平均误差不超过全功率范围的14%。所提框架显著提升了无人机在关键、动态3D任务中的自主性与可靠性。
原文摘要 · Abstract (English)
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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