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

TL;DR

This paper introduces Shrinking POMCP, a novel real-time path planning framework for UAV search and rescue, improving efficiency by optimizing decision-making under limited visibility and time constraints in urban environments.

Contribution

The paper presents a new Shrinking POMCP algorithm that effectively addresses time constraints in UAV search and rescue by integrating belief maintenance and obstacle avoidance in simulators.

Findings

Significant reduction in search times compared to existing methods.

Effective performance across various belief types in simulated environments.

Demonstrated scalability and robustness in urban search scenarios.

Abstract

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.