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Hierarchical optimization method of space patrol task assignment based on deep neural network and consensus-based bundle algorithm

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ACTA ASTRONAUTICA 2023年第1期207卷 295-306页

作者： Wu, Weinan Lu, Huayue Xu, Jie Sun, Yiming Northwestern Polytech Univ Sch Astronaut 127 Youyi West Rd Xian 710072 Shaanxi Peoples R China

This research looks towards improving the accuracy and efficiency of orbit transfer speed increment estimation and task assignment for space patrol missions involving multiple platforms. First, we set up a quick prediction model for the increment in orbit transfer velocity. Then, a two-layered hybrid optimization framework is developed, with a deep neural network sub-model as the inner layer to find the optimal speed increase and the consensus-based bundle algorithm as the outer layer to solve the multi-task patrol sequence problem. Finally, the simulation and analysis of the patrol task assignment problem are conducted under two types of scenarios. The results show that the hierarchical optimization method proposed in this paper is three orders of magnitude faster than the traditional optimization method. In addition, the suggested hierarchical optimization method has sig-nificant promise in practical applications, where it can produce solutions more quickly to adapt to more complex patrol scenarios.

关键词： Space patrol task assignment consensus-based bundle algorithm Deep neural network

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Multi-AUV underwater static target search method based on consensus-based bundle algorithm and improved Glasius bio-inspired neural network

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INFORMATION SCIENCES 2024年 673卷

作者： Li, Yibing Huang, Yujie Zou, Zili Yu, Qiang Zhang, Zitang Sun, Qian Harbin Engn Univ Coll Informat & Commun Harbin 150001 Peoples R China Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao 266000 Peoples R China

This research introduces a hierarchical strategy for static target searches with multi -autonomous underwater vehicles (AUVs) to optimize cumulative search rewards. The approach comprises two primary elements: task allocation and path planning. A Voronoi diagram segments regions based on peak detection via a maximum filter in the task allocation stage. Then, a consensus -based bundling algorithm ensures the load -balanced distribution of peak sub -regions across AUVs, while a dynamic cooperation mechanism allows for dynamic adjustment of task allocation, thereby increasing the system's operational flexibility. Path planning employs an improved Glasius bio-inspired neural network, leveraging analogies to convolution processes and incorporating mean pooling, multiple convolutions, and resampling. This method enhances global information propagation and optimizes path point selection through a discounted reward function evaluating adjacent nodes, thus boosting the search efficiency of individual AUVs. Simulation experiments validate the method's effectiveness and robustness in multi-AUV static target searches, demonstrating its potential to improve search efficiency.

关键词： Autonomous underwater vehicle Static target search Coverage path planning Glasius bio-inspired neural network consensus-based bundle algorithm

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A Distributed Approach to the Multi-Robot Task Allocation Problem Using the consensus-based bundle algorithm and Ant Colony System

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IEEE ACCESS 2020年 8卷 27479-27494页

作者： Zitouni, Farouq Harous, Saad Maamri, Ramdane Kasdi Merbah Univ Dept Comp Sci Ouargla 30000 Algeria Abdelhamid Mehri Univ LIRE Lab Constantine 25000 Algeria United Arab Emirates Univ Dept Comp Sci & Software Engn Abu Dhabi U Arab Emirates Abdelhamid Mehri Univ Dept Comp Sci Constantine 25000 Algeria

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be NP-hard even with small numbers of robots and tasks. The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks;(b) Unmanned Aerial Vehicles are the robots;and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve confiicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans;(ii) traveled distances;and (iii) exchanged messages.

关键词： Ant colony system consensus-based bundle algorithm coordination multi-robot systems multi-robot task allocation problem resolution of conflicts

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Research on dynamic task allocation method of heterogeneous multi-UAV based on consensus based bundle algorithm

Research on dynamic task allocation method of heterogeneous ...

