Cooperative search and rescue using a scheduling algorithm

Abstract

Thesis (MEng)--Stellenbosch University, 2025.Buys, S. 2025. Cooperative search and rescue using a scheduling algorithm. Unpublished masters thesis. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/items/e8c0f195-7216-4f5e-9d5b-c49a663b7c39Autonomous search and rescue (SAR) operations demand innovative solutions that can efficiently coordinate multiple search agents in complex and dynamic environments. The autonomous SAR problem is often modelled as a coverage path planning (CPP) problem, as both aim to systematically explore all points in a given area while operating under the critical time pressures inherent in SAR scenarios. Many existing methods divide the search environment into distinct sub-areas to enhance search efficiency for multiple search agents. However, the strict constraints imposed to achieve optimal performance in these settings often limit the algorithm’s adaptability for real-world scenarios. Two typical real-world scenarios include limited fuel availability and partially mapped or fully unmapped environments. Limited fuel availability often necessitates reliance on a single refuelling station, making such divisions impractical. Additionally, partially mapped environments complicate the process of optimally dividing the search area. This thesis presents a novel framework for search and rescue missions that integrates a scheduler and a multi-agent path planner. The scheduler allocates exploration cells, or frontiers, to agents using a greedy-heuristic strategy, while the multi-agent path planner ensures collision-free trajectories, enabling effective collaboration between agents. The approach was further extended to operate in unmapped environments, where agents adapt to unknown terrain during execution, and in scenarios constrained by limited fuel, incorporating refuelling strategies to maintain mission continuity. The proposed system was tested in simulations of varying complexity, including real-world-inspired scenarios, to evaluate metrics such as success rate, computational scalability, and the efficiency of the planned trajectories. The results demonstrate the system’s ability to outperform baseline approaches, achieving higher success rates for a wide range of environmental types. These findings underline the framework’s potential as a practical tool for autonomous search and rescue missions, advancing the capabilities of UAV systems in critical applications.Master