Path planning in GPS-denied environments via collective intelligence of distributed sensor networks
This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Jha, Devesh K [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2016 |
|---|
| Rechteinformationen: |
Nutzungsrecht: © 2015 Taylor & Francis 2015 |
|---|
| Schlagwörter: |
|---|
| Übergeordnetes Werk: |
Enthalten in: International journal of control - London : Taylor & Francis, 1965, 89(2016), 5, Seite 984 |
|---|---|
| Übergeordnetes Werk: |
volume:89 ; year:2016 ; number:5 ; pages:984 |
| Links: |
|---|
| DOI / URN: |
10.1080/00207179.2015.1110754 |
|---|
| Katalog-ID: |
OLC1974536025 |
|---|
| LEADER | 01000caa a2200265 4500 | ||
|---|---|---|---|
| 001 | OLC1974536025 | ||
| 003 | DE-627 | ||
| 005 | 20230714190133.0 | ||
| 007 | tu | ||
| 008 | 160609s2016 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1080/00207179.2015.1110754 |2 doi | |
| 028 | 5 | 2 | |a PQ20160610 |
| 035 | |a (DE-627)OLC1974536025 | ||
| 035 | |a (DE-599)GBVOLC1974536025 | ||
| 035 | |a (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 | ||
| 035 | |a (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 620 |q DNB |
| 100 | 1 | |a Jha, Devesh K |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Path planning in GPS-denied environments via collective intelligence of distributed sensor networks |
| 264 | 1 | |c 2016 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 520 | |a This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. | ||
| 540 | |a Nutzungsrecht: © 2015 Taylor & Francis 2015 | ||
| 650 | 4 | |a Target detection | |
| 650 | 4 | |a collective intelligence | |
| 650 | 4 | |a mobile sensor network | |
| 650 | 4 | |a path Planning | |
| 650 | 4 | |a language measure | |
| 650 | 4 | |a Intelligence | |
| 650 | 4 | |a Sensors | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Robots | |
| 700 | 1 | |a Chattopadhyay, Pritthi |4 oth | |
| 700 | 1 | |a Sarkar, Soumik |4 oth | |
| 700 | 1 | |a Ray, Asok |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |t International journal of control |d London : Taylor & Francis, 1965 |g 89(2016), 5, Seite 984 |w (DE-627)129595780 |w (DE-600)240693-7 |w (DE-576)015088804 |x 0020-7179 |7 nnns |
| 773 | 1 | 8 | |g volume:89 |g year:2016 |g number:5 |g pages:984 |
| 856 | 4 | 1 | |u http://dx.doi.org/10.1080/00207179.2015.1110754 |3 Volltext |
| 856 | 4 | 2 | |u http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-TEC | ||
| 912 | |a GBV_ILN_70 | ||
| 912 | |a GBV_ILN_2020 | ||
| 912 | |a GBV_ILN_4314 | ||
| 912 | |a GBV_ILN_4318 | ||
| 912 | |a GBV_ILN_4700 | ||
| 951 | |a AR | ||
| 952 | |d 89 |j 2016 |e 5 |h 984 | ||
| author_variant |
d k j dk dkj |
|---|---|
| matchkey_str |
article:00207179:2016----::ahlnignpdneevrnetvaolcienelgnef |
| hierarchy_sort_str |
2016 |
| publishDate |
2016 |
| allfields |
10.1080/00207179.2015.1110754 doi PQ20160610 (DE-627)OLC1974536025 (DE-599)GBVOLC1974536025 (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein DE-627 ger DE-627 rakwb eng 620 DNB Jha, Devesh K verfasserin aut Path planning in GPS-denied environments via collective intelligence of distributed sensor networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. Nutzungsrecht: © 2015 Taylor & Francis 2015 Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots Chattopadhyay, Pritthi oth Sarkar, Soumik oth Ray, Asok oth Enthalten in International journal of control London : Taylor & Francis, 1965 89(2016), 5, Seite 984 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:89 year:2016 number:5 pages:984 http://dx.doi.org/10.1080/00207179.2015.1110754 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 89 2016 5 984 |
| spelling |
10.1080/00207179.2015.1110754 doi PQ20160610 (DE-627)OLC1974536025 (DE-599)GBVOLC1974536025 (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein DE-627 ger DE-627 rakwb eng 620 DNB Jha, Devesh K verfasserin aut Path planning in GPS-denied environments via collective intelligence of distributed sensor networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. Nutzungsrecht: © 2015 Taylor & Francis 2015 Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots Chattopadhyay, Pritthi oth Sarkar, Soumik oth Ray, Asok oth Enthalten in International journal of control London : Taylor & Francis, 1965 89(2016), 5, Seite 984 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:89 year:2016 number:5 pages:984 http://dx.doi.org/10.1080/00207179.2015.1110754 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 89 2016 5 984 |
| allfields_unstemmed |
10.1080/00207179.2015.1110754 doi PQ20160610 (DE-627)OLC1974536025 (DE-599)GBVOLC1974536025 (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein DE-627 ger DE-627 rakwb eng 620 DNB Jha, Devesh K verfasserin aut Path planning in GPS-denied environments via collective intelligence of distributed sensor networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. Nutzungsrecht: © 2015 Taylor & Francis 2015 Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots Chattopadhyay, Pritthi oth Sarkar, Soumik oth Ray, Asok oth Enthalten in International journal of control London : Taylor & Francis, 1965 89(2016), 5, Seite 984 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:89 year:2016 number:5 pages:984 http://dx.doi.org/10.1080/00207179.2015.1110754 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 89 2016 5 984 |
| allfieldsGer |
10.1080/00207179.2015.1110754 doi PQ20160610 (DE-627)OLC1974536025 (DE-599)GBVOLC1974536025 (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein DE-627 ger DE-627 rakwb eng 620 DNB Jha, Devesh K verfasserin aut Path planning in GPS-denied environments via collective intelligence of distributed sensor networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. Nutzungsrecht: © 2015 Taylor & Francis 2015 Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots Chattopadhyay, Pritthi oth Sarkar, Soumik oth Ray, Asok oth Enthalten in International journal of control London : Taylor & Francis, 1965 89(2016), 5, Seite 984 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:89 year:2016 number:5 pages:984 http://dx.doi.org/10.1080/00207179.2015.1110754 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 89 2016 5 984 |
