(searched for: doi:10.1109/cgncc.2016.7828772)
Wireless Communications and Mobile Computing, Volume 2021, pp 1-13; https://doi.org/10.1155/2021/4312592
Unmanned aerial vehicles (UAVs) are gradually used in logistics transportation. They are forbidden to fly in some airspace. To ensure the safety of UAVs, reasonable path planning and design is one of the key factors. Aiming at the problem of how to improve the success rate of unmanned aerial vehicle (UAV) maneuver penetration, a method of UAV penetration path planning and design is proposed. Ant colony algorithm has strong path planning ability in biological swarm intelligence algorithm. Based on the modeling of UAV planning and threat factors, improved ant colony algorithm is used for UAV penetration path planning and design. It is proposed that the path with the best pheromone content is used as the planning path. Some principles are given for using ant colony algorithm in UAV penetration path planning. By introducing heuristic information into the improved ant colony algorithm, the convergence is completed faster under the same number of iteratives. Compared with classical methods, the total steps reduced by 56% with 50 ant numbers and 200 iterations. 62% fewer steps to complete the first iteration. It is found that the optimal trajectory planned by the improved ant colony algorithm is smoother and the shortest path satisfying the constraints.
Published: 21 May 2021
2021 11th International Conference on Information Science and Technology (ICIST) pp 383-393; https://doi.org/10.1109/icist52614.2021.9440608
Conference: 2021 11th International Conference on Information Science and Technology (ICIST), 2021-5-21 - 2021-5-23, Chengdu, China
Collaborative search is one of the key application fields of UAV swarm, Efficient and accurate algorithm is very important to complete the task of UAV swarm search, and the dynamic and real-time uncertainty of unmanned aerial vehicle swarm search task makes the problem very difficult. Therefore, in the past few years, a large number of scholars have shown strong interest in the problem of UAV swarm search task. With the rapid development of computer technology and Intelligent optimization algorithm, many Intelligent optimization algorithm have been proposed to solve this problem. However, the research on cooperative control and search algorithm is still not comprehensive, and there is a lack of induction and summary of recent research results. The purpose of this paper is to introduce the mathematical model of the search task and give a comprehensive review of the intelligence algorithms used in the swarm search task in recent years and their improvement. In addition, the results and efficiency of each algorithm to solve UAV search tasks are compared, and the advantages and disadvantages of different swarm intelligence algorithms applied to UAV swarm search tasks are summarized and summarized, so as to provide useful reference for UAV swarm to complete search tasks in the future.
Chinese Journal of Aeronautics, Volume 34, pp 187-204; https://doi.org/10.1016/j.cja.2020.12.027
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Published: 1 June 2019
2019 International Conference on Unmanned Aircraft Systems (ICUAS) pp 787-793; https://doi.org/10.1109/icuas.2019.8797815
Conference: 2019 International Conference on Unmanned Aircraft Systems (ICUAS), 2019-6-11 - 2019-6-14, Atlanta, United States
Since unmanned aerial vehicle (UAV) for industries are operated in complex low altitude environments, planning feasible paths is a necessary feature to achieve mission goals. D* Lite is applicable for industrial complex that uncertainties exist. This paper focuses on 3D path planning for quadcopter UAV based on D* Lite. Simulation results show that the algorithm can be applied in cluttered static and dynamic environments including unknown obstacles. In addition, when some waypoints exist, the proposed algorithm is able to optimize the global path by determining visit order. Therefore, this study is expected to contribute to increase the application of UAVs in industrial fields.