Bounding on Rough Terrain with the LittleDog Robot
A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing...
| मुख्य लेखकों: | , , , |
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| अन्य लेखक: | |
| स्वरूप: | लेख |
| भाषा: | en_US |
| प्रकाशित: |
Sage
2011
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| ऑनलाइन पहुंच: | http://hdl.handle.net/1721.1/61974 https://orcid.org/0000-0002-8712-7092 |
| _version_ | 1826199377548410880 |
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| author | Shkolnik, Alexander C. Levashov, Michael Manchester, Ian R. Tedrake, Russell Louis |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Shkolnik, Alexander C. Levashov, Michael Manchester, Ian R. Tedrake, Russell Louis |
| author_sort | Shkolnik, Alexander C. |
| collection | MIT |
| description | A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays. |
| first_indexed | 2024-09-23T11:19:15Z |
| format | Article |
| id | mit-1721.1/61974 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T11:19:15Z |
| publishDate | 2011 |
| publisher | Sage |
| record_format | dspace |
| spelling | mit-1721.1/619742022-09-27T18:41:20Z Bounding on Rough Terrain with the LittleDog Robot Shkolnik, Alexander C. Levashov, Michael Manchester, Ian R. Tedrake, Russell Louis Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Tedrake, Russell Louis Shkolnik, Alexander C. Levashov, Michael Manchester, Ian R. Tedrake, Russell Louis A motion planning algorithm is described for bounding over rough terrain with the LittleDog robot. Unlike walking gaits, bounding is highly dynamic and cannot be planned with quasi-steady approximations. LittleDog is modeled as a planar five-link system, with a 16-dimensional state space; computing a plan over rough terrain in this high-dimensional state space that respects the kinodynamic constraints due to underactuation and motor limits is extremely challenging. Rapidly Exploring Random Trees (RRTs) are known for fast kinematic path planning in high-dimensional configuration spaces in the presence of obstacles, but search efficiency degrades rapidly with the addition of challenging dynamics. A computationally tractable planner for bounding was developed by modifying the RRT algorithm by using: (1) motion primitives to reduce the dimensionality of the problem; (2) Reachability Guidance, which dynamically changes the sampling distribution and distance metric to address differential constraints and discontinuous motion primitive dynamics; and (3) sampling with a Voronoi bias in a lower-dimensional “task space” for bounding. Short trajectories were demonstrated to work on the robot, however open-loop bounding is inherently unstable. A feedback controller based on transverse linearization was implemented, and shown in simulation to stabilize perturbations in the presence of noise and time delays. United States. Defense Advanced Research Projects Agency. Learning Locomotion Program (AFRL contract # FA8650-05-C-7262) 2011-03-25T20:06:36Z 2011-03-25T20:06:36Z 2010-12 Article http://purl.org/eprint/type/JournalArticle 1741-3176 0278-3649 http://hdl.handle.net/1721.1/61974 Shkolnik, Alexander et al. “Bounding On Rough Terrain With the LittleDog Robot.” The International Journal Of Robotics Research 30.2 (2011) : 192 -215. Copyright © 2011 by SAGE Publications https://orcid.org/0000-0002-8712-7092 en_US http://dx.doi.org/10.1177/0278364910388315 International Journal of Robotics Research Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Sage MIT web domain |
| spellingShingle | Shkolnik, Alexander C. Levashov, Michael Manchester, Ian R. Tedrake, Russell Louis Bounding on Rough Terrain with the LittleDog Robot |
| title | Bounding on Rough Terrain with the LittleDog Robot |
| title_full | Bounding on Rough Terrain with the LittleDog Robot |
| title_fullStr | Bounding on Rough Terrain with the LittleDog Robot |
| title_full_unstemmed | Bounding on Rough Terrain with the LittleDog Robot |
| title_short | Bounding on Rough Terrain with the LittleDog Robot |
| title_sort | bounding on rough terrain with the littledog robot |
| url | http://hdl.handle.net/1721.1/61974 https://orcid.org/0000-0002-8712-7092 |
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