Multi-UAV Search and Rescue in Wilderness Using Smart Agent-Based Probability Models

TL;DR

This paper introduces a smart agent-based probability model combined with a distributed multi-UAV search strategy to improve wilderness search and rescue efficiency by predicting likely locations of lost persons using terrain features.

Contribution

It proposes a novel agent-based probability model and a distributed receding horizon search strategy that leverage terrain data for more effective UAV search missions in wilderness areas.

Findings

Enhanced search efficiency in simulated experiments

Effective prioritization of search areas based on probability models

Improved success rate over benchmark methods

Abstract

The application of Multiple Unmanned Aerial Vehicles (Multi-UAV) in Wilderness Search and Rescue (WiSAR) significantly enhances mission success due to their rapid coverage of search areas from high altitudes and their adaptability to complex terrains. This capability is particularly crucial because time is a critical factor in searching for a lost person in the wilderness; as time passes, survival rates decrease and the search area expands. The probability of success in such searches can be further improved if UAVs leverage terrain features to predict the lost person's position. In this paper, we aim to enhance search missions by proposing a smart agent-based probability model that combines Monte Carlo simulations with an agent strategy list, mimicking the behavior of a lost person in the wildness areas. Furthermore, we develop a distributed Multi-UAV receding horizon search strategy with dynamic partitioning, utilizing the generated probability density model as prior information to prioritize locations where the lost person is most likely to be found. Simulated search experiments across different terrains have been conducted to validate the search efficiency of the proposed methods compared to other benchmark methods.