Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects
—We consider the online planning problem for a team of agents to discover and track an unknown and time-varying number of moving objects from onboard sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field-of-views, the usual planning strategy base...
| Main Authors: | , , , , |
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| Format: | Journal Article |
| Published: |
2024
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| Online Access: | http://purl.org/au-research/grants/arc/LP160101177 http://hdl.handle.net/20.500.11937/96498 |
| _version_ | 1848766158811955200 |
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| author | Van Nguyen, Hoa Vo, Ba-Ngu Vo, Ba-Tuong Rezatofighi, H. Ranasinghe, D.C. |
| author_facet | Van Nguyen, Hoa Vo, Ba-Ngu Vo, Ba-Tuong Rezatofighi, H. Ranasinghe, D.C. |
| author_sort | Van Nguyen, Hoa |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | —We consider the online planning problem for a team of agents to discover and track an unknown and time-varying number of moving objects from onboard sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field-of-views, the usual planning strategy based solely on either tracking detected objects or discovering unseen objects is inadequate. To address this, we formulate a new information-based multi-objective multi-agent control problem, cast as a partially observable Markov decision process (POMDP). The resulting multi-agent planning problem is exponentially complex due to the unknown data association between objects and multi-sensor measurements; hence, computing an optimal control action is intractable. We prove that the proposed multi-objective value function is a monotone submodular set function, which admits low-cost suboptimal solutions via greedy search with a tight optimality bound. The resulting planning algorithm has a linear complexity in the number of objects and measurements across the sensors, and quadratic in the number of agents. We demonstrate the proposed solution via a series of numerical experiments with a real-world dataset. |
| first_indexed | 2025-11-14T11:46:42Z |
| format | Journal Article |
| id | curtin-20.500.11937-96498 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:46:42Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-964982025-01-14T03:35:45Z Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects Van Nguyen, Hoa Vo, Ba-Ngu Vo, Ba-Tuong Rezatofighi, H. Ranasinghe, D.C. —We consider the online planning problem for a team of agents to discover and track an unknown and time-varying number of moving objects from onboard sensor measurements with uncertain measurement-object origins. Since the onboard sensors have limited field-of-views, the usual planning strategy based solely on either tracking detected objects or discovering unseen objects is inadequate. To address this, we formulate a new information-based multi-objective multi-agent control problem, cast as a partially observable Markov decision process (POMDP). The resulting multi-agent planning problem is exponentially complex due to the unknown data association between objects and multi-sensor measurements; hence, computing an optimal control action is intractable. We prove that the proposed multi-objective value function is a monotone submodular set function, which admits low-cost suboptimal solutions via greedy search with a tight optimality bound. The resulting planning algorithm has a linear complexity in the number of objects and measurements across the sensors, and quadratic in the number of agents. We demonstrate the proposed solution via a series of numerical experiments with a real-world dataset. 2024 Journal Article http://hdl.handle.net/20.500.11937/96498 10.1109/TSP.2024.3423755 http://purl.org/au-research/grants/arc/LP160101177 http://purl.org/au-research/grants/arc/LP200301507 http://purl.org/au-research/grants/arc/FT210100506 fulltext |
| spellingShingle | Van Nguyen, Hoa Vo, Ba-Ngu Vo, Ba-Tuong Rezatofighi, H. Ranasinghe, D.C. Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects |
| title | Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects |
| title_full | Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects |
| title_fullStr | Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects |
| title_full_unstemmed | Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects |
| title_short | Multi-Objective Multi-Agent Planning for Discovering and Tracking Multiple Mobile Objects |
| title_sort | multi-objective multi-agent planning for discovering and tracking multiple mobile objects |
| url | http://purl.org/au-research/grants/arc/LP160101177 http://purl.org/au-research/grants/arc/LP160101177 http://purl.org/au-research/grants/arc/LP160101177 http://hdl.handle.net/20.500.11937/96498 |