Manipulation of deformable linear objects using knot invariants to classify the object condition based on image sensor information

Matsuno, Takayuki; Tamaki, D.; Arai, Fumihito; Fukuda, Toshio

doi:10.1109/tmech.2006.878557

Cited by 43 publications

(28 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Untangling knots requires reasoning about its knot structure. Matsuno et al [19] and Matsuno and Fukuda [18] focus on interpreting real-world knots as their mathematical counterparts, primarily using knot polynomials. Although knot polynomials are knot-invariant, they are not useful for real-world manipulations because they do not allow us to identify useful manipulation pointsthey are mainly used for knot isomorphism between two rope configurations.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Tangled: Learning to untangle ropes with RGB-D perception

Lui

Saxena

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

Abstract-In this paper, we address the problem of manipulating deformable objects such as ropes. Starting with an RGB-D view of a tangled rope, our goal is to infer its knot structure and then choose appropriate manipulation actions that result in the rope getting untangled. We design appropriate features and present an inference algorithm based on particle filters to infer the rope's structure. Our learning algorithm is based on max-margin learning. We then choose an appropriate manipulation action based on the current knot structure and other properties such as slack in the rope. We then repeatedly perform perception and manipulation until the rope is untangled. We evaluate our algorithm extensively on a dataset having five different types of ropes and 10 different types of knots. We then perform robotic experiments, in which our bimanual manipulator (PR2) untangles ropes successfully 76.9% of the time.

show abstract

Section: Related Workmentioning

confidence: 99%

“…al. [19] explore the manipulation of rope bodies, but their primary application is tying simple knots from a known starting state. Instead, we aim to start from an unknown state and infer the necessary rope configuration and properties to allow us to manipulate it and make progress towards an untangled state.…”

Section: Related Workmentioning

confidence: 99%

Tangled: Learning to untangle ropes with RGB-D perception

Lui

Saxena

2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

View full text Add to dashboard Cite

show abstract

“…Although motion planning based on topological relationships are emerging [10], [9], such methods always require the status of the objects to be projected onto a pre-defined 2D plane, which is inconvenient in case we want to plan the movements of the humanoid in 3D space. We will tackle this problem in this research.…”

Section: Previous Workmentioning

confidence: 99%

Planning tangling motions for humanoids

Komura

2007

2007 7th IEEE-RAS International Conference on Humanoid Robots

View full text Add to dashboard Cite

There are not much methods for humanoid robots to path-plan movements such as piggyback-carry, firefighter-lift or giving a shoulder to support the gait of another. Such methods are essential to apply humanoid robots for rescuing people in emergency, or for taking care of injured/elderly people in the hospital. It is difficult to plan such motions as they require the limbs of the humanoids to be tangled. In this paper, we propose a new method to plan such movements using a criteria based on the Gauss Linking Integral (GLI). The user first specifies the target configurations of the humanoids. The system then detects the tangles made between the bodies, and then plans how the humanoids should move to arrive to the target posture from separate configurations. The motion get entangled is planned by the Rapidly-exploring Random Trees (RRT) expansion. Using our method, it is possible to path-plan complex tangling motions which were difficult by previous methods.

show abstract

“…There are several examples of DLO [2][3][4] such as cables, hose and wires, which have different applications in industry [5], such as medical probes [6]. Models of DLO have been developed specifically to model rope manipulation to produce knots [7,8]. Vibration damping of DLO has been dealt with fuzzy control [9], and sliding mode control [10,11].…”

Section: Introductionmentioning

confidence: 99%