Research on AED delivery path planning for emergency drones based on the AsyLnCPSO algorithm with opposition-based learning mechanism

Out-of-hospital cardiac arrest (OHCA) is one of the main causes of sudden death in young people today. According to existing research, timely external defibrillation and cardiopulmonary resuscitation (CPR) are key measures to improve patient survival rates. Currently, the most common method of external defibrillation is the use of an Automated External Defibrillator (AED). However, AEDs are primarily distributed in public places such as shopping malls, hospitals, and schools, resulting in a low distribution density. Furthermore, due to urban traffic congestion and the impact of other complex terrains, traditional ground-based emergency resources, such as ambulances, often struggle to reach patients in a timely manner. In recent years, the unmanned aerial vehicle (UAV) industry has developed rapidly. Its high mobility and operational flexibility make it well-suited as a new platform for delivering AEDs. Path planning is crucial for UAVs as an AED delivery platform. Addressing existing path planning methods, this paper proposes an Asynchronous Learning Factor Particle Swarm Optimization algorithm with an Opposition-Based Learning mechanism (AsyLnCPSO), used as a planning method for unmanned aerial vehicle AED emergency delivery paths. To address the deficiency of single simulation environment samples in other studies, simulation experiments conducted in two representative environments - urban high-rise building clusters and complex mountainous terrain - show that compared to the standard Particle Swarm Optimization (PSO) algorithm, the proposed improved AsyLnCPSO method can reduce the planned path length by approximately 1.5% and lower the optimal fitness value by about 4.5%. The research indicates that compared to the traditional PSO algorithm, this method exhibits stronger global optimal solution search capability and is more prone to convergence, while also providing a theoretical reference for the delivery of air-ground collaborative medical emergency resources.