Grasp planning for everyday objects based on primitive shape representation for parallel jaw grippers

Yamanobe, Natsuki; Nagata, Kazuyuki

doi:10.1109/robio.2010.5723563

Cited by 23 publications

(25 citation statements)

References 8 publications

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“…[17] use shape primitives for grasp selection but also require known models of the object. Yamanobe and Nagata [18] assign various primitives by hand. Huebner et al .…”

Section: Related Workmentioning

confidence: 99%

Grasp detection for assistive robotic manipulation

Jain

Argall

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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In this paper, we present a novel grasp detection algorithm targeted towards assistive robotic manipulation systems. We consider the problem of detecting robotic grasps using only the raw point cloud depth data of a scene containing unknown objects, and apply a geometric approach that categorizes objects into geometric shape primitives based on an analysis of local surface properties. Grasps are detected without a priori models, and the approach can generalize to any number of novel objects that fall within the shape primitive categories. Our approach generates multiple candidate object grasps, which moreover are semantically meaningful and similar to what a human would generate when teleoperating the robot—and thus should be suitable manipulation goals for assistive robotic systems. An evaluation of our algorithm on 30 household objects includes a pilot user study, confirms the robustness of the detected grasps and was conducted in real-world experiments using an assistive robotic arm.

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“…[17] use shape primitives for grasp selection but also require known models of the object. Yamanobe and Nagata [18] assign various primitives by hand. Huebner et al .…”

Section: Related Workmentioning

confidence: 99%

Grasp detection for assistive robotic manipulation

Jain

Argall

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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“…The number of parameters, however, can be reduced via grasp parameterization. An example of grasp parameterization can be found in [20].…”

Section: A Mathematical Definitionsmentioning

confidence: 99%

A Single-Query Manipulation Planner

Lertkultanon

Pham

2016

IEEE Robot. Autom. Lett.

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In manipulation tasks, a robot interacts with movable object(s). The configuration space in manipulation planning is thus the Cartesian product of the configuration space of the robot with those of the movable objects. It is the complex structure of such a "composite configuration space" that makes manipulation planning particularly challenging. Previous works approximate the connectivity of the composite configuration space by means of discretization or by creating random roadmaps. Such approaches involve an extensive pre-processing phase, which furthermore has to be re-done each time the environment changes. In this paper, we propose a high-level Grasp-Placement Table similar to that proposed by Tournassoud et al. (1987), but which does not require any discretization or heavy pre-processing. The table captures the potential connectivity of the composite configuration space while being specific only to the movable objects: in particular, it does not require to be re-computed when the environment changes. During the query phase, the table is used to guide a tree-based planner that explores the space systematically. Our simulations and experiments show that the proposed method enables improvements in both running time and trajectory quality as compared to existing approaches.

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“…Miller [3] proposed a method that uses spheres, cylinders, cones and boxes as shape primitives. Yamanobe et al [4] proposed a method that uses various primitive shapes such as spheres, cylinders, cones, boxes, tubular boxes, and tubular cylinders. Harada et al [5] proposed a method where objects are approximated 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014) September 14-18, 2014, Chicago, IL, USA using a plane.…”

Section: Related Workmentioning

confidence: 99%

Grasp planning for constricted parts of objects approximated with quadric surfaces

Tsuji

Uto

Harada

et al. 2014

2014 IEEE/RSJ International Conference on Intelligent Robots and Systems

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This paper presents a grasp planner which allows a robot to grasp the constricted parts of objects in our daily life. Even though constricted parts can be grasped more firmly than convex parts, previous planners have not sufficiently focused on grasping this part. We develop techniques for quadric surface approximation, grasp posture generation, and stability evaluation for grasping constricted parts. By modeling an object into multiple quadric surfaces, the planner generates a grasping posture by selecting one-sheet hyperbolic surfaces or two adjacent ellipsoids as constricted parts. When a grasping posture being generated, the grasp stability is evaluated based on the distribution of the stress applied to an object by the fingers. We perform several simulations and experiments to verify the effectiveness of our proposed method.

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Grasp planning for everyday objects based on primitive shape representation for parallel jaw grippers

Cited by 23 publications

References 8 publications

Grasp detection for assistive robotic manipulation

Grasp detection for assistive robotic manipulation

A Single-Query Manipulation Planner

Grasp planning for constricted parts of objects approximated with quadric surfaces

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