Fly Safe: Aerial Swarm Robotics using Force Field Particle Swarm Optimisation

Abstract

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.

Deep Learning Based Semantic Segmentation Technique for Anomaly Detection on Metal Surfaces Using High Calibre U- Shaped Network

“…This is a unique method in identifying the cracks on concrete bridges. Another bridge crack detection system is discussed in the study of Parker et al [32]. It uses a coordinated aerial swarm system.…”

E2Coop: Energy Efficient and Cooperative Obstacle Detection and Avoidance for UAV Swarms

“…Some methods based on PSO incorporate APF in ODA for swarms, in order to provide environmental information regarding obstacles and the destination for the fitness function in PSO. For example, a force field PSO is proposed, called FFPSO, to consider the potential field in the fitness function of PSO (Parker, Butterworth, and Luo 2019). A potential field based PSO is proposed, called PPSO, which adds a new smoothing field in order to ensure smoothness of the trajectories (Cai and Yang 2014).…”

“…• F F P SO [29]: A new term repelling particles from one another is introduced to the velocity update equation in PSO. The fitness function of a particle is simply the negative of the distance between the particle and its destination.…”

“…Spatial Domain: the advantage of E 2 CoP re in spatial domain is trajectory-based PSO. Unlike conventional waypoint-based PSO [29], [30] where particles in PSO correspond to waypoints of a trajectory, the particles in trajectory-based PSO correspond to trajectories. Trajectory-based PSO generates smoother trajectories but has larger search space dimensionalities.…”

“…Extensive experiments are conducted to evaluate E 2 CoP re against state-of-the-art collision avoidance schemes [29], [30], [31]. E 2 CoP re can save energy up to 80% in the best case and 20% in the worst case compared to state-of-the-art schemes, showing the advantage of the novel fitness function of E 2 CoP re on energy saving.…”