TY - GEN
T1 - Multi-UAV 3D Path Planning in Simultaneous Attack
AU - Xiong, Chuyi
AU - Xin, Bin
AU - Guo, Miao
AU - Ding, Yulong
AU - Zhang, Hao
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/9
Y1 - 2020/10/9
N2 - As the key technology of UAV mission planning, path planning has a very important impact on the combat effectiveness of UAV. In order to destroy enemy units, multiple UAVs are often needed to attack the target at the same time. In the complex combat environment, the path planning of multiple UAVs is a very complex problem. On the basis of genetic algorithm, this paper designs a path planning algorithm to ensure that multiple UAVs arrive at the same time from different angles on the premise of satisfying environment constraints and the motion constraints of UAV. The algorithm uses the method of regionalization to initialize the path, designs a reasonable fitness function, and adds an adaptive disturbance operator to plan the path of each UAV. Finally, the length difference between the paths planned for each UAV is taken as the path evaluation factor. Some of the paths are replanned to achieve the goal of the same path length and simultaneous attack by UAV. The effectiveness of the algorithm is verified by experiments in typical combat scenarios, and the superiority of the adaptive disturbance operator in the optimization of the algorithm is verified by the contrast experiment.
AB - As the key technology of UAV mission planning, path planning has a very important impact on the combat effectiveness of UAV. In order to destroy enemy units, multiple UAVs are often needed to attack the target at the same time. In the complex combat environment, the path planning of multiple UAVs is a very complex problem. On the basis of genetic algorithm, this paper designs a path planning algorithm to ensure that multiple UAVs arrive at the same time from different angles on the premise of satisfying environment constraints and the motion constraints of UAV. The algorithm uses the method of regionalization to initialize the path, designs a reasonable fitness function, and adds an adaptive disturbance operator to plan the path of each UAV. Finally, the length difference between the paths planned for each UAV is taken as the path evaluation factor. Some of the paths are replanned to achieve the goal of the same path length and simultaneous attack by UAV. The effectiveness of the algorithm is verified by experiments in typical combat scenarios, and the superiority of the adaptive disturbance operator in the optimization of the algorithm is verified by the contrast experiment.
UR - https://www.scopus.com/pages/publications/85098070151
U2 - 10.1109/ICCA51439.2020.9264450
DO - 10.1109/ICCA51439.2020.9264450
M3 - Conference contribution
AN - SCOPUS:85098070151
T3 - IEEE International Conference on Control and Automation, ICCA
SP - 500
EP - 505
BT - 2020 IEEE 16th International Conference on Control and Automation, ICCA 2020
PB - IEEE Computer Society
T2 - 16th IEEE International Conference on Control and Automation, ICCA 2020
Y2 - 9 October 2020 through 11 October 2020
ER -