Acceleration-Based PSO for Multi-UAV Source-Seeking

This paper presents a novel algorithm for a swarm of unmanned aerial vehicles to search for an unknown source. The proposed method is inspired by the well-known particle swarm optimization (PSO) algorithm and is called acceleration- based particle swarm optimization (APSO) to address the source- seeking problem with no a priori information. Unlike the conventional particle swarm optimization algorithm, where the particle velocity is updated based on the self-cognition and social- cognition information, here the update is performed on the particle acceleration. A theoretical analysis is provided, showing the stability and convergence of the proposed acceleration-based particle swarm optimization algorithm. Conditions on the parameters of the resulting third-order update equations are obtained using Jury's stability test. High-fidelity simulations performed in CoppeliaSim, show the improved performance of the proposed acceleration-based particle swarm optimization algorithm for searching an unknown source when compared with the state-of- the-art particle swarm-based source-seeking algorithms. From the obtained results, it is observed that the proposed method performs better than the existing methods under scenarios like different inter unmanned aerial vehicle communication network topologies, varying numbers of unmanned aerial vehicles in the swarm, different sizes of search regions, restricted source movement, and in the presence of measurements noise.