Stability Constrained Mobile Manipulation Planning on Rough Terrain
This paper presents a framework that allows online dynamic-stability-constrained optimal trajectory planning of a mobile manipulator robot working on rough terrain. First, the kinematics model of a mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are presented as theoretic...
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| creator | Song, Jiazhi Sharf, Inna |
| description | This paper presents a framework that allows online
dynamic-stability-constrained optimal trajectory planning of a mobile
manipulator robot working on rough terrain. First, the kinematics model of a
mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are
presented as theoretical background. Then, a sampling-based quasi-static
planning algorithm modified for stability guarantee and traction optimization
in continuous dynamic motion is presented along with a mathematical proof. The
robot's quasi-static path is then used as an initial guess to warm-start a
nonlinear optimal control solver which may otherwise have difficulties finding
a solution to the stability-constrained formulation efficiently. The
performance and computational efficiency of the framework are demonstrated
through an application to a simulated timber harvesting mobile manipulator
machine working on varying terrain. The results demonstrate feasibility of
online trajectory planning on varying terrain while satisfying the dynamic
stability constraint. |
| doi_str_mv | 10.48550/arxiv.2105.04396 |
| format | Article |
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dynamic-stability-constrained optimal trajectory planning of a mobile
manipulator robot working on rough terrain. First, the kinematics model of a
mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are
presented as theoretical background. Then, a sampling-based quasi-static
planning algorithm modified for stability guarantee and traction optimization
in continuous dynamic motion is presented along with a mathematical proof. The
robot's quasi-static path is then used as an initial guess to warm-start a
nonlinear optimal control solver which may otherwise have difficulties finding
a solution to the stability-constrained formulation efficiently. The
performance and computational efficiency of the framework are demonstrated
through an application to a simulated timber harvesting mobile manipulator
machine working on varying terrain. The results demonstrate feasibility of
online trajectory planning on varying terrain while satisfying the dynamic
stability constraint.</description><identifier>DOI: 10.48550/arxiv.2105.04396</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Robotics ; Computer Science - Systems and Control</subject><creationdate>2021-05</creationdate><rights>http://creativecommons.org/licenses/by/4.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2105.04396$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2105.04396$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Song, Jiazhi</creatorcontrib><creatorcontrib>Sharf, Inna</creatorcontrib><title>Stability Constrained Mobile Manipulation Planning on Rough Terrain</title><description>This paper presents a framework that allows online
dynamic-stability-constrained optimal trajectory planning of a mobile
manipulator robot working on rough terrain. First, the kinematics model of a
mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are
presented as theoretical background. Then, a sampling-based quasi-static
planning algorithm modified for stability guarantee and traction optimization
in continuous dynamic motion is presented along with a mathematical proof. The
robot's quasi-static path is then used as an initial guess to warm-start a
nonlinear optimal control solver which may otherwise have difficulties finding
a solution to the stability-constrained formulation efficiently. The
performance and computational efficiency of the framework are demonstrated
through an application to a simulated timber harvesting mobile manipulator
machine working on varying terrain. The results demonstrate feasibility of
online trajectory planning on varying terrain while satisfying the dynamic
stability constraint.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Robotics</subject><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj8tOwzAURL1hgQofwKr-gQQ_kuvrJYp4Sa1AkH10nditpeBUboro39MWVjMaHY10GLuToqywrsU95Z_4XSop6lJU2sI1az5ncnGM85E3U9rPmWLyA19Pp9HzNaW4O4w0xynx95FSimnDT_1jOmy2vPX5zN-wq0Dj3t_-54K1T49t81Ks3p5fm4dVQWCgqIzxPRpAq8D0QblQeYMyuNA7QiOUdEojOggDDoMFZ5WxGjRIRO8C6QVb_t1eLLpdjl-Uj93ZprvY6F8_p0VD</recordid><startdate>20210510</startdate><enddate>20210510</enddate><creator>Song, Jiazhi</creator><creator>Sharf, Inna</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20210510</creationdate><title>Stability Constrained Mobile Manipulation Planning on Rough Terrain</title><author>Song, Jiazhi ; Sharf, Inna</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a676-477ec87689267cf2bf4e781fbfcba87021b2388b6fd8dd96b92793636188ebfa3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Robotics</topic><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Song, Jiazhi</creatorcontrib><creatorcontrib>Sharf, Inna</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Song, Jiazhi</au><au>Sharf, Inna</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Stability Constrained Mobile Manipulation Planning on Rough Terrain</atitle><date>2021-05-10</date><risdate>2021</risdate><abstract>This paper presents a framework that allows online
dynamic-stability-constrained optimal trajectory planning of a mobile
manipulator robot working on rough terrain. First, the kinematics model of a
mobile manipulator robot, and the Zero Moment Point (ZMP) stability measure are
presented as theoretical background. Then, a sampling-based quasi-static
planning algorithm modified for stability guarantee and traction optimization
in continuous dynamic motion is presented along with a mathematical proof. The
robot's quasi-static path is then used as an initial guess to warm-start a
nonlinear optimal control solver which may otherwise have difficulties finding
a solution to the stability-constrained formulation efficiently. The
performance and computational efficiency of the framework are demonstrated
through an application to a simulated timber harvesting mobile manipulator
machine working on varying terrain. The results demonstrate feasibility of
online trajectory planning on varying terrain while satisfying the dynamic
stability constraint.</abstract><doi>10.48550/arxiv.2105.04396</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Computer Science - Artificial Intelligence Computer Science - Robotics Computer Science - Systems and Control |
| title | Stability Constrained Mobile Manipulation Planning on Rough Terrain |
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