Acceleration based PSO for Multi-UAV Source-Seeking
This work addresses source-seeking for UAV swarms, an incremental improvement over existing PSO-based methods.
The paper tackles the problem of enabling a swarm of UAVs to search for an unknown source without prior information by proposing an acceleration-based particle swarm optimization (APSO) algorithm, which shows improved performance in simulations compared to state-of-the-art methods under various scenarios like different network topologies and noise conditions.
This paper presents a novel algorithm for a swarm of unmanned aerial vehicles (UAVs) to search for an unknown source. The proposed method is inspired by the well-known 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 PSO 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 APSO algorithm. Conditions on the parameters of the resulting third order update equations are obtained using Jurys stability test. High fidelity simulations performed in CoppeliaSim, shows the improved performance of the proposed APSO 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-UAV communication network topologies, varying number of UAVs in the swarm, different sizes of search region, restricted source movement and in the presence of measurements noise.