Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking
Current strategies employed for maritime target search and tracking are primarily based on the use of agents following a predetermined path to perform a systematic sweep of a search area. Recently, dynamic Particle Swarm Optimization (PSO) algorithms have been used together with swarming multi-robot...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: | https://hdl.handle.net/1721.1/139763 |
| _version_ | 1826206748574220288 |
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| author | Kwa, Hian Lee Tokic, Grgur Bouffanais, Roland Yue, Dick KP |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Kwa, Hian Lee Tokic, Grgur Bouffanais, Roland Yue, Dick KP |
| author_sort | Kwa, Hian Lee |
| collection | MIT |
| description | Current strategies employed for maritime target search and tracking are primarily based on the use of agents following a predetermined path to perform a systematic sweep of a search area. Recently, dynamic Particle Swarm Optimization (PSO) algorithms have been used together with swarming multi-robot systems (MRS), giving search and tracking solutions the added properties of robustness, scalability, and flexibility. Swarming MRS also give the end-user the opportunity to incrementally upgrade the robotic system, inevitably leading to the use of heterogeneous swarming MRS. However, such systems have not been well studied and incorporating upgraded agents into a swarm may result in degraded mission performances. In this paper, we propose a PSO-based strategy using a topological k-nearest neighbor graph with tunable exploration and exploitation dynamics with an adaptive repulsion parameter. This strategy is implemented within a simulated swarm of 50 agents with varying proportions of fast agents tracking a target represented by a fictitious binary function. Through these simulations, we are able to demonstrate an increase in the swarm's collective response level and target tracking performance by substituting in a proportion of fast buoys. |
| first_indexed | 2024-09-23T13:37:39Z |
| format | Article |
| id | mit-1721.1/139763 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:37:39Z |
| publishDate | 2022 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1397632023-02-03T21:30:50Z Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking Kwa, Hian Lee Tokic, Grgur Bouffanais, Roland Yue, Dick KP Massachusetts Institute of Technology. Department of Mechanical Engineering Current strategies employed for maritime target search and tracking are primarily based on the use of agents following a predetermined path to perform a systematic sweep of a search area. Recently, dynamic Particle Swarm Optimization (PSO) algorithms have been used together with swarming multi-robot systems (MRS), giving search and tracking solutions the added properties of robustness, scalability, and flexibility. Swarming MRS also give the end-user the opportunity to incrementally upgrade the robotic system, inevitably leading to the use of heterogeneous swarming MRS. However, such systems have not been well studied and incorporating upgraded agents into a swarm may result in degraded mission performances. In this paper, we propose a PSO-based strategy using a topological k-nearest neighbor graph with tunable exploration and exploitation dynamics with an adaptive repulsion parameter. This strategy is implemented within a simulated swarm of 50 agents with varying proportions of fast agents tracking a target represented by a fictitious binary function. Through these simulations, we are able to demonstrate an increase in the swarm's collective response level and target tracking performance by substituting in a proportion of fast buoys. 2022-01-27T13:50:31Z 2022-01-27T13:50:31Z 2020 2022-01-27T13:47:06Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/139763 Kwa, Hian Lee, Tokic, Grgur, Bouffanais, Roland and Yue, Dick KP. 2020. "Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking." Global Oceans 2020: Singapore – U.S. Gulf Coast. en 10.1109/IEEECONF38699.2020.9389145 Global Oceans 2020: Singapore – U.S. Gulf Coast Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Kwa, Hian Lee Tokic, Grgur Bouffanais, Roland Yue, Dick KP Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking |
| title | Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking |
| title_full | Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking |
| title_fullStr | Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking |
| title_full_unstemmed | Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking |
| title_short | Heterogeneous Swarms for Maritime Dynamic Target Search and Tracking |
| title_sort | heterogeneous swarms for maritime dynamic target search and tracking |
| url | https://hdl.handle.net/1721.1/139763 |
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