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JACIII Vol.26 No.4 pp. 570-580
doi: 10.20965/jaciii.2022.p0570
(2022)

Paper:

Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

Hao Zhang*, Lihua Dou*,**, Bin Xin*, Ruowei Zhang*, and Qing Wang*

*School of Automation, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

**Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Received:
March 19, 2022
Accepted:
April 14, 2022
Published:
July 20, 2022
Keywords:
multi-objective evolutionary algorithm, heterogeneous UAVs, task planning, task allocation, path planning
Abstract
Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study

The task planning of UAVs with heterogeneous payloads

In this study, the reconnaissance and confirmation task planning of multiple fixed-wing unmanned aerial vehicles (UAV) with specific payloads, which is an NP-hard problem with strong constraints and mixed variables, is decomposed into two subproblems, task allocation with “payload-target” matching constraints, and fast path planning of the UAV group, for which two mathematical models are respectively established. A bi-layer collaborative solution framework is also proposed. The outer layer optimizes the allocation scheme between the UAVs and targets, whereas the inner layer generates the UAV path and evaluates the outer scheme. In the outer layer, a unified encoding based on the grouping and pairing relationship between UAVs and targets is proposed. The corresponding combinatorial mutation operators are then designed for the representative NSGA-II, MOEA/D-AWA, and DMOEA-ϵC algorithms. In the inner layer, an efficient heuristic algorithm is used to solve the path planning of each UAV group. The simulation results verify the effectiveness of the cooperative bi-layer solution scheme and the combined mutation operators. At the same time, compared with the NSGA-II and MOEA/D-AWA, DMOEA-ϵC can obtain a significantly better Pareto front and can weigh the assigned number of UAVs and the total task completion time to generate more diversified reconnaissance confirmation execution schemes.

Cite this article as:
H. Zhang, L. Dou, B. Xin, R. Zhang, and Q. Wang, “Reconnaissance and Confirmation Task Planning of Multiple Fixed-Wing UAVs with Specific Payloads: A Comparison Study,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.4, pp. 570-580, 2022.
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Last updated on Aug. 05, 2022