TY - GEN
T1 - Task Planning Algorithm for Heterogeneous UAVs Based on Improved Density Clustering and LKH-Neural Network Binding
AU - Shi, Hang
AU - Fang, Wenqi
AU - Chang, Xiang
AU - Yao, Borui
AU - Wang, Zhen
AU - Huo, Mingying
AU - Qi, Naiming
N1 - Publisher Copyright:
© Press of Acta Aeronautica et Astronautica Sinica 2026.
PY - 2026
Y1 - 2026
N2 - Targeting task planning for heterogeneous multi-UAV swarms, a collaborative method integrating improved density clustering with LKH-neural network optimization is proposed. To overcome limitations like high computational complexity and low path planning efficiency in multi-region scanning tasks, key innovations are introduced. Firstly, an improved density clustering algorithm balances task region clustering based on spatial distribution and attributes. Secondly, dynamic task cluster allocation balances workloads across heterogeneous UAVs. Finally, a neural network predicts critical LKH parameters to optimize paths and minimize flight time. Simulations show that, compared to K-means + LKH and genetic algorithms, the proposed method offers advantages in task completion time or efficiency as regions and UAVs scale, providing an effective solution for intelligent UAV swarm planning.
AB - Targeting task planning for heterogeneous multi-UAV swarms, a collaborative method integrating improved density clustering with LKH-neural network optimization is proposed. To overcome limitations like high computational complexity and low path planning efficiency in multi-region scanning tasks, key innovations are introduced. Firstly, an improved density clustering algorithm balances task region clustering based on spatial distribution and attributes. Secondly, dynamic task cluster allocation balances workloads across heterogeneous UAVs. Finally, a neural network predicts critical LKH parameters to optimize paths and minimize flight time. Simulations show that, compared to K-means + LKH and genetic algorithms, the proposed method offers advantages in task completion time or efficiency as regions and UAVs scale, providing an effective solution for intelligent UAV swarm planning.
KW - Density clustering
KW - Lin-Kernighan-Helsgaun algorithm
KW - Multi-heterogeneous UAVs
KW - Neural network
KW - Task planning
UR - https://www.scopus.com/pages/publications/105021809539
U2 - 10.1007/978-981-95-3010-6_12
DO - 10.1007/978-981-95-3010-6_12
M3 - 会议稿件
AN - SCOPUS:105021809539
SN - 9789819530090
T3 - Lecture Notes in Mechanical Engineering
SP - 177
EP - 194
BT - Proceedings of the 2nd Aerospace Frontiers Conference (AFC 2025) - Volume III
PB - Springer Science and Business Media Deutschland GmbH
T2 - 2nd Aerospace Frontiers Conference, AFC 2025
Y2 - 11 April 2025 through 14 April 2025
ER -