Cooperative Task Assignment of Heterogeneous UAVs in SEAD Mission Using A Mutil-Type Genes Chromosome Genetic Algorithm
Article No.: 4, Pages 1 - 6
Abstract
This paper puts forward a novel multi-type genes chromosome genetic algorithm(MTGC-GA) to solve the cooperative multiple task assignment problem (CMTAP) in the SEAD mission. MTGC-GA considers not only heterogeneity, kinematic and resource constraints of the UAVs, but also the timing-precedence constraint between tasks. Considering the situation of deadlock in solutions caused by timing-precedence constraint and task execution sequences of UAVs, we abstract the execution process of tasks into a directed graph and design a deadlock-free algorithm based on the directed graph to detect and remove the deadlock. In addition, specific genetic operators such as initialization, fitness, mutation, and crossover are designed to ensure the feasibility of chromosomes during the process of evolution. MTGC-GA also introduces the Dubins curve and path extension strategy to generate the real flight paths of UAVs. Finally, Monte Carlo simulation experiments show that MTGC-GA can provide a flying solution for UAVs and achieve better performance than random search method (RSM).
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Published In
December 2021
699 pages
ISBN:9781450385053
DOI:10.1145/3508546
Copyright © 2021 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2022
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- Refereed limited
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- National Defense Science and Technology Innovation Zone Foundation
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ACAI'21
ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence
December 22 - 24, 2021
Sanya, China
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Overall Acceptance Rate 173 of 395 submissions, 44%
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