Fast geometry-based computation of grasping points on three-dimensional point clouds

Zapata-Impata, Brayan S.; Gil, Pablo; Pomares, Jorge; Torres, Fernando

doi:10.1177/1729881419831846

Cited by 42 publications

(22 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…4. GeoGrasp [42], [43] was used to compute grasping points on the 3D point clouds. This method finds grasps on unknown objects using a 3D point cloud with a partial view, which fits our setup.…”

Section: E Data Collectionmentioning

confidence: 99%

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Zapata-Impata

Gil

Mezouar

et al. 2021

IEEE Trans. Haptics

Self Cite

View full text Add to dashboard Cite

Tactile perception is a rich source of information for robotic grasping: it allows a robot to identify a grasped object and assess the stability of a grasp, among other things. However, the tactile sensor must come into contact with the target object in order to produce readings. As a result, tactile data can only be attained if a real contact is made. We propose to overcome this restriction by employing a method that models the behaviour of a tactile sensor using 3D vision and grasp information as a stimulus. Our system regresses the quantified tactile response that would be experienced if this grasp were performed on the object. We experiment with 16 items and 4 tactile data modalities to show that our proposal learns this task with low error.

show abstract

Section: E Data Collectionmentioning

confidence: 99%

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Zapata-Impata

Gil

Mezouar

et al. 2021

IEEE Trans. Haptics

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the scanning and grasping station, we use a custom version of the GeoGrasp software modified for the specific task in hand. GeoGrasp [24] is an algorithm designed for the computation of grasping points for unknown objects using a point cloud acquired from a single partial view of a scene. Even though originally designed for usage with RGBD cameras, it has been adapted to process point clouds obtained from a laser scanner.…”

Section: Grasping Pointsmentioning

confidence: 99%

“…7). A more detailed explanation can be found at [24]. Unlike point clouds obtained from RGBD cameras containing a few thousand points, those obtained from laser line profile scanners have a much bigger resolution, usually containing several hundred thousand points for similar dimension objects.…”

Section: Grasping Pointsmentioning

confidence: 99%

Towards footwear manufacturing 4.0: shoe sole robotic grasping in assembling operations

Oliver

Gil

Gómez

et al. 2021

Int J Adv Manuf Technol

Self Cite

View full text Add to dashboard Cite

In this paper, we present a robotic workcell for task automation in footwear manufacturing such as sole digitization, glue dispensing, and sole manipulation from different places within the factory plant. We aim to make progress towards shoe industry 4.0. To achieve it, we have implemented a novel sole grasping method, compatible with soles of different shapes, sizes, and materials, by exploiting the particular characteristics of these objects. Our proposal is able to work well with low density point clouds from a single RGBD camera and also with dense point clouds obtained from a laser scanner digitizer. The method computes antipodal grasping points from visual data in both cases and it does not require a previous recognition of sole. It relies on sole contour extraction using concave hulls and measuring the curvature on contour areas. Our method was tested both in a simulated environment and in real conditions of manufacturing at INESCOP facilities, processing 20 soles with different sizes and characteristics. Grasps were performed in two different configurations, obtaining an average score of 97.5% of successful real grasps for soles without heel made with materials of low or medium flexibility. In both cases, the grasping method was tested without carrying out tactile control throughout the task.

show abstract

“…In robotic grasping, the grasp detection can be determined directly on the point clouds. 4,5 Zapata-Impata et al presented a method to find the best pair of grasping points given a three-dimensional point cloud for an unknown object, where a set of geometric rules is employed to explore the cloud. 4 Considering the raw incomplete 3D point cloud, Gori et al first reconstruct the object in 3D and then obtain candidate triplets using discrete particle swarm optimization for three-finger manipulation.…”

Section: Introductionmentioning

confidence: 99%

A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes

Geng

Cao

Li³

et al. 2021

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

Vision-based grasping plays an important role in the robot providing better services. It is still challenging under disturbed scenes, where the target object cannot be directly grasped constrained by the interferences from other objects. In this article, a robotic grasping approach with firstly moving the interference objects is proposed based on elliptical cone-based potential fields. Single-shot multibox detector (SSD) is adopted to detect objects, and considering the scene complexity, Euclidean cluster is also employed to obtain the objects without being trained by SSD. And then, we acquire the vertical projection of the point cloud for each object. Considering that different objects have different shapes with respective orientation, the vertical projection is executed along its major axis acquired by the principal component analysis. On this basis, the minimum projected envelope rectangle of each object is obtained. To construct continuous potential field functions, an elliptical-based functional representation is introduced due to the better matching degree of the ellipse with the envelope rectangle among continuous closed convex curves. Guided by the design principles, including continuity, same-eccentricity equivalence, and monotonicity, the potential fields based on elliptical cone are designed. The current interference object to be grasped generates an attractive field, whereas other objects correspond to repulsive ones, and their resultant field is used to solve the best placement of the current interference object. The effectiveness of the proposed approach is verified by experiments.

show abstract

Fast geometry-based computation of grasping points on three-dimensional point clouds

Cited by 42 publications

References 47 publications

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Generation of Tactile Data From 3D Vision and Target Robotic Grasps

Towards footwear manufacturing 4.0: shoe sole robotic grasping in assembling operations

A robotic grasping approach with elliptical cone-based potential fields under disturbed scenes

Contact Info

Product

Resources

About