提出新型实时无人机搜救路径规划方法，提升复杂环境搜索效率。
Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue
- 基于部分可观马尔可夫决策过程，设计可缩减的POMCP算法应对时间限制。
- 在3D与2D仿真中均显著缩短搜索时间，优于现有方法。
- 适合需要快速响应的无人机城市搜救任务，尤其关注实时性与避障能力。
无人机在搜救任务中的路径优化面临视野受限、时间紧迫及城市环境中信息获取复杂等挑战。本文提出一种针对街区区域的无人机搜救路径规划综合方法，结合3D AirSim-ROS2仿真环境与2D仿真平台。将路径规划问题建模为部分可观马尔可夫决策过程（POMDP），并提出新颖的“Shrinking POMCP”方法以应对时间约束。在AirSim环境中，集成概率世界模型用于信念维护，以及神经符号导航器实现障碍物规避；2D仿真采用功能等价的替代ROS2节点。通过2D仿真对比不同方法生成的轨迹，并在3D AirSim-ROS仿真中评估多种信念类型下的性能表现。实验结果表明，所提方法在两类仿真中均显著降低搜索时间，展现出提升无人机辅助搜救效率的巨大潜力。
原文摘要 · Abstract (English)
Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimize UAV-based search and rescue operations in neighborhood areas, utilizing both a 3D AirSim-ROS2 simulator and a 2D simulator. The path planning problem is formulated as a partially observable Markov decision process (POMDP), and we propose a novel ``Shrinking POMCP'' approach to address time constraints. In the AirSim environment, we integrate our approach with a probabilistic world model for belief maintenance and a neurosymbolic navigator for obstacle avoidance. The 2D simulator employs surrogate ROS2 nodes with equivalent functionality. We compare trajectories generated by different approaches in the 2D simulator and evaluate performance across various belief types in the 3D AirSim-ROS simulator. Experimental results from both simulators demonstrate that our proposed shrinking POMCP solution achieves significant improvements in search times compared to alternative methods, showcasing its potential for enhancing the efficiency of UAV-assisted search and rescue operations.
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