| allfieldsSound |
10.1080/00207179.2015.1110754 doi PQ20160610 (DE-627)OLC1974536025 (DE-599)GBVOLC1974536025 (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein DE-627 ger DE-627 rakwb eng 620 DNB Jha, Devesh K verfasserin aut Path planning in GPS-denied environments via collective intelligence of distributed sensor networks 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. Nutzungsrecht: © 2015 Taylor & Francis 2015 Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots Chattopadhyay, Pritthi oth Sarkar, Soumik oth Ray, Asok oth Enthalten in International journal of control London : Taylor & Francis, 1965 89(2016), 5, Seite 984 (DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 0020-7179 nnns volume:89 year:2016 number:5 pages:984 http://dx.doi.org/10.1080/00207179.2015.1110754 Volltext http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 AR 89 2016 5 984 |
| language |
English |
| source |
Enthalten in International journal of control 89(2016), 5, Seite 984 volume:89 year:2016 number:5 pages:984 |
| sourceStr |
Enthalten in International journal of control 89(2016), 5, Seite 984 volume:89 year:2016 number:5 pages:984 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots |
| dewey-raw |
620 |
| isfreeaccess_bool |
false |
| container_title |
International journal of control |
| authorswithroles_txt_mv |
Jha, Devesh K @@aut@@ Chattopadhyay, Pritthi @@oth@@ Sarkar, Soumik @@oth@@ Ray, Asok @@oth@@ |
| publishDateDaySort_date |
2016-01-01T00:00:00Z |
| hierarchy_top_id |
129595780 |
| dewey-sort |
3620 |
| id |
OLC1974536025 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1974536025</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714190133.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160609s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00207179.2015.1110754</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160610</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1974536025</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1974536025</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jha, Devesh K</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Path planning in GPS-denied environments via collective intelligence of distributed sensor networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2015 Taylor & Francis 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Target detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">collective intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mobile sensor network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">path Planning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">language measure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robots</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chattopadhyay, Pritthi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sarkar, Soumik</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ray, Asok</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of control</subfield><subfield code="d">London : Taylor & Francis, 1965</subfield><subfield code="g">89(2016), 5, Seite 984</subfield><subfield code="w">(DE-627)129595780</subfield><subfield code="w">(DE-600)240693-7</subfield><subfield code="w">(DE-576)015088804</subfield><subfield code="x">0020-7179</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:89</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:984</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00207179.2015.1110754</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4314</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">89</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="h">984</subfield></datafield></record></collection>
|
| author |
Jha, Devesh K |
| spellingShingle |
Jha, Devesh K ddc 620 misc Target detection misc collective intelligence misc mobile sensor network misc path Planning misc language measure misc Intelligence misc Sensors misc Algorithms misc Robots Path planning in GPS-denied environments via collective intelligence of distributed sensor networks |
| authorStr |
Jha, Devesh K |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)129595780 |
| format |
Article |
| dewey-ones |
620 - Engineering & allied operations |
| delete_txt_mv |
keep |
| author_role |
aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
0020-7179 |
| topic_title |
620 DNB Path planning in GPS-denied environments via collective intelligence of distributed sensor networks Target detection collective intelligence mobile sensor network path Planning language measure Intelligence Sensors Algorithms Robots |
| topic |
ddc 620 misc Target detection misc collective intelligence misc mobile sensor network misc path Planning misc language measure misc Intelligence misc Sensors misc Algorithms misc Robots |
| topic_unstemmed |
ddc 620 misc Target detection misc collective intelligence misc mobile sensor network misc path Planning misc language measure misc Intelligence misc Sensors misc Algorithms misc Robots |
| topic_browse |
ddc 620 misc Target detection misc collective intelligence misc mobile sensor network misc path Planning misc language measure misc Intelligence misc Sensors misc Algorithms misc Robots |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| author2_variant |
p c pc s s ss a r ar |
| hierarchy_parent_title |
International journal of control |
| hierarchy_parent_id |
129595780 |
| dewey-tens |
620 - Engineering |
| hierarchy_top_title |
International journal of control |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)129595780 (DE-600)240693-7 (DE-576)015088804 |
| title |
Path planning in GPS-denied environments via collective intelligence of distributed sensor networks |
| ctrlnum |
(DE-627)OLC1974536025 (DE-599)GBVOLC1974536025 (PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20 (KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein |
| title_full |
Path planning in GPS-denied environments via collective intelligence of distributed sensor networks |
| author_sort |
Jha, Devesh K |
| journal |
International journal of control |
| journalStr |
