UAV path optimization algorithm and simulation case analysis

Abstract

Drones are much accounted for, with the advantage of low cost, flexibility, and easy-controlled. As its application fields become more extensive, from human production to scientific research, the requirements for UAV path planning are getting higher. Reasonable path planning minimizes resource consumption and is worthy of continuous exploration. This article lists one algorithm each among heuristic algorithms, graph search algorithms, and traditional algorithms: ant colony optimization (ACO), A* algorithm, and artificial potential field. After a discussion of the process and steps, a simulation of three algorithms and a comparison has been made. ACO focuses more on path planning with disordered points, while the A* algorithm and artificial potential field do well in avoiding obstacles when traveling with a given starting point and destination. More strengths and weaknesses are explained later. Path planning still needs more research to adapt to current needs, such as emergencies, dynamic changes in actual situations, consideration of difficult terrain and climate, and weakness of signal connection.