An Automatic Grasp Planning System for Multi-fingered Robotic Hands

Xue, Zhixing; Rühl, Steffen W.; Zöllner, J. Marius; Dillmann, Rüdiger

doi:10.1007/978-3-642-25116-0_26

Cited by 19 publications

(20 citation statements)

References 16 publications

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“…Although robust grasp planning and execution approaches are available, they typically require precise shape and pose information to select a grasp in an offline optimization process [1], [2]. In order to acquire the necessary shape and pose information, traditional approaches typically employ a-priori knowledge about object models [1], [3], [4], [5], which is used for object recognition and the subsequent planning process.…”

Section: Introductionmentioning

confidence: 99%

Real-time 3D segmentation of cluttered scenes for robot grasping

Ückermann

Haschke

Ritter

2012

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

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Abstract-We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and partially occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. The real-time capabilities and the quality of the algorithm are evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of objects given by our algorithm in a task in which it cleans a table.

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Section: Introductionmentioning

confidence: 99%

Real-time 3D segmentation of cluttered scenes for robot grasping

Ückermann

Haschke

Ritter

2012

2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012)

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“…Values in [2,50] are good, while smaller value cause over-segmentation. Under-segmentation can be fixed by the (more costly) space partitioning.…”

Section: B Parameter Selectionmentioning

confidence: 99%

“…Although grasp planning approaches are available, they typically require precise shape and pose information to select a grasp in an offline optimization process [1], [2]. In order to acquire the necessary shape and pose information, traditional approaches typically employ a-priori knowledge about object models [1], [3], [4], [5], which is employed for object recognition and the subsequent planning process.…”

Section: Introductionmentioning

confidence: 99%

3D scene segmentation for autonomous robot grasping

Ückermann

Elbrechter

Haschke

et al. 2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-We present an algorithm to segment an unstructured table top scene. Operating on the depth image of a Kinect camera, the algorithm robustly separates objects of previously unknown shape in cluttered scenes of stacked and partially occluded objects. The model-free algorithm finds smooth surface patches which are subsequently combined to form object hypotheses. We evaluate the algorithm regarding its robustness and real-time capabilities and discuss its advantages compared to existing approaches as well as its weak spots to be addressed in future work. We also report on an autonomous grasping experiment with the Shadow Robot Hand which employs the estimated shape and pose of segmented objects.

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“…In the work of (Xue et al, 2009) the grasping of objects modeled in the 3D object modeling center (KIT Object Models Web Database) was presented. The center employs a digitizer, a turntable and a pair of RGB cameras mounted to a rotating bracket which allows for views from above the scene.…”

Section: Object Classificationmentioning

confidence: 99%

Combined 2D–3D categorization and classification for multimodal perception systems

Márton

Pangercic

Blodow

et al. 2011

The International Journal of Robotics Research

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This article describes an object perception system for autonomous robots performing everyday manipulation tasks in kitchen environments. The perception system gains its strengths by exploiting that the robots are to perform the same kinds of tasks with the same objects over and over again. It does so by learning the object representations necessary for the recognition and reconstruction in the context of pick and place tasks.The system employs a library of specialized perception routines that solve different, well-defined perceptual sub-tasks and can be combined into composite perceptual activities including the construction of an object model database, multi-modal object classification, and object model reconstruction for grasping.We evaluate the effectiveness of our methods, and give examples of application scenarios using our personal robotic assistants acting in a human living environment.

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An Automatic Grasp Planning System for Multi-fingered Robotic Hands

Cited by 19 publications

References 16 publications

Real-time 3D segmentation of cluttered scenes for robot grasping

Real-time 3D segmentation of cluttered scenes for robot grasping

3D scene segmentation for autonomous robot grasping

Combined 2D–3D categorization and classification for multimodal perception systems

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