Fast Grasp Planning Using Cord Geometry

Li, Yang; Saut, Jean-Philippe; Pettré, Julien; Sahbani, Anis; Multon, Franck

doi:10.1109/tro.2015.2492863

Cited by 11 publications

(14 citation statements)

References 32 publications

(65 reference statements)

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“…Shi et al [21] considered environmental constraints as well as the kinematic constraints of robot hands to plan accessible grasps for bin-picking and kitting tasks. Li et al [22] used stretching ropes (cord geometry) to find the contact of a hand jaw with object surfaces and hence plan the grasps. Ciocarlie et al [23] considered local geometry and structures at contact points and modeled friction forces using soft models.…”

Section: A Grasp Theories and Grasp Planningmentioning

confidence: 99%

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

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This article develops model-based grasp planning algorithms. It focuses on industrial end-effectors like grippers and suction cups, and plans grasp configurations considering computer aided design (CAD) models of target objects. The developed algorithms can stably find many high-quality grasps, with satisfying precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which preprocesses a mesh model by peeling it into superimposed facets. The algorithms use the facets to locate contacts and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters are prepared to adapt the algorithms to meet various requirements. The experimental section studies the influence of the tunable parameters and analyzes the cost, precision, and robustness of the proposed algorithms and their planned grasps, with both simulations and real-world systems. Besides, the proposed algorithms are applicable to mesh models reconstructed from point clouds obtained by depth sensors. Some experiments and analysis are also carried out to study and demonstrate the ability. Index Terms-Grasping, grippers and other end-effectors, manipulation planning. I. INTRODUCTIONT HIS article develops algorithms to plan grasping configurations automatically. It focuses on industrial end-effectors, and plans grasp poses for these end-effectors considering computer aided design (CAD) models of target objects.Developing grasp planning algorithms is vital to manufacturing using "teachingless" robotic manipulators. Modern robotic manipulation systems use manually annotated or pretaught grasp configurations to perform specific tasks, which is not only costly Manuscript

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Section: A Grasp Theories and Grasp Planningmentioning

confidence: 99%

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

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“…Another grasp planner for power grasp (i.e., enveloping grasp) based on object surface matching was presented in [11], where the best points for opposing grasp using opposing fingers were found by matching local curvature of the object surface. Li et al [12] proposed a novel geometric algorithm to find enveloping grasp configurations for a multifingered hand. The proposed method performs a low-level shape matching by tightly wrapping multiple cords around an object to quickly isolate potential grasping regions.…”

Section: Related Workmentioning

confidence: 99%

“…Then heuristics were used to select graspable box faces and finally, an off-line trained neural network gives the best grasp hypothesis. Li et al [12] employed deterministic sampling on the spherical surface constructed around the object to find the initial hand position and approach direction. Shape diameter function (SDF) was used by Vahrenkamp et al [26] to segment objects into parts and then principal component analysis was applied on the parts to align the hand with the corresponding object part.…”

Section: Related Workmentioning

confidence: 99%

“…In this sub-section, a comprehensive comparison with the existing state-of-the-art including recent work has been presented. Three grasp planners [9,11] and [12] have been found to be most relevant and chosen for evaluating the proposed grasp planner quantitatively and qualitatively. The computation time and the grasp quality measure described in Sect.…”

Section: Comparative Evaluation With the State-of-the-artmentioning

confidence: 99%

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A novel object slicing-based grasp planner for unknown 3D objects

Ansary

Deb

2021

Intel Serv Robotics

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Robotic grasp planning has been one of the active areas of research in robotics but still remains a challenging problem for arbitrary objects even in completely known environments. Most previously developed algorithms had focused on precision/fingertip type of grasps, failing to solve the problem even for fully actuated hands/grippers during enveloping/adaptive/wrapping/power type of grasps, where each finger makes contact with an object at several points. Kinematic closed-form solutions are not possible for such an articulated finger, which simultaneously reaches several given goal points. This paper presents a framework for computing the best grasp for robotic hands/grippers, based on a novel object slicing method. The proposed method quickly finds contacts using an object slicing technique and uses a grasp quality measure to find the best grasp from a pool of pre-grasps. The pool of pre-grasps is generated by dividing the objects into parts and organizing them in a decomposition tree structure, where the parts are approximated by simple box primitives. To validate the proposed method, the developed grasp planner has been implemented on an industrial Motoman robot and a two-finger gripper. Further, the results have been compared with the state-of-the-art in grasp planning to evaluate the performance of the proposed grasp planner. As compared to other existing approaches, the proposed approach has several advantages to offer. It can handle objects with complex shapes and sizes. Most importantly, it works on both point clouds taken using a depth sensor and polygonal mesh models. It takes into account hand constraints and generates feasible grasps for both adaptive/enveloping and fingertip type of grasps. KeywordsGrasp planner • Spatial search trees • Grasp quality metric • Underactuated robotic hand/gripper B Sainul Islam Ansary

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“…Tsuji et al 7 combine the above two methods, select the constricted region of the object as the grasp interest region, and fit the local model of the object near the grasp interest region to the grasp primitives. Li et al 8 wrap ropes around the object to find possible grasp regions and then computes the contacts of a multifingered hand with object surfaces around these regions. Wan et al 9 apply superimposed segmentation to the object mesh model and use the uniform facets to locate contacts and generate grasp poses for grippers and suction cups.…”

Section: Introductionmentioning

confidence: 99%

Planning for grasping cluttered objects based on obstruction degree

Zhao

Wang

Chen

et al. 2021

International Journal of Advanced Robotic Systems

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Grasping objects in clutter is more difficult than grasping a separated single object. An important issue is that unsafe grasps may occur, in case, one object sits or leans on another, which could cause the collapse of objects. In addition, reachability of each object surrounded by other obstacles also has to be considered. So the order of multiple objects for grasping and the grasp configuration of each object must be planned simultaneously. This article combines grasp order and grasp configuration planning to perform fast and safe multiobject grasping in cluttered scenes. First, a comprehensive grasp configuration database is built to provide enough feasible grasp configurations for the objects. Then, we propose an obstruction degree to estimate the likelihood of reachability of each grasp configuration as well as each object. This measurement also implicitly infers object interactions. Finally, grasp order and grasp configurations are planned together to deal with the constraints caused by reachability and object interaction. Simulations and experiments in a series of cluttered scenes demonstrate that our method can grasp objects efficiently and can greatly reduce unsafe grasps.

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Fast Grasp Planning Using Cord Geometry

Cited by 11 publications

References 32 publications

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

A novel object slicing-based grasp planner for unknown 3D objects

Planning for grasping cluttered objects based on obstruction degree

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