Sampling-based planning for non-myopic multi-robot information gathering
Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring an...
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| Autor*in: |
Kantaros, Yiannis [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Autonomous robots - Springer US, 1994, 45(2021), 7 vom: 28. Juni, Seite 1029-1046 |
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| Übergeordnetes Werk: |
volume:45 ; year:2021 ; number:7 ; day:28 ; month:06 ; pages:1029-1046 |
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| DOI / URN: |
10.1007/s10514-021-09995-4 |
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| 520 | |a Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. | ||
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| 700 | 1 | |a Pappas, George J. |4 aut | |
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10.1007/s10514-021-09995-4 doi (DE-627)OLC2077400226 (DE-He213)s10514-021-09995-4-p DE-627 ger DE-627 rakwb eng 620 VZ Kantaros, Yiannis verfasserin (orcid)0000-0002-0257-7378 aut Sampling-based planning for non-myopic multi-robot information gathering 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. Information gathering Multi-robot systems Sensor-based planning Schlotfeldt, Brent aut Atanasov, Nikolay aut Pappas, George J. aut Enthalten in Autonomous robots Springer US, 1994 45(2021), 7 vom: 28. Juni, Seite 1029-1046 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:45 year:2021 number:7 day:28 month:06 pages:1029-1046 https://doi.org/10.1007/s10514-021-09995-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 45 2021 7 28 06 1029-1046 |
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10.1007/s10514-021-09995-4 doi (DE-627)OLC2077400226 (DE-He213)s10514-021-09995-4-p DE-627 ger DE-627 rakwb eng 620 VZ Kantaros, Yiannis verfasserin (orcid)0000-0002-0257-7378 aut Sampling-based planning for non-myopic multi-robot information gathering 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. Information gathering Multi-robot systems Sensor-based planning Schlotfeldt, Brent aut Atanasov, Nikolay aut Pappas, George J. aut Enthalten in Autonomous robots Springer US, 1994 45(2021), 7 vom: 28. Juni, Seite 1029-1046 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:45 year:2021 number:7 day:28 month:06 pages:1029-1046 https://doi.org/10.1007/s10514-021-09995-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 45 2021 7 28 06 1029-1046 |
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10.1007/s10514-021-09995-4 doi (DE-627)OLC2077400226 (DE-He213)s10514-021-09995-4-p DE-627 ger DE-627 rakwb eng 620 VZ Kantaros, Yiannis verfasserin (orcid)0000-0002-0257-7378 aut Sampling-based planning for non-myopic multi-robot information gathering 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. Information gathering Multi-robot systems Sensor-based planning Schlotfeldt, Brent aut Atanasov, Nikolay aut Pappas, George J. aut Enthalten in Autonomous robots Springer US, 1994 45(2021), 7 vom: 28. Juni, Seite 1029-1046 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:45 year:2021 number:7 day:28 month:06 pages:1029-1046 https://doi.org/10.1007/s10514-021-09995-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 45 2021 7 28 06 1029-1046 |
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10.1007/s10514-021-09995-4 doi (DE-627)OLC2077400226 (DE-He213)s10514-021-09995-4-p DE-627 ger DE-627 rakwb eng 620 VZ Kantaros, Yiannis verfasserin (orcid)0000-0002-0257-7378 aut Sampling-based planning for non-myopic multi-robot information gathering 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. Information gathering Multi-robot systems Sensor-based planning Schlotfeldt, Brent aut Atanasov, Nikolay aut Pappas, George J. aut Enthalten in Autonomous robots Springer US, 1994 45(2021), 7 vom: 28. Juni, Seite 1029-1046 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:45 year:2021 number:7 day:28 month:06 pages:1029-1046 https://doi.org/10.1007/s10514-021-09995-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 45 2021 7 28 06 1029-1046 |
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10.1007/s10514-021-09995-4 doi (DE-627)OLC2077400226 (DE-He213)s10514-021-09995-4-p DE-627 ger DE-627 rakwb eng 620 VZ Kantaros, Yiannis verfasserin (orcid)0000-0002-0257-7378 aut Sampling-based planning for non-myopic multi-robot information gathering 2021 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. Information gathering Multi-robot systems Sensor-based planning Schlotfeldt, Brent aut Atanasov, Nikolay aut Pappas, George J. aut Enthalten in Autonomous robots Springer US, 1994 45(2021), 7 vom: 28. Juni, Seite 1029-1046 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:45 year:2021 number:7 day:28 month:06 pages:1029-1046 https://doi.org/10.1007/s10514-021-09995-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC AR 45 2021 7 28 06 1029-1046 |
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Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
| abstract_unstemmed |
Abstract This paper proposes a novel highly scalable sampling-based planning algorithm for multi-robot active information acquisition tasks in complex environments. Active information gathering scenarios include target localization and tracking, active SLAM, surveillance, environmental monitoring and others. The objective is to compute control policies for sensing robots which minimize the accumulated uncertainty of a dynamic hidden state over an a priori unknown horizon. To address this problem, we propose a new sampling-based algorithm that simultaneously explores both the robot motion space and the reachable information space. Unlike relevant sampling-based approaches, we show that the proposed algorithm is probabilistically complete, asymptotically optimal and is supported by convergence rate bounds. Moreover, we propose a novel biased sampling strategy that biases exploration towards informative areas. This allows the proposed method to quickly compute sensor policies that achieve desired levels of uncertainty in large-scale estimation tasks that may involve large sensor teams, workspaces, and dimensions of the hidden state. Extensions of the proposed algorithm to account for hidden states with no prior information are discussed. We provide extensive simulation results that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale estimation tasks that are computationally challenging for existing methods. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021 |
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Sampling-based planning for non-myopic multi-robot information gathering |
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Schlotfeldt, Brent Atanasov, Nikolay Pappas, George J. |
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