Path Planning for Multiple Targets Interception by the Swarm of UAVs based on Swarm Intelligence Algorithms: A Review

Abstract

The dramatic increase in the capabilities and availability of autonomous ground and aerial tools introduces safety and security challenges, particularly in protecting strategic infrastructures. In this context, the interception of multiple mobile threats, aiming to invade restricted spaces of such infrastructures is an important topic. This paper focuses on the problem of path planning for intercepting multiple aerial targets by a swarm of UAVs. 3D path planning for interception of moving targets is a challenging task, in particular when the interception is performed by a swarm of UAVs, as there are multiple kinematic and dynamic constraints. The aim is first to allocate targets to the individual UAVs (task assignment) and to construct a 3D path for each one. Many algorithms have been recognized as noble schemes for solving this kind of problems based on Swarm Intelligence (SI), many of them are based on biological systems such as particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), bat-inspired algorithm (BA), etc. The paper presents a comprehensive review of SI algorithms centered on the problems related to 3D path planning for target interception by a swarm of UAVs. It also focuses on the improvement of existing SI algorithms for better path optimization. A comprehensive investigation for each algorithm is presented by analyzing its merits and demerits in the context of target interception. This broad review is an outline for scholars and professionals in the field of the swarm of UAVs.