Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots
Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bay...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sydney, Nitin [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Autonomous robots - Springer US, 1994, 41(2015), 1 vom: 30. Dez., Seite 231-241 |
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| Übergeordnetes Werk: |
volume:41 ; year:2015 ; number:1 ; day:30 ; month:12 ; pages:231-241 |
| Links: |
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| DOI / URN: |
10.1007/s10514-015-9542-0 |
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| 520 | |a Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. | ||
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10.1007/s10514-015-9542-0 doi (DE-627)OLC2052750509 (DE-He213)s10514-015-9542-0-p DE-627 ger DE-627 rakwb eng 620 VZ Sydney, Nitin verfasserin aut Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. Cooperative control Target detection Path planning Paley, Derek A. aut Sofge, Donald aut Enthalten in Autonomous robots Springer US, 1994 41(2015), 1 vom: 30. Dez., Seite 231-241 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:41 year:2015 number:1 day:30 month:12 pages:231-241 https://doi.org/10.1007/s10514-015-9542-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 41 2015 1 30 12 231-241 |
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10.1007/s10514-015-9542-0 doi (DE-627)OLC2052750509 (DE-He213)s10514-015-9542-0-p DE-627 ger DE-627 rakwb eng 620 VZ Sydney, Nitin verfasserin aut Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. Cooperative control Target detection Path planning Paley, Derek A. aut Sofge, Donald aut Enthalten in Autonomous robots Springer US, 1994 41(2015), 1 vom: 30. Dez., Seite 231-241 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:41 year:2015 number:1 day:30 month:12 pages:231-241 https://doi.org/10.1007/s10514-015-9542-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 41 2015 1 30 12 231-241 |
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10.1007/s10514-015-9542-0 doi (DE-627)OLC2052750509 (DE-He213)s10514-015-9542-0-p DE-627 ger DE-627 rakwb eng 620 VZ Sydney, Nitin verfasserin aut Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. Cooperative control Target detection Path planning Paley, Derek A. aut Sofge, Donald aut Enthalten in Autonomous robots Springer US, 1994 41(2015), 1 vom: 30. Dez., Seite 231-241 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:41 year:2015 number:1 day:30 month:12 pages:231-241 https://doi.org/10.1007/s10514-015-9542-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 41 2015 1 30 12 231-241 |
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10.1007/s10514-015-9542-0 doi (DE-627)OLC2052750509 (DE-He213)s10514-015-9542-0-p DE-627 ger DE-627 rakwb eng 620 VZ Sydney, Nitin verfasserin aut Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. Cooperative control Target detection Path planning Paley, Derek A. aut Sofge, Donald aut Enthalten in Autonomous robots Springer US, 1994 41(2015), 1 vom: 30. Dez., Seite 231-241 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:41 year:2015 number:1 day:30 month:12 pages:231-241 https://doi.org/10.1007/s10514-015-9542-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 41 2015 1 30 12 231-241 |
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10.1007/s10514-015-9542-0 doi (DE-627)OLC2052750509 (DE-He213)s10514-015-9542-0-p DE-627 ger DE-627 rakwb eng 620 VZ Sydney, Nitin verfasserin aut Physics-inspired motion planning for information-theoretic target detection using multiple aerial robots 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. Cooperative control Target detection Path planning Paley, Derek A. aut Sofge, Donald aut Enthalten in Autonomous robots Springer US, 1994 41(2015), 1 vom: 30. Dez., Seite 231-241 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:41 year:2015 number:1 day:30 month:12 pages:231-241 https://doi.org/10.1007/s10514-015-9542-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 41 2015 1 30 12 231-241 |
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Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. © Springer Science+Business Media New York 2015 |
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Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. © Springer Science+Business Media New York 2015 |
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Abstract This paper presents a motion-planning strategy for multiple, mobile sensor platforms using visual sensors with a finite field of view. Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. We show that the gradient-following behavior corresponds to locally maximizing the mutual information between the measurements and the target state. The performance of the algorithm is experimentally demonstrated for visual measurements in a motion capture facility using quadrotor sensor platforms equipped with downward facing cameras. © Springer Science+Business Media New York 2015 |
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10.1007/s10514-015-9542-0 |
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2024-07-03T16:23:43.461Z |
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Visual sensors are used to collect position measurements of potential targets within the search domain. Measurements are assimilated into a multi-target Bayesian likelihood ratio tracker that recursively produces a probability density function over the possible target positions. Vehicles are dynamically routed using a controller based on a concept from artificial physics, where vehicle motion depends on the target probability at their location as well as the distance to nearby agents. In this paradigm, the inverse log-likelihood ratio represents temperature, i.e., high likelihood corresponds to cold temperature and low likelihood corresponds to high temperature. Vehicles move at a temperature-dependent speed along the negative gradient of the temperature surface while interacting locally with other agents via a Lennard-Jones potential in order to emergently transition between the three states of matter—solid, liquid, and gas. 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