Deformable One-Dimensional Object Detection for Routing and Manipulation

Keipour, Azarakhsh; Bandari, Maryam; Schaal, Stefan

doi:10.1109/lra.2022.3146920

Cited by 19 publications

(14 citation statements)

References 29 publications

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“…At each step, we used the DOO detection algorithm proposed in [8] for automatically detecting the cable and extracting its configuration on the circuit board. Figures 6(b…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The actual DOO state is continuous and needs to be converted to a discrete spatial representation for robotics and computer applications. Common different ways of such representation include sampling equidistant points along the DOO's medial axis, fixed-length B-splines, and fixed-length cylinders connected by spherical joints [3], [8].…”

Section: Problem Definitionmentioning

confidence: 99%

“…[maryamb, sschaal]@intrinsic.ai representations range from spring-mass models [5] to linear combination of curves [6], [7], and fixed-length segments [8].…”

Section: Introductionmentioning

confidence: 99%

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Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation

Keipour¹,

Bandari²,

Schaal³

2022

Preprint

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With the field of rigid-body robotics having matured in the last fifty years, routing, planning, and manipulation of deformable objects have emerged in recent years as a more untouched research area in many fields ranging from surgical robotics to industrial assembly and construction. Routing approaches for deformable objects which rely on learned implicit spatial representations (e.g., Learning-from-Demonstration methods) make them vulnerable to changes in the environment and the specific setup. On the other hand, algorithms that entirely separate the spatial representation of the deformable object from the routing and manipulation, often using a representation approach independent of planning, result in slow planning in high dimensional space.This paper proposes a novel approach to spatial representation combined with route planning that allows efficient routing of deformable one-dimensional objects (e.g., wires, cables, ropes, threads). The spatial representation is based on the geometrical decomposition of the space into convex subspaces, which allows an efficient coding of the configuration. Having such a configuration, the routing problem can be solved using a dynamic programming matching method with a quadratic time and space complexity. The proposed method couples the routing and efficient configuration for improved planning time. Our tests and experiments show the method correctly computing the next manipulation action in sub-millisecond time and accomplishing various routing and manipulation tasks.

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“…At each step, we used the DOO detection algorithm proposed in [8] for automatically detecting the cable and extracting its configuration on the circuit board. Figures 6(b…”

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Problem Definitionmentioning

confidence: 99%

See 1 more Smart Citation

Efficient Spatial Representation and Routing of Deformable One-Dimensional Objects for Manipulation

Keipour¹,

Bandari²,

Schaal³

2022

Preprint

Self Cite

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“…This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ In the past, the problem of DLO identification has been solved in simple settings: in [6] the authors required the presence of a single DLO in the scene and its segmentation was based on a color threshold with a controlled background; in [7] a good contrast between the background and the DLO is again assumed; in [8] a threshold is again applied in a controlled background to segment the cable. Indeed, the major difficulties in DLO identification rely on its simplicity, which does not offer distinctive features to be used for an unambiguous detection.…”

Section: Related Workmentioning

confidence: 99%

“…Indeed, the major difficulties in DLO identification rely on its simplicity, which does not offer distinctive features to be used for an unambiguous detection. Moreover, the approach proposed in [6] assumes to deal with just a single DLO in the scene. These assumptions about segmentation capability and number of expected instances limit the applicability of the proposed solutions in real-case scenario where DLOs are commonly involved.…”

Section: Related Workmentioning

confidence: 99%

FASTDLO: Fast Deformable Linear Objects Instance Segmentation

Caporali

Galassi

Zanella

et al. 2022

IEEE Robot. Autom. Lett.

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In this paper, an approach for fast and accurate segmentation of Deformable Linear Objects (DLOs) named FASTDLO is presented. A deep convolutional neural network is employed for background segmentation, generating a binary mask that isolates DLOs in the image. Thereafter, the obtained mask is processed with a skeletonization algorithm and the intersections between different DLOs are solved with a similarity-based network. Apart from the usual pixel-wise color-mapped image, FASTDLO also describes each DLO instance with a sequence of 2D coordinates, enabling the possibility of modeling the DLO instances with splines curves, for example. Synthetically generated data are exploited for the training of the data-driven methods, avoiding expensive collection and annotations of real data. FASTDLO is experimentally compared against both a DLO-specific approach and general-purpose deep learning instance segmentation models, achieving better overall performances and a processing rate higher than 20 FPS.

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