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Chinese Automation Congress (CAC)

作者： Wang, Jianfeng Jia, Gaowei Xin, Hongbo Hon, Zhongxi Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781728176871

When the multi-UAV system perform tasks in the risk and confrontation environment, the task situation will evolve with time and space, and the original plan will be disturbed in the dynamic environment, thus affecting the overall task execution efficiency. Dynamic planning is to adjust the configuration of multi-UAV system reasonably according to the situation change, so as to meet the following task coordination relationship. In this paper, a dynamic distributed task dynamic planning method is proposed to solve the problem of changing the numb er of tasks in dynamic environment. Dynamic planning is based on the consensus-based bundle algorithm (CBBA), considering the heterogeneous characteristics of UAVs and the time sequence coupling relationship of the tasks, and making fast local adjustment for the original plan. The dynamic planning method can dynamically adjust the number and value of reset tasks, so as to meet the needs of high time sensitive tasks, which is suitable for large-scale UAVs control.

关键词： multi-UAV system dynamic planning consensus-based bundle algorithm

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Communication-efficient heterogeneous multi-UAV task allocation based on clustering

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COMPUTER COMMUNICATIONS 2025年 229卷

作者： Dong, Na Liu, Shuai Mai, Xiaoming Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China

The heterogeneous unmanned aerial vehicle (UAV) system aims to achieve higher-level task coordination and execution by integrating UAVs of different types, functionalities, and scales. Addressing the diverse and complex requirements of tasks, the allocation algorithm for decentralized multi-UAV systems often encounters communication redundancy, leading to the issue of excessive communication overhead. This paper proposes a clustering-based consensus-based bundle algorithm (Clustering-CBBA), which introduces a novel bundle construction, an improved consensus strategy, and a distance-based UAV grouping approach. Specifically, utilizing the k-means++ method based on distance factors, UAVs are initially partitioned into different clusters, breaking down the large-scale problem into smaller ones. Subsequently, the first UAV in each cluster is designated as the leader UAV. The proposed algorithm can handle multi-UAV tasks by improving the task bundle construction method and consensus algorithm. Additionally, intra-cluster UAVs employ an internal conflict resolution method to gather the latest information, while inter-cluster UAVs use an external conflict resolution method to ensure conflict-free task allocation, continuing until the algorithm converges. Experimental results demonstrate that the proposed method, compared to DMCHBA, G-CBBA, and baseline CBBA, significantly reduces communication overhead across different task scales and UAV quantities. Moreover, it maintains ideal performance regarding task completion and global task reward, showcasing higher efficiency and practicality.

关键词： Heterogeneous Clustering Multi task allocation consensus-based bundle algorithm Communication

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Clustering based distributed multi-robot task allocation algorithm in large-scale systems 39

Clustering based distributed multi-robot task allocation alg...

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39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Meng, Fanyi Wang, Dong Liu, Zhong Lian, Jie Wang, Hongwei Dalian Univ Technol Sch Control Sci & Engn Dalian Peoples R China Grad Sch Chinese Aeronaut Estab Yangzhou Peoples R China

ISBN: (纸本)9798350390780;9798350379228

This paper proposes a consistency bundle algorithm based on clustering grouping for large-scale task allocation problems. Large scale task allocation problems often result in incomplete network coverage, exponential increase in communication frequency, and communication obstruction. Therefore, this article first uses clustering algorithms to group robots based on the number of tasks, transforming large-scale problems into small-scale problems;Secondly, use the consistency bundle algorithm to solve the task allocation problem for each group separately;Finally, the algorithm was used for simulation experiments on large-scale task allocation problems, and the results showed that the proposed algorithm can solve the problem while effectively reducing the number of communications.

关键词： multi-robot task allocation problem clustering algorithm consensus-based bundle algorithm

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Control research on hybrid task allocation of robots 6

Control research on hybrid task allocation of robots

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6th IEEE International Conference on Automation, Electronics and Electrical Engineering, AUTEEE 2023

作者： Di, Zhang Junru, Wang Guangbin, Sun Beijing Information Science & Technology University School of Automation Beijing China Beijing Metin Technology Co. Ltd. Beijing China

