Handle URI:
http://hdl.handle.net/10754/599200
Title:
Planning with Reachable Distances
Authors:
Tang, Xinyu; Thomas, Shawna; Amato, Nancy M.
Abstract:
Motion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robot's degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the robot's number of degrees of freedom. In addition to supporting efficient sampling, we show that the RD-space formulation naturally supports planning, and in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1000 links in time comparable to open chain sampling, and we can generate samples for 1000-link multi-loop systems of varying topology in less than a second. © 2009 Springer-Verlag.
Citation:
Tang X, Thomas S, Amato NM (2009) Planning with Reachable Distances. Algorithmic Foundation of Robotics VIII: 517–531. Available: http://dx.doi.org/10.1007/978-3-642-00312-7_32.
Publisher:
Springer Science + Business Media
Journal:
Algorithmic Foundation of Robotics VIII
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
2009
DOI:
10.1007/978-3-642-00312-7_32
Type:
Book Chapter
ISSN:
1610-7438; 1610-742X
Sponsors:
The work of X. Tang was done when he was a Ph.D. student in the Department of Computer Science and Engineering at Texas A&M University. This research supported in part by NSF Grants EIA-0103742, ACR-0113971, CCR-0113974, ACI-0326350, CRI-0551685, CCF-0833199, CCF-0830753, by Chevron, IBM, Intel, HP, and by King Abdullah University of Science and Technology (KAUST) Award KUS-C1-016-04. Thomas supported in part by an NSF Graduate Research Fellowship, a PEO Scholarship, a Department of Education Graduate Fellowship (GAANN), and an IBM TJ Watson PhD Fellowship.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorTang, Xinyuen
dc.contributor.authorThomas, Shawnaen
dc.contributor.authorAmato, Nancy M.en
dc.date.accessioned2016-02-25T13:54:46Zen
dc.date.available2016-02-25T13:54:46Zen
dc.date.issued2009en
dc.identifier.citationTang X, Thomas S, Amato NM (2009) Planning with Reachable Distances. Algorithmic Foundation of Robotics VIII: 517–531. Available: http://dx.doi.org/10.1007/978-3-642-00312-7_32.en
dc.identifier.issn1610-7438en
dc.identifier.issn1610-742Xen
dc.identifier.doi10.1007/978-3-642-00312-7_32en
dc.identifier.urihttp://hdl.handle.net/10754/599200en
dc.description.abstractMotion planning for spatially constrained robots is difficult due to additional constraints placed on the robot, such as closure constraints for closed chains or requirements on end effector placement for articulated linkages. It is usually computationally too expensive to apply sampling-based planners to these problems since it is difficult to generate valid configurations. We overcome this challenge by redefining the robot's degrees of freedom and constraints into a new set of parameters, called reachable distance space (RD-space), in which all configurations lie in the set of constraint-satisfying subspaces. This enables us to directly sample the constrained subspaces with complexity linear in the robot's number of degrees of freedom. In addition to supporting efficient sampling, we show that the RD-space formulation naturally supports planning, and in particular, we design a local planner suitable for use by sampling-based planners. We demonstrate the effectiveness and efficiency of our approach for several systems including closed chain planning with multiple loops, restricted end effector sampling, and on-line planning for drawing/sculpting. We can sample single-loop closed chain systems with 1000 links in time comparable to open chain sampling, and we can generate samples for 1000-link multi-loop systems of varying topology in less than a second. © 2009 Springer-Verlag.en
dc.description.sponsorshipThe work of X. Tang was done when he was a Ph.D. student in the Department of Computer Science and Engineering at Texas A&M University. This research supported in part by NSF Grants EIA-0103742, ACR-0113971, CCR-0113974, ACI-0326350, CRI-0551685, CCF-0833199, CCF-0830753, by Chevron, IBM, Intel, HP, and by King Abdullah University of Science and Technology (KAUST) Award KUS-C1-016-04. Thomas supported in part by an NSF Graduate Research Fellowship, a PEO Scholarship, a Department of Education Graduate Fellowship (GAANN), and an IBM TJ Watson PhD Fellowship.en
dc.publisherSpringer Science + Business Mediaen
dc.titlePlanning with Reachable Distancesen
dc.typeBook Chapteren
dc.identifier.journalAlgorithmic Foundation of Robotics VIIIen
dc.contributor.institutionGoogle Inc., Mountain View, United Statesen
dc.contributor.institutionTexas A and M University, College Station, United Statesen
kaust.grant.numberKUS-C1-016-04en
All Items in KAUST are protected by copyright, with all rights reserved, unless otherwise indicated.