Cooperative path planner for UAVs using ACO algorithm with Gaussian distribution functions

• Published in: 2009 IEEE International Symposium on Circuits and Systems (ISCAS) pp. 173 - 176
• Main Authors: Chi-Tsun Cheng, Fallahi, K., Leung, H., Tse, C.K.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2009
• Subjects: ACO; Ant colony optimization; Automotive engineering; B-spline; Coverage; Gaussian distribution; Large-scale systems; Military aircraft; Mobile robots; Path planning; Remotely operated vehicles; Resource Management; Spline; UAV; Unmanned aerial vehicles
• ISBN: 1424438276; 9781424438273
• ISSN: 0271-4302
• DOI: 10.1109/ISCAS.2009.5117713

Unmanned aerial vehicles (UAVs) are remote controlled or autonomous air vehicles. An UAV can be equipped with various types of sensors to perform life rescue missions or it can be armed with weapons to carry out stealthy attack missions. With the unmanned nature of UAVs, a mission can be taken in any hostile environment without risking the life of pilots. Among life rescue missions, the common objective is often defined as maximizing the total coverage area of the UAVs with the limited resources. When the number of UAVs increases, coordination among these UAVs becomes very complicated even for experienced pilots. In this paper, a cooperative path planner for UAVs is proposed. The path of each UAV is represented by a B-spline curve with a number of control points. The positions of these control points are optimized using an ant colony optimization algorithm (ACO) such that the total coverage of the UAVs is maximized.