ISBN: (纸本)9798350305623

In the logistics operation of unmanned storage, there will be mixed goods with both simple and complex demand types. To ensure the effective distribution of these mixed goods, the hybrid task assignment problem of a heterogeneous robot system is worth studying. A novel algorithm based on the Fuzzy C-Means algorithm called the Fuzzy C-means-consensus-based bundle algorithm (FC-CBBA) is proposed. This algorithm incorporates complex constraints and uses the consensual-based bundle algorithm (CBBA). The logistics operations with different demand types in the storage are defined as hybrid tasks, and the execution priority of complex tasks is set by the time window. Firstly, the FCM algorithm is used to divide the position of the task into soft clustering regions according to the membership degree. Then, the robot grouping resource allocation strategy is proposed to minimize the number of robots in the region. Finally, the FC-CBBA algorithm is used to allocate tasks for each region. The performance of the algorithm is verified in MATLAB, and the results show that the proposed FC-CBBA algorithm can shorten the allocation time and achieve a task completion degree of 96.21%. Compared with CBBA and the consensus-based bundle algorithm (CBCA), the proposed algorithm shows advantages in timeliness, and task completion, which reflect the effectiveness of the proposed algorithm in solving the hybrid task assignment problem. © 2023 IEEE.

关键词： consensus-based bundle algorithm heterogeneous robot system hybrid task allocation time window

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BDBM: A distributed network simplification method for solving task allocation problems

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Liao, Bin Wan, Fangyi Hua, Yi Zhu, Shenrui Ma, Ting Qing, Xinlin Northwestern Polytech Univ Sch Aeronaut Xian 710072 Peoples R China Xiamen Univ Sch Aerosp Engn Xiamen 361005 Peoples R China

Existing task allocation algorithms often fail to consider communication load and do not meet the scalability requirements of large-scale systems in practical applications. Furthermore, many general network simplification algorithms are centralized and not designed for task allocation scenarios, thus losing their superiority when applied to solve task allocation problems in distributed systems. Inspired by recent network simplification algorithms, this paper introduces a novel network simplification algorithm called bid -based distributed brokenmotifs (BDBM), which simplifies the communication network by reducing the number of closed -loop triangles. The BDBM algorithm keeps those communication edges that facilitate the deconfliction based on the initial bids, thus avoiding the surge in the number of convergence iterations caused by the simplification. In addition, CBBA is distributed and requires only two iterations to complete the communication network simplification. Theoretical analysis confirms that the simplified network of BDBM remains connected and can be used in combination with improved task allocation algorithms based on consensus -based bundle algorithm (CBBA). In terms of experiments, we conducted comprehensive experiments in different settings and found that the proposed algorithm does not lead to worse allocation solutions. The statistical results also show that BDBM outperforms state-of-the-art network simplification algorithms in terms of solving efficiency and scalability in assisting CBBA solve large-scale complex task allocation problems.

关键词： Communication reduction Network simplification Task allocation Distributed multi-agent system consensus-based bundle algorithm

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Task Assignment for Forest Fire Suppression by Multiple UAVs

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Journal of Mechanics Engineering and Automation 2013年第2期3卷 65-70页

作者： SooYung Byeon Wonsuk Lee Hyochoong Bang Aerospace Engineering KAIST Daejeon 305-701 Korea

This paper presents a scenario of forest fire suppression using UAVs （Unmanned Aerial Vehicles） and addresses task assignment algorithm to coordinate UAVs. Forest fires are a major problem in many nations and fast extinguishing forest fires brings a lot of ecological advantages so proper use of firefighting resources is very critical. In this sense, multi UAVs forest fire suppression system can be effective way to prevent fire outbreaks. In multi agent system, an appropriate task assignment according to the SA （Situational Awareness） is the most essential to conduct mission. We should consider real time re-planning or re-scheduling of multi UAVs team because environmental situations such as wind are changeable and that changes affect the forest fire spreading. Furthermore, we have to think about convergence to a consistent SA because it may take too much time. CBBA （consensus-based bundle algorithm） is robust decentralized task assignment tool so it can be implemented in real time re-planning application. A simulation model which is the main topic in this paper shows that multi UAVs can be properly operated to suppress forest fires even if there are unpredictable random factors and partial disconnection. The simulation model includes concrete operating scenarios and recursive task re-assign algorithm until fires in the whole area are suppressed.

关键词： Multi agent system task assignment consensus-based bundle algorithm forest fire suppression.

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