International journal of control |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
600 - Technology |
| recordtype |
marc |
| publishDateSort |
2016 |
| contenttype_str_mv |
txt |
| container_start_page |
984 |
| author_browse |
Jha, Devesh K |
| container_volume |
89 |
| class |
620 DNB |
| format_se |
Aufsätze |
| author-letter |
Jha, Devesh K |
| doi_str_mv |
10.1080/00207179.2015.1110754 |
| dewey-full |
620 |
| title_sort |
path planning in gps-denied environments via collective intelligence of distributed sensor networks |
| title_auth |
Path planning in GPS-denied environments via collective intelligence of distributed sensor networks |
| abstract |
This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. |
| abstractGer |
This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. |
| abstract_unstemmed |
This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 GBV_ILN_2020 GBV_ILN_4314 GBV_ILN_4318 GBV_ILN_4700 |
| container_issue |
5 |
| title_short |
Path planning in GPS-denied environments via collective intelligence of distributed sensor networks |
| url |
http://dx.doi.org/10.1080/00207179.2015.1110754 http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754 |
| remote_bool |
false |
| author2 |
Chattopadhyay, Pritthi Sarkar, Soumik Ray, Asok |
| author2Str |
Chattopadhyay, Pritthi Sarkar, Soumik Ray, Asok |
| ppnlink |
129595780 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| author2_role |
oth oth oth |
| doi_str |
10.1080/00207179.2015.1110754 |
| up_date |
2024-07-04T04:31:26.542Z |
| _version_ |
1803621484770361344 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a2200265 4500</leader><controlfield tag="001">OLC1974536025</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230714190133.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">160609s2016 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1080/00207179.2015.1110754</subfield><subfield code="2">doi</subfield></datafield><datafield tag="028" ind1="5" ind2="2"><subfield code="a">PQ20160610</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC1974536025</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBVOLC1974536025</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(PRQ)i1459-c15e0a35b08cbd894a6e71f2cf076242c19fce145f6a7644ecbb41b320e579b20</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(KEY)0006630320160000089000500984pathplanningingpsdeniedenvironmentsviacollectivein</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">620</subfield><subfield code="q">DNB</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Jha, Devesh K</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Path planning in GPS-denied environments via collective intelligence of distributed sensor networks</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2016</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">This paper proposes a framework for reactive goal-directed navigation without global positioning facilities in unknown dynamic environments. A mobile sensor network is used for localising regions of interest for path planning of an autonomous mobile robot. The underlying theory is an extension of a generalised gossip algorithm that has been recently developed in a language-measure-theoretic setting. The algorithm has been used to propagate local decisions of target detection over a mobile sensor network and thus, it generates a belief map for the detected target over the network. In this setting, an autonomous mobile robot may communicate only with a few mobile sensing nodes in its own neighbourhood and localise itself relative to the communicating nodes with bounded uncertainties. The robot makes use of the knowledge based on the belief of the mobile sensors to generate a sequence of way-points, leading to a possible goal. The estimated way-points are used by a sampling-based motion planning algorithm to generate feasible trajectories for the robot. The proposed concept has been validated by numerical simulation on a mobile sensor network test-bed and a Dubin's car-like robot.</subfield></datafield><datafield tag="540" ind1=" " ind2=" "><subfield code="a">Nutzungsrecht: © 2015 Taylor & Francis 2015</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Target detection</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">collective intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mobile sensor network</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">path Planning</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">language measure</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Intelligence</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Sensors</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Robots</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chattopadhyay, Pritthi</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sarkar, Soumik</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ray, Asok</subfield><subfield code="4">oth</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of control</subfield><subfield code="d">London : Taylor & Francis, 1965</subfield><subfield code="g">89(2016), 5, Seite 984</subfield><subfield code="w">(DE-627)129595780</subfield><subfield code="w">(DE-600)240693-7</subfield><subfield code="w">(DE-576)015088804</subfield><subfield code="x">0020-7179</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:89</subfield><subfield code="g">year:2016</subfield><subfield code="g">number:5</subfield><subfield code="g">pages:984</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">http://dx.doi.org/10.1080/00207179.2015.1110754</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="u">http://www.tandfonline.com/doi/abs/10.1080/00207179.2015.1110754</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2020</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4314</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4318</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4700</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">89</subfield><subfield code="j">2016</subfield><subfield code="e">5</subfield><subfield code="h">984</subfield></datafield></record></collection>
|
| score |
7.400755 |