TY - GEN
T1 - Heterogeneous Multi-UAV Multi-task Reallocation Problem Using Mixed-Integer Linear Programing
AU - Wang, Zekun
AU - Zhao, Jianxin
AU - Deng, Hongbin
AU - Xing, Cheng
AU - Gao, Tengfei
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose a method to formulate a heterogeneous multi-UAV multi-task redistribution problem with physical and strategic constraints, which can be solved in mixed integer linear programming (MILP). UAVs are classified in different types that capture their different capabilities, and each task has different complete probabilities for different types of UAVs. Given a time-space limit, certain UAV loads and an initial value of allocation, our goal is to assign these tasks to heterogeneous UAV swarm consisting of different types of UAVs. So we unified different task types in one objective function, and established constraints to different task types. Physical and objective constraints are established either. We find centroid of tasks and measure distance of centroid to task position so as to linearize constraints of voyage. Then we solve the MILP function to find optimal allocation results. The simulation results with different pair of UAV and task number give different optimal solutions, it shows the effectiveness of MILP formulation.
AB - In this paper, we propose a method to formulate a heterogeneous multi-UAV multi-task redistribution problem with physical and strategic constraints, which can be solved in mixed integer linear programming (MILP). UAVs are classified in different types that capture their different capabilities, and each task has different complete probabilities for different types of UAVs. Given a time-space limit, certain UAV loads and an initial value of allocation, our goal is to assign these tasks to heterogeneous UAV swarm consisting of different types of UAVs. So we unified different task types in one objective function, and established constraints to different task types. Physical and objective constraints are established either. We find centroid of tasks and measure distance of centroid to task position so as to linearize constraints of voyage. Then we solve the MILP function to find optimal allocation results. The simulation results with different pair of UAV and task number give different optimal solutions, it shows the effectiveness of MILP formulation.
KW - Heterogeneous system
KW - Mixed-integer linear programing
KW - Task allocation problem
UR - https://www.scopus.com/pages/publications/85130937609
U2 - 10.1007/978-981-16-9492-9_340
DO - 10.1007/978-981-16-9492-9_340
M3 - Conference contribution
AN - SCOPUS:85130937609
SN - 9789811694912
T3 - Lecture Notes in Electrical Engineering
SP - 3453
EP - 3459
BT - Proceedings of 2021 International Conference on Autonomous Unmanned Systems, ICAUS 2021
A2 - Wu, Meiping
A2 - Niu, Yifeng
A2 - Gu, Mancang
A2 - Cheng, Jin
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Autonomous Unmanned Systems, ICAUS 2021
Y2 - 24 September 2021 through 26 September 2021
ER -