TY - GEN
T1 - Efficient and Non-crossing Multi-UAV Task Assignment via a Polar-Guided Genetic Algorithm
AU - Shi, Jixuan
AU - Xia, Kewei
AU - Zou, Yao
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2027.
PY - 2027
Y1 - 2027
N2 - The path crossing problem in multi-UAV task assignment is a critical challenge that significantly increases the complexity of subsequent path planning. To address this issue, this paper proposes an improved genetic algorithm guided by polar coordinates. More specifically, a globally guided lookup table is constructed from depot-centric polar angles to eliminate path crossings throughout the optimization process. This approach also generates high-quality initial solutions without requiring dedicated initialization strategies, where pre-iteration computational overhead is reduced. Furthermore, the joint optimization of workload balancing and task point sequencing is formulated as a Pareto frontier problem, by which computational complexity is substantially decreased. Finally, numerical simulations are performed to validate and assess the effectiveness of the proposed algorithm.
AB - The path crossing problem in multi-UAV task assignment is a critical challenge that significantly increases the complexity of subsequent path planning. To address this issue, this paper proposes an improved genetic algorithm guided by polar coordinates. More specifically, a globally guided lookup table is constructed from depot-centric polar angles to eliminate path crossings throughout the optimization process. This approach also generates high-quality initial solutions without requiring dedicated initialization strategies, where pre-iteration computational overhead is reduced. Furthermore, the joint optimization of workload balancing and task point sequencing is formulated as a Pareto frontier problem, by which computational complexity is substantially decreased. Finally, numerical simulations are performed to validate and assess the effectiveness of the proposed algorithm.
KW - Genetic algorithm
KW - Multi-traveling salesman problem
KW - Multi-UAV system task assignment
KW - Path crossing
UR - https://www.scopus.com/pages/publications/105050705297
U2 - 10.1007/978-981-92-4392-1_17
DO - 10.1007/978-981-92-4392-1_17
M3 - Conference contribution
AN - SCOPUS:105050705297
SN - 9789819243914
T3 - Lecture Notes in Electrical Engineering
SP - 196
EP - 208
BT - Proceedings of 2025 9th Chinese Conference on Swarm Intelligence and Cooperative Control - Swarm Decision and Planning Technologies
A2 - Li, Xiaoduo
A2 - Li, Yichen
A2 - Feng, Zhi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2025
Y2 - 31 October 2025 through 3 November 2025
ER -