Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm
dc.contributor.author | Zhuang, Y | |
dc.contributor.author | sharma, sanjay | |
dc.contributor.author | Subudhi, B | |
dc.contributor.author | Huang, H | |
dc.contributor.author | Wan, Jian | |
dc.date.accessioned | 2016-11-14T16:53:11Z | |
dc.date.available | 2016-11-14T16:53:11Z | |
dc.date.issued | 2016-11-15 | |
dc.identifier.issn | 0029-8018 | |
dc.identifier.other | C | |
dc.identifier.uri | http://hdl.handle.net/10026.1/6737 | |
dc.description | publisher: Elsevier articletitle: Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm journaltitle: Ocean Engineering articlelink: http://dx.doi.org/10.1016/j.oceaneng.2016.09.040 content_type: article copyright: © 2016 Elsevier Ltd. All rights reserved. | |
dc.description.abstract |
This paper presents an efficient path-planner based on a hybrid optimization algorithm for autonomous underwater vehicles (AUVs) operating in cluttered and uncertain environments. The algorithm integrates particle swarm optimization (PSO) algorithm with Legendre pseudospectral method (LPM), which is named as hybrid PSO-LPM algorithm. PSO is first employed as an initialization generator with its strong global searching ability and robustness to random initial values. Then, the searching algorithm is switched to LPM with the initialization obtained by PSO algorithm to accelerate the following searching process. The flatness property of AUV is also utilized to reduce the computational cost for planning, making the optimization algorithm valid for local re-planning to efficiently solve the collision avoidance problem. Simulation results show that the hybrid PSO-LPM algorithm is able to find a better trajectory than standard PSO algorithm and with the re-planning scheme it also succeeds in real-time collision avoidance from both static obstacles and moving obstacles with varying levels of position uncertainty. Finally, 100-run Monte Carlo simulations are carried out to check robustness of the proposed re-planner. The results demonstrate that the hybrid optimization algorithm is robust to random initializations and it is effective and efficient for collision-free path planning. | |
dc.format.extent | 190-199 | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Elsevier BV | |
dc.subject | Autonomous underwater vehicle | |
dc.subject | Pseudospectral method | |
dc.subject | Particle swarm optimization | |
dc.subject | Differential flatness | |
dc.subject | Path re-planning | |
dc.subject | Collision avoidance | |
dc.title | Efficient collision-free path planning for autonomous underwater vehicles in dynamic environments with a hybrid optimization algorithm | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000388050400017&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.volume | 127 | |
plymouth.publication-status | Published | |
plymouth.journal | Ocean Engineering | |
dc.identifier.doi | 10.1016/j.oceaneng.2016.09.040 | |
plymouth.organisational-group | /Plymouth | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering | |
plymouth.organisational-group | /Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA | |
plymouth.organisational-group | /Plymouth/REF 2021 Researchers by UoA/UoA12 Engineering | |
plymouth.organisational-group | /Plymouth/Research Groups | |
plymouth.organisational-group | /Plymouth/Research Groups/Marine Institute | |
plymouth.organisational-group | /Plymouth/Users by role | |
plymouth.organisational-group | /Plymouth/Users by role/Academics | |
plymouth.organisational-group | /Plymouth/Users by role/Researchers in ResearchFish submission | |
dcterms.dateAccepted | 2016-09-24 | |
dc.rights.embargodate | 2017-10-12 | |
dc.rights.embargoperiod | 12 months | |
rioxxterms.versionofrecord | 10.1016/j.oceaneng.2016.09.040 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/under-embargo-all-rights-reserved | |
rioxxterms.licenseref.startdate | 2016-11-15 | |
rioxxterms.type | Journal Article/Review |