Distributed task allocation for multiple heterogeneous UAVs based on consensus algorithm and online cooperative strategy

Wu, Weinan; Cui, Naigang; Shan, Wenzhao; Wang, Xiaogang

doi:10.1108/aeat-10-2017-0224

Cited by 20 publications

(8 citation statements)

References 21 publications

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“…In the communication-constrained environment, centralized solutions respond slowly to dynamic changes [4,19]. en the distributed solution, such as contract net protocol (CNP) [20] and the consensus algorithm [21], is offered to resolve the issue. In the distributed solution, each UAV can directly process task information as a relatively independent decision-processing unit.…”

Section: Related Workmentioning

confidence: 99%

“…Xiang et al [23] regarded the missile as an agent and proposed an improved CNP algorithm to ensure that the missile could be assigned to a better target by updating the contract. e consensus algorithm necessitates massive information transmission to achieve consistent convergence [21]. Duan et al [24] proposed a new hybrid auction algorithm, which greatly improves the task allocation effect through hierarchical decision-making.…”

Section: Related Workmentioning

confidence: 99%

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Mathematical Problems in Engineering Improved CNP-Method-Based Local Real-Time Cooperative Task Allocation of Heterogeneous Multi-UAV in Communication-Constrained Environment

Xie

Guo

Zhang

et al. 2022

Mathematical Problems in Engineering

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UAVs are widely employed in military and civilian fields because of their inherent advantages. The cooperative task allocation of multi-UAV is more in conformity with the requirements of UAV application scenarios, which has become a hot research topic. However, resource consumption allocation and the application scenarios of communication constraints are often ignored. This paper proposes a distributed multi-UAV task allocation method based on improved CNP to solve the local cooperative task allocation problem of heterogeneous multi-UAV in the communication-constrained environment. The improved CNP-based method can be divided into four stages: task release, bid application, coalition formation, and signing contracts. In the task release stage, we proposed the adaptive maximum number setting method of information transfer times and the information consistency method to solve conflicts in the local communication network. In the process of coalition formation, the resource consumption allocation algorithm based on the Gini coefficient is proposed to keep the resource difference between UAVs in the coalition within a reasonable range. The simulation results demonstrate that improved CNP-method-based cooperative task allocation can handle the local real-time task allocation problem of heterogeneous multi-UAV under communication constraints; it obtains greater task rewards and spends less time on task completion than the resource-welfare-based method, PTCFA. Simultaneously, the resource consumption algorithm makes the UAV swarm maintain a more reasonable resource difference to maximize the number of missions completed.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Mathematical Problems in Engineering Improved CNP-Method-Based Local Real-Time Cooperative Task Allocation of Heterogeneous Multi-UAV in Communication-Constrained Environment

Xie

Guo

Zhang

et al. 2022

Mathematical Problems in Engineering

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“…Grøtli and Johansen [46] used the mixed integer linear programming model to deal with the task allocation problem of UAV, which could guarantee the global optimal allocation results, but it has poor real-time and weak applicauility in large-scale task allocation problem. Wu et al [47] developed a distributed heterogeneous UAVs task allocation model based on the constraints and uncertainties in combat tasks, and proposed an algorithm based on the consistency algorithm and online collaboration strategy to solve the model. The method can obtain the feasible solution within the acceptable time.…”

Section: Ucavs Task Allocation Using Gds-woamentioning

confidence: 99%

An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

Han

Zhao

et al. 2019

IEEE Access

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To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA. In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction. Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter. When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum. Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions. The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO. The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy. Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs). To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective. The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.

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“…Decentralized algorithms based on market-based approaches [16]- [18], bio-nspired approaches [19], [20], and consensus algorithms [21] have been proposed to address the issues associated with centralized approaches. However, most of the decentralized approaches assume the communication is reliable with unlimited bandwidth, or they assume the agents are communicating with the agents in a fully connected network during the task allocation [22].…”

Section: Introductionmentioning

confidence: 99%

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

2022

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This paper presents a deep reinforcement learning framework in which agents learn how to schedule and censor themselves amongst the other agents competing for access to a limited communication medium. In particular, the process learns to schedule the communication between agents to improve the performance of task allocation in environments with constrained communication in terms of limited bandwidth and message collision. The proposed approach, called Communication-Aware Consensus-Based Bundle Algorithm (CA-CBBA), extends the previous CBBA [1] in that the learned communication policy enables efficient utilization of the shared medium by prioritizing agents with messages that are important for the mission. Furthermore, agents in denser parts of the network are censored appropriately to alleviate the message collision and hidden node problems. We evaluate our approach in various task assignment scenarios, and the results show that CA-CBBA outperforms CBBA in terms of convergence time, rate of conflict resolution, and task allocation reward. Moreover, we show that CA-CBBA yields a policy that generalizes beyond the training set to handle larger team sizes. Finally, the results on time-critical problems, such as a search-and-rescue mission, show that CA-CBBA also outperforms the baselines considered (e.g., CBBA, MCDGA [2], and ACBBA [3]) in terms of number of unassigned and conflicted tasks in most of the scenarios.

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Distributed task allocation for multiple heterogeneous UAVs based on consensus algorithm and online cooperative strategy

Cited by 20 publications

References 21 publications

Mathematical Problems in Engineering Improved CNP-Method-Based Local Real-Time Cooperative Task Allocation of Heterogeneous Multi-UAV in Communication-Constrained Environment

Mathematical Problems in Engineering Improved CNP-Method-Based Local Real-Time Cooperative Task Allocation of Heterogeneous Multi-UAV in Communication-Constrained Environment

An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

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