Shrinking POMCP: A Framework for Real-Time UAV Search and Rescue

--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 UA V-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 neu-rosymbolic 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 UA V-assisted search and rescue operations. Search and rescue (SAR) operations are critical, time-sensitive missions conducted in challenging environments like neighborhoods, wilderness [1], or maritime settings [2]. These resource-intensive operations require efficient path planning and optimal routing [3]. In recent years, Unmanned Aerial V ehicles (UA Vs) have become valuable SAR assets, offering advantages such as rapid deployment, extended flight times, and access to hard-to-reach areas. Equipped with sensors and cameras, UA Vs can detect heat signatures, identify objects, and provide real-time aerial imagery to search teams [4]. However, the use of UA Vs in SAR operations presents unique challenges, particularly in path planning and decision-making under uncertainty. Factors such as limited battery life, changing weather conditions, and incomplete information about the search area complicate the task of efficiently coordinating UA V movements to maximize the probability of locating targets [3].