Geometry-Based Grasping of Vine Tomatoes
- URL: http://arxiv.org/abs/2103.01272v1
- Date: Mon, 1 Mar 2021 19:33:51 GMT
- Title: Geometry-Based Grasping of Vine Tomatoes
- Authors: Taeke de Haan, Padmaja Kulkarni, and Robert Babuska
- Abstract summary: We propose a geometry-based grasping method for vine tomatoes.
It relies on a computer-vision pipeline to identify the required geometric features of the tomatoes and of the truss stem.
The grasping method then uses a geometric model of the robotic hand and the truss to determine a suitable grasping location on the stem.
- Score: 6.547498821163685
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a geometry-based grasping method for vine tomatoes. It relies on a
computer-vision pipeline to identify the required geometric features of the
tomatoes and of the truss stem. The grasping method then uses a geometric model
of the robotic hand and the truss to determine a suitable grasping location on
the stem. This approach allows for grasping tomato trusses without requiring
delicate contact sensors or complex mechanistic models and under minimal risk
of damaging the tomatoes. Lab experiments were conducted to validate the
proposed methods, using an RGB-D camera and a low-cost robotic manipulator. The
success rate was 83% to 92%, depending on the type of truss.
Related papers
- Flatten Anything: Unsupervised Neural Surface Parameterization [76.4422287292541]
We introduce the Flatten Anything Model (FAM), an unsupervised neural architecture to achieve global free-boundary surface parameterization.
Compared with previous methods, our FAM directly operates on discrete surface points without utilizing connectivity information.
Our FAM is fully-automated without the need for pre-cutting and can deal with highly-complex topologies.
arXiv Detail & Related papers (2024-05-23T14:39:52Z) - A Smartphone-Based Method for Assessing Tomato Nutrient Status through Trichome Density Measurement [0.0]
Early detection of fertilizer-induced stress in tomato plants is crucial for timely crop management interventions and yield optimization.
This study proposes a novel, noninvasive technique for quantifying the density of trichomes-elongated hair-like structures found on plant surfaces-on young leaves using a smartphone.
arXiv Detail & Related papers (2024-04-30T12:45:41Z) - Deep learning-based approach for tomato classification in complex scenes [0.8287206589886881]
We have proposed a tomato ripening monitoring approach based on deep learning in complex scenes.
The objective is to detect mature tomatoes and harvest them in a timely manner.
Experiments are based on images collected from the internet gathered through searches using tomato state across diverse languages.
arXiv Detail & Related papers (2024-01-26T18:33:57Z) - A Vision-Guided Robotic System for Grasping Harvested Tomato Trusses in
Cluttered Environments [4.5195969272623815]
We propose a method to grasp trusses that are stacked in a crate with considerable clutter, which is how they are commonly stored and transported after harvest.
The method consists of a deep learning-based vision system to first identify the individual trusses in the crate and then determine a suitable grasping location on the stem.
Lab experiments with a robotic manipulator equipped with an eye-in-hand RGB-D camera showed a 100% clearance rate when tasked to pick all trusses from a pile.
arXiv Detail & Related papers (2023-09-29T12:07:08Z) - Look how they have grown: Non-destructive Leaf Detection and Size
Estimation of Tomato Plants for 3D Growth Monitoring [4.303287713669109]
In this paper, an automated non-destructive imaged-based measuring system is presented.
It uses 2D and 3D data obtained using a Zivid 3D camera, creating 3D virtual representations (digital twins) of the tomato plants.
The performance of the platform has been measured through a comprehensive trial on real-world tomato plants.
arXiv Detail & Related papers (2023-04-07T12:16:10Z) - Detection of Tomato Ripening Stages using Yolov3-tiny [0.0]
We use a neural network-based model for tomato classification and detection.
Our experiments showed an f1-score of 90.0% in the localization and classification of ripening stages in a custom dataset.
arXiv Detail & Related papers (2023-02-01T00:57:58Z) - Construction of Object Boundaries for the Autopilotof a Surface Robot
from Satellite Imagesusing Computer Vision Methods [101.18253437732933]
A method for detecting water objects on satellite maps is proposed.
An algorithm for calculating the GPS coordinates of the contours is created.
The proposed algorithm allows saving the result in a format suitable for the surface robot autopilot module.
arXiv Detail & Related papers (2022-12-05T12:07:40Z) - Neural Scene Representation for Locomotion on Structured Terrain [56.48607865960868]
We propose a learning-based method to reconstruct the local terrain for a mobile robot traversing urban environments.
Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the estimates the topography in the robot's vicinity.
We propose a 3D reconstruction model that faithfully reconstructs the scene, despite the noisy measurements and large amounts of missing data coming from the blind spots of the camera arrangement.
arXiv Detail & Related papers (2022-06-16T10:45:17Z) - GDR-Net: Geometry-Guided Direct Regression Network for Monocular 6D
Object Pose Estimation [71.83992173720311]
6D pose estimation from a single RGB image is a fundamental task in computer vision.
We propose a simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to learn the 6D pose in an end-to-end manner.
Our approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V datasets.
arXiv Detail & Related papers (2021-02-24T09:11:31Z) - Online Body Schema Adaptation through Cost-Sensitive Active Learning [63.84207660737483]
The work was implemented in a simulation environment, using the 7DoF arm of the iCub robot simulator.
A cost-sensitive active learning approach is used to select optimal joint configurations.
The results show cost-sensitive active learning has similar accuracy to the standard active learning approach, while reducing in about half the executed movement.
arXiv Detail & Related papers (2021-01-26T16:01:02Z) - Nothing But Geometric Constraints: A Model-Free Method for Articulated
Object Pose Estimation [89.82169646672872]
We propose an unsupervised vision-based system to estimate the joint configurations of the robot arm from a sequence of RGB or RGB-D images without knowing the model a priori.
We combine a classical geometric formulation with deep learning and extend the use of epipolar multi-rigid-body constraints to solve this task.
arXiv Detail & Related papers (2020-11-30T20:46:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.