Optimal Maneuvering for Autonomous Vehicle Self-Localization
We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localiz...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/24/8/1169 |
| _version_ | 1797410424809848832 |
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| author | John L. McGuire Yee Wei Law Kutluyıl Doğançay Sook-Ying Ho Javaan Chahl |
| author_facet | John L. McGuire Yee Wei Law Kutluyıl Doğançay Sook-Ying Ho Javaan Chahl |
| author_sort | John L. McGuire |
| collection | DOAJ |
| description | We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an <i>l</i>-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation. |
| first_indexed | 2024-03-09T04:28:46Z |
| format | Article |
| id | doaj.art-70489d5095db4511900004961782bb24 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-09T04:28:46Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-70489d5095db4511900004961782bb242023-12-03T13:37:29ZengMDPI AGEntropy1099-43002022-08-01248116910.3390/e24081169Optimal Maneuvering for Autonomous Vehicle Self-LocalizationJohn L. McGuire0Yee Wei Law1Kutluyıl Doğançay2Sook-Ying Ho3Javaan Chahl4UniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaUniSA STEM, University of South Australia, Mawson Lakes, SA 5095, AustraliaWe consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an <i>l</i>-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation.https://www.mdpi.com/1099-4300/24/8/1169autonomous systemsoptimal maneuveringinformative path planningangle-of-arrival localizationinformation and sensor fusionoptimization and planning |
| spellingShingle | John L. McGuire Yee Wei Law Kutluyıl Doğançay Sook-Ying Ho Javaan Chahl Optimal Maneuvering for Autonomous Vehicle Self-Localization Entropy autonomous systems optimal maneuvering informative path planning angle-of-arrival localization information and sensor fusion optimization and planning |
| title | Optimal Maneuvering for Autonomous Vehicle Self-Localization |
| title_full | Optimal Maneuvering for Autonomous Vehicle Self-Localization |
| title_fullStr | Optimal Maneuvering for Autonomous Vehicle Self-Localization |
| title_full_unstemmed | Optimal Maneuvering for Autonomous Vehicle Self-Localization |
| title_short | Optimal Maneuvering for Autonomous Vehicle Self-Localization |
| title_sort | optimal maneuvering for autonomous vehicle self localization |
| topic | autonomous systems optimal maneuvering informative path planning angle-of-arrival localization information and sensor fusion optimization and planning |
| url | https://www.mdpi.com/1099-4300/24/8/1169 |
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