Hand eye coordination in rope handling
Open accessAbstract
本論文は, ロボットにより紐を操作する際の視覚・行動系の協調動作に関する研究について述べたものである.紐のように柔構造の物体を操作するには視覚情報の利用が必要不可欠である.本研究では視覚情報を利用して動作を修正し, 動作結果をもとに注視する領域を制御し動作確認をするという協調動作の作業例として紐のハンドリングをとりあげる.本論文では, 視覚・行動系の協調動作の実現方法を解析し, 紐のハンドリング実験に用いたハンド・アイ・システムと視覚処理アルゴリズムについて述べる.協調動作を実現するための手法として, 局所領域を注視する視覚機能, 座標系間の較正, 視覚による動作確認, 視覚による操作量の修正法について述べる.紐の操作実験は紐をつかんでリングに通したあと, 紐をつかみ直して結ぶという一連の動作からなる.実験システムは, 両眼立体視システム, 6+1自由度アームシステム, 作業記述用のLispシステムからなる.線分領域の画像処理を組合せて対象を認識し, 両眼立体視によって3次元計測を行ない環境の認識と動作の制御を実現している.視覚により得られる情報を基に操作量を修正し, 動作の確認を視覚により行なうことによって, 柔軟な紐を3次元空間で操作することができるということを実験により確認した.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Controllers developed under classical robotics typically only apply to a fixed or a narrow range of configurations.
“Controllers developed under classical robotics typically only apply to a fixed or a narrow range of configurations [49,50].”
From Data-Driven Robotic Manipulation of Cloth-like Deformable Objects: The Present, Challenges and Future Prospects (2023) · cites this paper as [49]
Section 2.5 (p. 542) explains why the authors use visual feedback to adjust a rope-handling motion, and Section 4.6 (p. 546) describes updating the guide path as the ring’s position changes. Neither passage compares classical robotics controllers with other approaches or says they typically work only across a fixed or narrow range of configurations. Searches for that comparison found no supporting passage in our copy. (LLM, conf=0.76)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Pattern classification and scene analysis
Duda, R. O., & Hart, P. E. (1973) · CERN Document Server (European Organization for Nuclear Research) · Cited by 12,895 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Learning from Demonstrations Through the Use of Non-rigid Registration
Schulman, J., Ho, J., Lee, C., & Abbeel, P. (2016) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-28872-7_20 · Cited by 133 · see citing works
Knotting/unknotting manipulation of deformable linear objects
Wakamatsu, H., Arai, E., & Hirai, S. (2006) · The International Journal of Robotics Research · doi:10.1177/0278364906064819 · Cited by 132 · see citing works
Learning force-based manipulation of deformable objects from multiple demonstrations
Lee, A. X., Lu, H., Gupta, A., Levine, S., & Abbeel, P. (2015) · 2015 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2015.7138997 · Cited by 140 · see citing works
A case study of flexible object manipulation
Hopcroft, J. E., Kearney, J. K., & Krafft, D. B. (1991) · The International Journal of Robotics Research · doi:10.1177/027836499101000105 · Cited by 82 · see citing works
Motion planning for dynamic knotting of a flexible rope with a high-speed robot arm
Yamakawa, Y., Namiki, A., & Ishikawa, M. (2010) · 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2010.5651168 · Cited by 57 · see citing works
In-air Knotting of Rope using Dual-Arm Robot based on Deep Learning
Suzuki, K., Kanamura, M., Suga, Y., Mori, H., & Ogata, T. (2021) · 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635954 · Cited by 31 · see citing works
Knotting manipulation of a flexible rope by a multifingered hand system based on skill synthesis
Yamakawa, Y., Namiki, A., Ishikawa, M., & Shimojo, M. (2008) · 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2008.4650802 · Cited by 29 · see citing works
In-air Knotting of Rope by a Dual-arm Multi-finger Robot
Kudoh, S., Gomi, T., Katano, R., Tomizawa, T., & Suehiro, T. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7354262 · Cited by 23 · see citing works
A new strategy for making a knot with a general-purpose arm
Vinh, T. V., Tomizawa, T., Kudoh, S., & Suehiro, T. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6224852 · Cited by 21 · see citing works
Kadi, H. A., & Terzić, K. (2023) · Sensors · doi:10.3390/s23052389 · Cited by 19 · see citing works
“Controllers developed under classical robotics typically only apply to a fixed or a narrow range of configurations [49,50].”
Background
- Authors
- INABA, M., & INOUE, H.
- Affiliations
- The University of Tokyo (JP)
- ORCID
- INABA, Masayuki
- Venue
- Journal of the Robotics Society of Japan
- Publisher
- The Robotics Society of Japan
- Published
- 1985
- Volume
- 3
- Issue
- 6
- Pages
- 538-547
- ISSN
- 0289-1824 (ISSN-L)
- 1884-7145
- OpenAlex
- W1977030886
- Cited by
- 95
- References
- 1
- Citations by year
- 2023: 2 · 2022: 2 · 2021: 5 · 2020: 1 · 2019: 7 · 2017: 1 · 2016: 6 · 2015: 9 · 2014: 2 · 2013: 3 · 2012: 6
- FWCI
- 2.47
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of the Robotics Society of Japan (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Engineering › Mechanical Engineering › Teleoperation and Haptic Systems
- Keywords
- eye-hand coordination, rope handling, visual servoing, visual feedback control, flexible object manipulation, stereo vision, 3D measurement, coordinate system calibration, visual object recognition, robotic arm control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
