Receding-Horizon Multi-Objective Optimization for Disaster Response

This paper proposes a receding-horizon, multiobjective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiv...

Full description

Saved in:
Bibliographic Details
Main Authors: Kooktae Lee, Martinez, Sonia, Cortes, Jorge, Chen, Robert H., Milam, Mark B.
Format: Conference Proceeding
Language:English
Subjects:
Optimization
Robot kinematics
Robot sensing systems
Trajectory
Uncertainty
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 5309
container_issue
container_start_page 5304
container_title
container_volume
creator Kooktae Lee
Martinez, Sonia
Cortes, Jorge
Chen, Robert H.
Milam, Mark B.
description This paper proposes a receding-horizon, multiobjective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiveness of such plan. We define three objective functions (performance, uncertainty about victim locations, and uncertainty about the environment) and formulate a multi-objective optimization problem employing a combined weighted-sum and ε-constraint method. To handle dynamic scenarios, we employ a receding-horizon approach that allows to dynamically adapt the ε constraint. We illustrate the effectiveness of the proposed method via simulations.
doi_str_mv 10.23919/ACC.2018.8431804
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_8431804</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>8431804</ieee_id><sourcerecordid>8431804</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-a96a4abe343f2dc22496dfc4a89a0f685019f8ca0dda7a2950a7b3174c1e9d553</originalsourceid><addsrcrecordid>eNotj81Kw0AURkdBsK0-gLjJCyTOf2aWJWorVAJF1-Vm5o5MaZOQGQX79Bbs6lscOJyPkAdGKy4ss0_Lpqk4ZaYyUjBD5RWZMyWMVpIbc01mXNSmVEazWzJPaU8ps1bTGWm26NDH_qtcD1M8DX3x_n3IsWy7Pbocf7BoxxyP8QQ5nmEYpuI5JkgZp2KLaRz6hHfkJsAh4f1lF-Tz9eWjWZebdvXWLDdlZLXKJVgNEjoUUgTuHefSah-cBGOBBm3UuSkYB9R7qIFbRaHuBKulY2i9UmJBHv-9ERF34xSPMP3uLofFH4CeSpw</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Receding-Horizon Multi-Objective Optimization for Disaster Response</title><source>IEEE Xplore All Conference Series</source><creator>Kooktae Lee ; Martinez, Sonia ; Cortes, Jorge ; Chen, Robert H. ; Milam, Mark B.</creator><creatorcontrib>Kooktae Lee ; Martinez, Sonia ; Cortes, Jorge ; Chen, Robert H. ; Milam, Mark B.</creatorcontrib><description>This paper proposes a receding-horizon, multiobjective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiveness of such plan. We define three objective functions (performance, uncertainty about victim locations, and uncertainty about the environment) and formulate a multi-objective optimization problem employing a combined weighted-sum and ε-constraint method. To handle dynamic scenarios, we employ a receding-horizon approach that allows to dynamically adapt the ε constraint. We illustrate the effectiveness of the proposed method via simulations.</description><identifier>EISSN: 2378-5861</identifier><identifier>EISBN: 1538654288</identifier><identifier>EISBN: 9781538654286</identifier><identifier>DOI: 10.23919/ACC.2018.8431804</identifier><language>eng</language><publisher>AACC</publisher><subject>Optimization ; Robot kinematics ; Robot sensing systems ; Trajectory ; Uncertainty</subject><ispartof>2018 Annual American Control Conference (ACC), 2018, p.5304-5309</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8431804$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,23930,23931,25140,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/8431804$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Kooktae Lee</creatorcontrib><creatorcontrib>Martinez, Sonia</creatorcontrib><creatorcontrib>Cortes, Jorge</creatorcontrib><creatorcontrib>Chen, Robert H.</creatorcontrib><creatorcontrib>Milam, Mark B.</creatorcontrib><title>Receding-Horizon Multi-Objective Optimization for Disaster Response</title><title>2018 Annual American Control Conference (ACC)</title><addtitle>ACC</addtitle><description>This paper proposes a receding-horizon, multiobjective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiveness of such plan. We define three objective functions (performance, uncertainty about victim locations, and uncertainty about the environment) and formulate a multi-objective optimization problem employing a combined weighted-sum and ε-constraint method. To handle dynamic scenarios, we employ a receding-horizon approach that allows to dynamically adapt the ε constraint. We illustrate the effectiveness of the proposed method via simulations.