安全飛行：斥力場を用いた粒子群最適化によるドローン群制御

原文アブストラクト

Particle Swarm Optimisation (PSO) is a powerful optimisation algorithm that can be used to locate global maxima in a search space. Recent interest in swarms of Micro Aerial Vehicles (MAVs) begs the question as to whether PSO can be used as a method to enable real robotic swarms to locate a target goal point. However, the original PSO algorithm does not take into account collisions between particles during search. In this paper we propose a novel algorithm called Force Field Particle Swarm Optimisation (FFPSO) that designates repellent force fields to particles such that these fields provide an additional velocity component into the original PSO equations. We compare the performance of FFPSO with PSO and show that it has the ability to reduce the number of particle collisions during search to 0 whilst also being able to locate a target of interest in a similar amount of time. The scalability of the algorithm is also demonstrated via a set of experiments that considers how the number of crashes and the time taken to find the goal varies according to swarm size. Finally, we demonstrate the algorithms applicability on a swarm of real MAVs.