Unmanned aerial vehicle path planning problem by adjusted elephant herding optimization

Unmanned aerial vehicle path planning is a high dimensional NP-hard problem. It is related to optimizing the flight route subject to various constraints inside the battlefield environment. Since the number of control points is large the traditional methods could not produce acceptable results when tackling this problem. Elephant herding optimization algorithm is one of the recent swarm intelligence algorithms which has not been sufficiently researched. In this paper we have adjusted the elephant herding optimization algorithm for the unmanned aerial vehicle path planning problem. We tested our approach using parameters of the battlefield environments from the literature and the comparative analysis has shown that our adjusted elephant herding optimization algorithm outperformed other approaches from the literature.

I. Introduction

The unmanned aerial vehicles (UAV) or drones are aircrafts which do not require a human pilot on board but are remotely piloted or self-piloted and can carry various pieces of equipment such as cameras, sensors, communications equipment, weapons. Compared with the general planes, they have the advantage of low-cost, high-security, high survival ability, good maneuvering performance. The path planning for the UAV is one of the most important problems and it presents a large-scale multi-constrained optimization problem. The UAV path planning requires calculation of the sub-optimal route between the initial location and the desired destination with avoidance of the hazardous areas, fuel consumption minimization and consideration of other constraint conditions.