A Dynamic Territorializing Approach for Multiagent Task Allocation
In this paper, we propose a dynamic territorializing approach for the problem of distributing tasks among a group of robots. We consider the scenario in which a task comprises two subtasks—detection and completion; two complementary teams of agents, hunters and gatherers, are assigned for the subtas...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/8141726 |
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| _version_ | 1832550976092897280 |
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| author | Mohammad Islam Mehdi Dadvar Hassan Zargarzadeh |
| author_facet | Mohammad Islam Mehdi Dadvar Hassan Zargarzadeh |
| author_sort | Mohammad Islam |
| collection | DOAJ |
| description | In this paper, we propose a dynamic territorializing approach for the problem of distributing tasks among a group of robots. We consider the scenario in which a task comprises two subtasks—detection and completion; two complementary teams of agents, hunters and gatherers, are assigned for the subtasks. Hunters are assigned with the task of exploring the environment, i.e., detection, whereas gatherers are assigned with the latter subtask. To minimize the workload among the gatherers, the proposed algorithm utilizes the center of mass of the known targets to form territories among the gatherers. The concept of center of mass has been adopted because it simplifies the task of territorial optimization and allows the system to dynamically adapt to changes in the environment by adjusting the assigned partitions as more targets are discovered. In addition, we present a game-theoretic analysis to justify the agents’ reasoning mechanism to stay within their territory while completing the tasks. Moreover, simulation results are presented to analyze the performance of the proposed algorithm. First, we investigate how the performance of the proposed algorithm varies as the frequency of territorializing is varied. Then, we examine how the density of the tasks affects the performance of the algorithm. Finally, the effectiveness of the proposed algorithm is verified by comparing its performance against an alternative approach. |
| format | Article |
| id | doaj-art-626409eaa6674e48b4eda28ff297ec09 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-626409eaa6674e48b4eda28ff297ec092025-02-03T06:05:16ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/81417268141726A Dynamic Territorializing Approach for Multiagent Task AllocationMohammad Islam0Mehdi Dadvar1Hassan Zargarzadeh2Electrical Engineering Department, Lamar University, Beaumont, TX 77710, USAElectrical Engineering Department, Lamar University, Beaumont, TX 77710, USAElectrical Engineering Department, Lamar University, Beaumont, TX 77710, USAIn this paper, we propose a dynamic territorializing approach for the problem of distributing tasks among a group of robots. We consider the scenario in which a task comprises two subtasks—detection and completion; two complementary teams of agents, hunters and gatherers, are assigned for the subtasks. Hunters are assigned with the task of exploring the environment, i.e., detection, whereas gatherers are assigned with the latter subtask. To minimize the workload among the gatherers, the proposed algorithm utilizes the center of mass of the known targets to form territories among the gatherers. The concept of center of mass has been adopted because it simplifies the task of territorial optimization and allows the system to dynamically adapt to changes in the environment by adjusting the assigned partitions as more targets are discovered. In addition, we present a game-theoretic analysis to justify the agents’ reasoning mechanism to stay within their territory while completing the tasks. Moreover, simulation results are presented to analyze the performance of the proposed algorithm. First, we investigate how the performance of the proposed algorithm varies as the frequency of territorializing is varied. Then, we examine how the density of the tasks affects the performance of the algorithm. Finally, the effectiveness of the proposed algorithm is verified by comparing its performance against an alternative approach.http://dx.doi.org/10.1155/2020/8141726 |
| spellingShingle | Mohammad Islam Mehdi Dadvar Hassan Zargarzadeh A Dynamic Territorializing Approach for Multiagent Task Allocation Complexity |
| title | A Dynamic Territorializing Approach for Multiagent Task Allocation |
| title_full | A Dynamic Territorializing Approach for Multiagent Task Allocation |
| title_fullStr | A Dynamic Territorializing Approach for Multiagent Task Allocation |
| title_full_unstemmed | A Dynamic Territorializing Approach for Multiagent Task Allocation |
| title_short | A Dynamic Territorializing Approach for Multiagent Task Allocation |
| title_sort | dynamic territorializing approach for multiagent task allocation |
| url | http://dx.doi.org/10.1155/2020/8141726 |
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