</description><subject>Optimization</subject><subject>Robot kinematics</subject><subject>Robot sensing systems</subject><subject>Trajectory</subject><subject>Uncertainty</subject><issn>2378-5861</issn><isbn>1538654288</isbn><isbn>9781538654286</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2018</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81Kw0AURkdBsK0-gLjJCyTOf2aWJWorVAJF1-Vm5o5MaZOQGQX79Bbs6lscOJyPkAdGKy4ss0_Lpqk4ZaYyUjBD5RWZMyWMVpIbc01mXNSmVEazWzJPaU8ps1bTGWm26NDH_qtcD1M8DX3x_n3IsWy7Pbocf7BoxxyP8QQ5nmEYpuI5JkgZp2KLaRz6hHfkJsAh4f1lF-Tz9eWjWZebdvXWLDdlZLXKJVgNEjoUUgTuHefSah-cBGOBBm3UuSkYB9R7qIFbRaHuBKulY2i9UmJBHv-9ERF34xSPMP3uLofFH4CeSpw</recordid><startdate>201806</startdate><enddate>201806</enddate><creator>Kooktae Lee</creator><creator>Martinez, Sonia</creator><creator>Cortes, Jorge</creator><creator>Chen, Robert H.</creator><creator>Milam, Mark B.</creator><general>AACC</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201806</creationdate><title>Receding-Horizon Multi-Objective Optimization for Disaster Response</title><author>Kooktae Lee ; Martinez, Sonia ; Cortes, Jorge ; Chen, Robert H. ; Milam, Mark B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-a96a4abe343f2dc22496dfc4a89a0f685019f8ca0dda7a2950a7b3174c1e9d553</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Optimization</topic><topic>Robot kinematics</topic><topic>Robot sensing systems</topic><topic>Trajectory</topic><topic>Uncertainty</topic><toplevel>online_resources</toplevel><creatorcontrib>Kooktae Lee</creatorcontrib><creatorcontrib>Martinez, Sonia</creatorcontrib><creatorcontrib>Cortes, Jorge</creatorcontrib><creatorcontrib>Chen, Robert H.</creatorcontrib><creatorcontrib>Milam, Mark B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEL</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kooktae Lee</au><au>Martinez, Sonia</au><au>Cortes, Jorge</au><au>Chen, Robert H.</au><au>Milam, Mark B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Receding-Horizon Multi-Objective Optimization for Disaster Response</atitle><btitle>2018 Annual American Control Conference (ACC)</btitle><stitle>ACC</stitle><date>2018-06</date><risdate>2018</risdate><spage>5304</spage><epage>5309</epage><pages>5304-5309</pages><eissn>2378-5861</eissn><eisbn>1538654288</eisbn><eisbn>9781538654286</eisbn><abstract>This paper proposes a receding-horizon, multiobjective optimization approach for robot motion planning in disaster response scenarios. During a search and rescue mission, a robot is deployed in the disaster area to find and egress all victims. In doing so, multiple criteria characterize the effectiveness of such plan. We define three objective functions (performance, uncertainty about victim locations, and uncertainty about the environment) and formulate a multi-objective optimization problem employing a combined weighted-sum and ε-constraint method. To handle dynamic scenarios, we employ a receding-horizon approach that allows to dynamically adapt the ε constraint. We illustrate the effectiveness of the proposed method via simulations.</abstract><pub>AACC</pub><doi>10.23919/ACC.2018.8431804</doi><tpages>6</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2378-5861
ispartof 2018 Annual American Control Conference (ACC), 2018, p.5304-5309
issn 2378-5861
language eng
recordid cdi_ieee_primary_8431804
source IEEE Xplore All Conference Series
subjects Optimization
Robot kinematics
Robot sensing systems
Trajectory
Uncertainty
title Receding-Horizon Multi-Objective Optimization for Disaster Response
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T04%3A55%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Receding-Horizon%20Multi-Objective%20Optimization%20for%20Disaster%20Response&rft.btitle=2018%20Annual%20American%20Control%20Conference%20(ACC)&rft.au=Kooktae%20Lee&rft.date=2018-06&rft.spage=5304&rft.epage=5309&rft.pages=5304-5309&rft.eissn=2378-5861&rft_id=info:doi/10.23919/ACC.2018.8431804&rft.eisbn=1538654288&rft.eisbn_list=9781538654286&rft_dat=%3Cieee_CHZPO%3E8431804%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-a96a4abe343f2dc22496dfc4a89a0f685019f8ca0dda7a2950a7b3174c1e9d553%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=8431804&rfr_iscdi=true