Gripping Point Determination for Bin Picking Using Heuristic Search

Spenrath, Felix; Pott, Andreas

doi:10.1016/j.procir.2016.06.015

Cited by 27 publications

(13 citation statements)

References 3 publications

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“…There is only a limited number of works focusing on learning-based approaches to robotic grasping in 6D for single objects [32•, 94-96] or in clutter [31, 33••, 34, 97]. While getting an increased focus in research, model-free grasping in 6D is especially relevant for picking objects from a cluttered bin [35], from a shelf [10], or for more robust grasps in general.…”

Section: Discussionmentioning

confidence: 99%

“…Model-based robotic grasping can be considered as a threestage process where first object poses are estimated, then a grasp pose is determined, and finally a collision-free and kinematically feasible path is planned towards the object to pick it [34,35]. This chapter focuses on the first part, which has the goal to estimate the translation and rotation relative to a given reference frame (usually the camera) of potentially multiple objects in the scene.…”

Section: Object Pose Estimation For Robotic Graspingmentioning

confidence: 99%

“…When utilizing object-specific knowledge, approaches typically require an object-specific configuration (high amount of manual tuning) until a satisfactory system performance is reached which limits the scalability to novel objects [5]. More specifically, parameters for the template or feature matching method for pose estimation [41,42] or the definition of robust grasp poses together with (static) priorities are required [35] and have to be tuned in real-world experiments. Therefore, model-based approaches aim for an automatic Model-free approaches (bottom row) directly determine grasp poses based on the observations given from the sensor.…”

Section: Object Pose Estimation For Robotic Graspingmentioning

confidence: 99%

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A Survey on Learning-Based Robotic Grasping

et al. 2020

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Purpose of Review This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provided. Recent Findings Model-free approaches are attractive due to their generalization capabilities to novel objects, but are mostly limited to top-down grasps and do not allow a precise object placement which can limit their applicability. In contrast, model-based methods allow a precise placement and aim for an automatic configuration without any human intervention to enable a fast and easy deployment. Summary Both approaches to robotic grasping and manipulation with and without object-specific knowledge are discussed. Due to the large amount of data required to train AI-based approaches, simulations are an attractive choice for robot learning. This article also gives an overview of techniques and achievements in transfers from simulations to the real world.

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Section: Discussionmentioning

confidence: 99%

Section: Object Pose Estimation For Robotic Graspingmentioning

confidence: 99%

Section: Object Pose Estimation For Robotic Graspingmentioning

confidence: 99%

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A Survey on Learning-Based Robotic Grasping

et al. 2020

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“…Some Methods 17,18] focus on identifying contact points which fulfill the wrench equilibrium without deriving shape information for the intersection. Li et al [19] calculated a grasp synthesis by shape matching the human hand with the form of the handling object based on a comparison of self-created feature sets. While this approach is suitable to find positions for a full overlapping, it is not applicable for partial overlappings.…”

Section: Gripping Point Determination and Intersection Detectionmentioning

confidence: 99%

Computing Gripping Points in 2D Parallel Surfaces Via Polygon Clipping

Vogt¹,

Zimmermann²,

Schilp

2022

Annals of Scientific Society for Assembly, Handling and Industrial Robotics 2021

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To generate suitable grasping positions between tessellated handling objects and specific planar grippers, we propose a 2D analytical approach which uses a polygon clipping algorithm to generate detailed information about the intersection between both objects. With the generated knowledge about the intersection we check whether its shape fits to the set criteria of the operator and represents a valid grasping position. Before the polygon clipping algorithm is applied, a preprocessing step is performed, where appropriate surfaces from the handling object and the gripper are extracted. After rotating all surfaces into a common plane, potential clipping positions are detected and the clipping is performed to get an accurate intersection detection. The validation shows comparable running times to a OBBTree algorithm (0.1 ms per grasping position) while increasing the stability of the results from 30 to 100% for the evaluated test objects.

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“…the finger distance, for each graspable stable equilibrium, which is selected for grasping. The design process is manual and geometry-based (Spenrath and Pott, 2017) and complies with the basic principles of grasping presented in (Fantoni et al, 2014). This process focuses on avoiding any collisions between the gripper geometry (including the finger, but ignoring the robot) and the obstacles formed by the light table (gripper in GP) and the fixture (gripper in PP).…”

Section: Grasp Planningmentioning

confidence: 99%

Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

Tipary

Kovács

Erdős

2021

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Purpose The purpose of this paper is to give a comprehensive solution method for the manipulation of parts with complex geometries arriving in bulk into a robotic assembly cell. As bin-picking applications are still not reliable in intricate workcells, first, the problem is transformed to a semi-structured pick-and-place application, then by collecting and organizing the required process planning steps, a methodology is formed to achieve reliable factory applications even in crowded assembly cell environments. Design/methodology/approach The process planning steps are separated into offline precomputation and online planning. The offline phase focuses on preparing the operation and reducing the online computational burdens. During the online phase, the parts laying in a semi-structured arrangement are first recognized and localized based on their stable equilibrium using two-dimensional vision. Then, the picking sequence and corresponding collision-free robot trajectories are planned and optimized. Findings The proposed method was evaluated in a geometrically complex experimental workcell, where it ensured precise, collision-free operation. Moreover, the applied planning processes could significantly reduce the execution time compared to heuristic approaches. Research limitations/implications The methodology can be further generalized by considering multiple part types and grasping modes. Additionally, the automation of grasp planning and the enhancement of part localization, sequence planning and path smoothing with more advanced solutions are further research directions. Originality/value The paper proposes a novel methodology that combines geometrical computations, image processing and combinatorial optimization, adapted to the requirements of flexible pick-and-place applications. The methodology covers each required planning step to reach reliable and more efficient operation.

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Gripping Point Determination for Bin Picking Using Heuristic Search

Cited by 27 publications

References 3 publications

A Survey on Learning-Based Robotic Grasping

A Survey on Learning-Based Robotic Grasping

Computing Gripping Points in 2D Parallel Surfaces Via Polygon Clipping

Planning and optimization of robotic pick-and-place operations in highly constrained industrial environments

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