While robotic manipulation of rigid objects is quite straightforward, coping with deformable objects is an open issue. More specifically, tasks like tying a knot, wiring a connector or even surgical suturing deal with the domain of Deformable Linear Objects (DLOs). In particular the detection of a DLO is a non-trivial problem especially under clutter and occlusions (as well as self-occlusions). The pose estimation of a DLO results into the identification of its parameters related to a designed model, e.g. a basis spline. It follows that the stand-alone segmentation of a DLO might not be sufficient to conduct a full manipulation task. This is why we propose a novel framework able to perform both a semantic segmentation and b-spline modeling of multiple deformable linear objects simultaneously without strict requirements about environment (i.e. the background). The core algorithm is based on biased random walks over the Region Adiacency Graph built on a superpixel oversegmentation of the source image. The algorithm is initialized by a Convolutional Neural Networks that detects the DLO’s endcaps. An open source implementation of the proposed approach is also provided to easy the reproduction of the whole detection pipeline along with a novel cables dataset in order to encourage further experiments.
As far as are concerned, visual perception is typically addressed in fairly simple settings. In @cite_31 markers are deployed to track end-points. In other works, like @cite_28 and @cite_3 , detection relies on background removal In @cite_3 the authors deal with a thin flexible manipulator which may be described as a cable due to the high number of degrees of freedom. .
@cite_28 @cite_31 @cite_3
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This paper deals with the problem of picking-up deformable linear workpieces such as cables or ropes with an industrial robot. First, we give a motivation and problem definition. Based on a brief conceptual discussion of possible approaches we derive an algorithm for picking-up hanging deformable linear objects using two light barriers as sensor system. For this hardware, a skill-based approach is described and the parameters and major influence factors are discussed. In an experi- mental study, the feasibility and reliability under diverse conditions are investigated. The algorithm is found to be very reliable, if certain boundary conditions are met. This paper addresses an engineering attempt of utilizing multiple robot arms in the assembly of deformable parts. The robot system developed in this research simulates a practically existing assembly process in an automobile plant where wrapped cables (wire harness) have to be fixed on the body of a car. This operation is performed by skilled workers and is considered to be difficult for automation. In this paper, a synthetic robotized solution is proposed for this process. For the first time, it realizes robotized assembly of a complex deformable object (wire harness) under a condition similar to an actual plant. Although some of the problems are simplified in the implementation, those critical to assemblies involving deformable objects are investigated and solved with experimental validation. Therefore, this research is expected to contribute to exploring the feasibility and potential of robotized assembly using deformable parts in actual plants. Rigid robotic manipulators employ traditional sensors such as encoders or potentiometers to measure joint angles and determine end-effector position. Manipulators that are flexible, however, introduce motions that are much more difficult to measure. This is especially true for continuum manipulators that articulate by means of material compliance. In this paper, we present a vision based system for quantifying the 3-D shape of a flexible manipulator in real-time. The sensor system is validated for accuracy with known point measurements and for precision by estimating a known 3-D shape. We present two applications of the validated system relating to the open-loop control of a tendon driven continuum manipulator. In the first application, we present a new continuum manipulator model and use the sensor to quantify 3-D performance. In the second application, we use the shape sensor system for model parameter estimation in the absence of tendon tension information.