Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario
The hunter-and-gatherer approach copes with the problem of dynamic multirobot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the ta...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9087250 |
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| _version_ | 1832548875642077184 |
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| author | Mehdi Dadvar Saeed Moazami Harley R. Myler Hassan Zargarzadeh |
| author_facet | Mehdi Dadvar Saeed Moazami Harley R. Myler Hassan Zargarzadeh |
| author_sort | Mehdi Dadvar |
| collection | DOAJ |
| description | The hunter-and-gatherer approach copes with the problem of dynamic multirobot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multirobot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multirobot exploration algorithm for hunters based on innovative notions of “expected information gain” to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacle complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly distorts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics. |
| format | Article |
| id | doaj-art-33fd1b075e174f9e957e87e030809b2f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-33fd1b075e174f9e957e87e030809b2f2025-02-03T06:12:51ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/90872509087250Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer ScenarioMehdi Dadvar0Saeed Moazami1Harley R. Myler2Hassan Zargarzadeh3Phillip M. Drayer Electrical Engineering Department of Lamar University, Beaumont, TX 77710, USAPhillip M. Drayer Electrical Engineering Department of Lamar University, Beaumont, TX 77710, USAPhillip M. Drayer Electrical Engineering Department of Lamar University, Beaumont, TX 77710, USAPhillip M. Drayer Electrical Engineering Department of Lamar University, Beaumont, TX 77710, USAThe hunter-and-gatherer approach copes with the problem of dynamic multirobot task allocation, where tasks are unknowingly distributed over an environment. This approach employs two complementary teams of agents: one agile in exploring (hunters) and another dexterous in completing (gatherers) the tasks. Although this approach has been studied from the task planning point of view in our previous works, the multirobot exploration and coordination aspects of the problem remain uninvestigated. This paper proposes a multirobot exploration algorithm for hunters based on innovative notions of “expected information gain” to minimize the collective cost of task accomplishments in a distributed manner. Besides, we present a coordination solution between hunters and gatherers by integrating the novel notion of profit margins into the concept of expected information gain. Statistical analysis of extensive simulation results confirms the efficacy of the proposed algorithms compared in different environments with varying levels of obstacle complexities. We also demonstrate that the lack of effective coordination between hunters and gatherers significantly distorts the total effectiveness of the planning, especially in environments containing dense obstacles and confined corridors. Finally, it is statistically proven that the overall workload is distributed equally for each type of agent which ensures that the proposed solution is not biased to a particular agent and all agents behave analogously under similar characteristics.http://dx.doi.org/10.1155/2021/9087250 |
| spellingShingle | Mehdi Dadvar Saeed Moazami Harley R. Myler Hassan Zargarzadeh Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario Complexity |
| title | Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario |
| title_full | Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario |
| title_fullStr | Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario |
| title_full_unstemmed | Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario |
| title_short | Exploration and Coordination of Complementary Multirobot Teams in a Hunter-and-Gatherer Scenario |
| title_sort | exploration and coordination of complementary multirobot teams in a hunter and gatherer scenario |
| url | http://dx.doi.org/10.1155/2021/9087250 |
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