Learning Based Robotic Bin-picking for Potentially Tangled Objects

Matsumura, Ryo; Domae, Yukiyasu; Wan, Weiwei; Harada, Kensuke

doi:10.1109/iros40897.2019.8968295

Cited by 42 publications

(27 citation statements)

References 19 publications

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“…Instead of simply dropping the picked object, many practical applications require an at least semi-precise or gentle placement of the components, which has been addressed less. While solutions for avoiding the entanglement of objects exist [99,100], no general solution has been proposed to unhook complex object geometries.…”

Section: Discussionmentioning

confidence: 99%

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%

A Survey on Learning-Based Robotic Grasping

et al. 2020

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“…Empirical methods include learning-based techniques, which rely on previously acquired knowledge, and heuristic techniques, which use directly a representation of the scene. Matsumura et al used depth images to compute a set of grasp candidates and trained a Convolutional Neural Network (CNN) using simulated examples to determine if grasping a given candidate and applying a lifting motion leads to entanglement [2]. To the best of the authors' knowledge, this is the only other paper that explicitly deals with random bin picking of entangled objects.…”

Section: Related Workmentioning

confidence: 99%

“…Handling entangled objects is all the more challenging as multiple objects may be inadvertently picked at once, rather than a single one, which can cause disruptions in industrial lines. In a recent publication by Matsumura et al [2], they claimed to be the first ones to present practical experiments of picking entangled objects from a pile.…”

Section: Introductionmentioning

confidence: 99%

Detecting and Solving Tube Entanglement in Bin Picking Operations

et al. 2020

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Manufacturing and production industries are increasingly turning to robots to carry out repetitive picking operations in an efficient manner. This paper focuses on tackling the novel challenge of automating the bin picking process for entangled objects, for which there is very little research. The chosen case study are sets of freely curved tubes, which are prone to occlusions and entanglement. The proposed algorithm builds a representation of the tubes as an ordered list of cylinders and joints using a point cloud acquired by a 3D scanner. This representation enables the detection of occlusions in the tubes. The solution also performs grasp planning and motion planning, by evaluating post-grasp trajectories via simulation using Gazebo and the ODE physics engine. A force/torque sensor is used to determine how many items were picked by a robot gripper and in which direction it should rotate to solve cases of entanglement. Real-life experiments with sets of PVC tubes and rubber radiator hoses showed that the robot was able to pick a single tube on the first try with success rates of 99% and 93%, respectively. This study indicates that using simulation for motion planning is a promising solution to deal with entangled objects.

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“…Grasping point detection algorithms [15][16][17] are robust methods to find graspable points from cluttered scenes. Fast Graspability Evaluation (FGE) [17] is a practical-use method that finds graspable points by convolving the 2D gripper model and the parts surfaces on a depth map.…”

Section: Kitting Task Proceduresmentioning

confidence: 99%

Team O2AS at the world robot summit 2018: an approach to robotic kitting and assembly tasks using general purpose grippers and tools

et al. 2020

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In this article, we propose a versatile robotic system for kitting and assembly tasks which uses no jigs or commercial tool changers. Instead of specialized end effectors, it uses its two-finger grippers to grasp and hold tools to perform subtasks such as screwing and suctioning. A third gripper is used as a precision picking and centering tool, and uses in-built passive compliance to compensate for small position errors and uncertainty. A novel grasp point detection for bin picking is described for the kitting task, using a single depth map. Using the proposed system we competed in the Assembly Challenge of the Industrial Robotics Category of the World Robot Challenge at the World Robot Summit 2018, obtaining 4th place and the SICE award for lean design and versatile tool use. We show the effectiveness of our approach through experiments performed during the competition.

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Learning Based Robotic Bin-picking for Potentially Tangled Objects

Cited by 42 publications

References 19 publications

A Survey on Learning-Based Robotic Grasping

A Survey on Learning-Based Robotic Grasping

Detecting and Solving Tube Entanglement in Bin Picking Operations

Team O2AS at the world robot summit 2018: an approach to robotic kitting and assembly tasks using general purpose grippers and tools

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