Show simple item record

dc.contributor.authorPerez, Alejandro Tomas
dc.contributor.authorKaraman, Sertac
dc.contributor.authorShkolnik, Alexander C.
dc.contributor.authorFrazzoli, Emilio
dc.contributor.authorTeller, Seth
dc.contributor.authorWalter, Matthew R.
dc.date.accessioned2012-10-02T14:19:58Z
dc.date.available2012-10-02T14:19:58Z
dc.date.issued2011-12
dc.date.submitted2011-09
dc.identifier.isbn978-1-61284-454-1
dc.identifier.issn2153-0858
dc.identifier.urihttp://hdl.handle.net/1721.1/73541
dc.description.abstractA desirable property of path planning for robotic manipulation is the ability to identify solutions in a sufficiently short amount of time to be usable. This is particularly challenging for the manipulation problem due to the need to plan over high-dimensional configuration spaces and to perform computationally expensive collision checking procedures. Consequently, existing planners take steps to achieve desired solution times at the cost of low quality solutions. This paper presents a planning algorithm that overcomes these difficulties by augmenting the asymptotically-optimal RRT* with a sparse sampling procedure. With the addition of a collision checking procedure that leverages memoization, this approach has the benefit that it quickly identifies low-cost feasible trajectories and takes advantage of subsequent computation time to refine the solution towards an optimal one. We evaluate the algorithm through a series of Monte Carlo simulations of seven, twelve, and fourteen degree of freedom manipulation planning problems in a realistic simulation environment. The results indicate that the proposed approach provides significant improvements in the quality of both the initial solution and the final path, while incurring almost no computational overhead compared to the RRT algorithm. We conclude with a demonstration of our algorithm for single-arm and dual-arm planning on Willow Garage's PR2 robot.en_US
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionofhttp://dx.doi.org/10.1109/IROS.2011.6048640en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alike 3.0en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/en_US
dc.sourceMIT web domainen_US
dc.titleAsymptotically-optimal path planning for manipulation using incremental sampling-based algorithmsen_US
dc.typeArticleen_US
dc.identifier.citationPerez, Alejandro et al. “Asymptotically-optimal Path Planning for Manipulation Using Incremental Sampling-based Algorithms.” IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011. 4307–4313.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratoryen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Aeronautics and Astronauticsen_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Laboratory for Information and Decision Systemsen_US
dc.contributor.mitauthorPerez, Alejandro Tomas
dc.contributor.mitauthorKaraman, Sertac
dc.contributor.mitauthorShkolnik, Alexander C.
dc.contributor.mitauthorFrazzoli, Emilio
dc.contributor.mitauthorTeller, Seth
dc.contributor.mitauthorWalter, Matthew R.
dc.relation.journalProceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
dspace.orderedauthorsPerez, Alejandro; Karaman, Sertac; Shkolnik, Alexander; Frazzoli, Emilio; Teller, Seth; Walter, Matthew R.en
dc.identifier.orcidhttps://orcid.org/0000-0001-6365-6937
dc.identifier.orcidhttps://orcid.org/0000-0002-0505-1400
dc.identifier.orcidhttps://orcid.org/0000-0002-2225-7275
mit.licenseOPEN_ACCESS_POLICYen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record