Affiliations: [a] UAV Research Institute of Nanjing University of Aeronautics and Astronautics, Middle and Small Size UAV Advanced Technique Key Laboratory of Ministry of Industry and Information Technology, Nanjing, Jiangsu, China
| [b] Science and Technology on Aircraft Control Laboratory, FACRI, Xi’an, Shanxi, China
| [c] Department of Information Technology, Al-Zahra College for Women, Muscat, Oman
Correspondence:
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Corresponding author. Rong Liu, UAV Research Institute of Nanjing University of Aeronautics and Astronautics, Middle and Small Size UAV Advanced Technique Key Laboratory of Ministry of Industry and Information Technology, Nanjing, Jiangsu, China. E-mail: [email protected].
Abstract: The UAV system has evolved in the direction of intelligence and autonomy. Mission planning is an important part of autonomous drone control. The issue of route planning and task assignment in drone mission planning is studied. For the drone path planning problem in three-dimensional static threat environment, two improved ant colony algorithms are proposed, and these prior knowledges are constructed as multiple heuristic information of ants, guiding the ant’s path search, and verifying the global convergence of the algorithm. The fuzzy inference system is used to dynamically adjust the parameters of the RRT algorithm according to the real-time information of the task environment and the growth status of the RRT random tree. The experimental results show that the two improved algorithms can obtain better planning results than the single artificial potential field method and ant colony algorithm, effectively shorten the route planning time, improve the planning accuracy, and obtain the optimal flight path.
