dc.contributor.author | Mellucci, C | |
dc.contributor.author | Prathyush, PM | |
dc.contributor.author | Edwards, C | |
dc.contributor.author | Challenor, PG | |
dc.date.accessioned | 2019-03-12T12:57:30Z | |
dc.date.issued | 2019-04-30 | |
dc.description.abstract | In this paper, a sliding mode based guidance
strategy is proposed for the control of an autonomous vehicle.
The aim of the autonomous vehicle deployment is the study
of unknown environmental spatial features. The proposed
approach allows the solution of both boundary tracking and
source seeking problems with a single autonomous vehicle
capable of sensing the value of the spatial field at its position.
The movement of the vehicle is controlled through the proposed guidance strategy, which is designed on the basis of the
collected measurements without the necessity of pre-planning
or human intervention. Moreover, no a priori knowledge
about the field and its gradient is required. The proposed
strategy is based on the so-called sub-optimal sliding mode
controller. The guidance strategy is demonstrated by computer based simulations and a set of boundary tracking
experimental sea trials. The efficacy of the algorithm to
autonomously steer the C-Enduro surface vehicle to follow
a fixed depth contour in a dynamic coastal region is demonstrated by the results from the trial described in this paper. | en_GB |
dc.description.sponsorship | Natural Environment Research Council (NERC) | en_GB |
dc.description.sponsorship | Defence Science and Technology Laboratory (DSTL) | en_GB |
dc.description.sponsorship | Innovate UK | en_GB |
dc.description.sponsorship | Autonomous Surface Vehicles (ASV) Ltd., Portchester | en_GB |
dc.identifier.citation | Published online 30 April 2019. | en_GB |
dc.identifier.doi | 10.1109/TCST.2019.2908141 | |
dc.identifier.uri | http://hdl.handle.net/10871/36410 | |
dc.language.iso | en | en_GB |
dc.publisher | Institute of Electrical and Electronics Engineers | en_GB |
dc.rights | © The Author(s), 2019. Open Access. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/ | |
dc.subject | Sliding mode control | en_GB |
dc.subject | Autonomous vehicle | en_GB |
dc.subject | boundary tracking | en_GB |
dc.subject | source seeking | en_GB |
dc.title | Environmental feature exploration with a single autonomous vehicle | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2019-03-12T12:57:30Z | |
dc.identifier.issn | 1063-6536 | |
dc.description | This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record. | en_GB |
dc.identifier.journal | IEEE Transactions on Control Systems Technology | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2019-02-17 | |
exeter.funder | ::Natural Environment Research Council (NERC) | en_GB |
rioxxterms.funder | Natural Environment Research Council | en_GB |
rioxxterms.identifier.project | Adaptive Autonomous Ocean Sampling Network (AAOSN) project | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2019-02-17 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2019-03-12T11:32:08Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2019-05-10T15:19:50Z | |
refterms.panel | B | en_GB |
rioxxterms.funder.project | 0044d5a8-1033-4e30-8cb9-d0cefc8a3fc8 | en_GB |