[go: up one dir, main page]

Hasan et al., 2020 - Google Patents

Model-free, vision-based object identification and contact force estimation with a hyper-adaptive robotic gripper

Hasan et al., 2020

View PDF
Document ID
12587025310554312996
Author
Hasan W
Gerez L
Liarokapis M
Publication year
Publication venue
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

External Links

Snippet

Robots and intelligent industrial systems that focus on sorting or inspection of products require end-effectors that can grasp and manipulate the objects surrounding them. The capability of such systems largely depends on their ability to efficiently identify the objects …
Continue reading at www.minasliarokapis.com (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
    • G06K9/46—Extraction of features or characteristics of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/62—Methods or arrangements for recognition using electronic means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1694—Programme controls characterised by use of sensors other than normal servo-feedback from position, speed or acceleration sensors, perception control, multi-sensor controlled systems, sensor fusion
    • B25J9/1697—Vision controlled systems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1674—Programme controls characterised by safety, monitoring, diagnostic
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection

Similar Documents

Publication Publication Date Title
Morrison et al. Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
Kumra et al. Antipodal robotic grasping using generative residual convolutional neural network
D’Avella et al. A study on picking objects in cluttered environments: Exploiting depth features for a custom low-cost universal jamming gripper
Johns et al. Deep learning a grasp function for grasping under gripper pose uncertainty
Björkman et al. Enhancing visual perception of shape through tactile glances
Zhang et al. Robotic grasp detection based on image processing and random forest
Eppner et al. Grasping unknown objects by exploiting shape adaptability and environmental constraints
CN111226237A (en) Robotic system and method for robust grasping and targeting of objects
Weng et al. Multi-modal transfer learning for grasping transparent and specular objects
US12318951B2 (en) Systems and methods for object detection
Jiang et al. Learning hardware agnostic grasps for a universal jamming gripper
Bellandi et al. Roboscan: a combined 2D and 3D vision system for improved speed and flexibility in pick-and-place operation
Gilles et al. Metagraspnet: A large-scale benchmark dataset for scene-aware ambidextrous bin picking via physics-based metaverse synthesis
Hasan et al. Model-free, vision-based object identification and contact force estimation with a hyper-adaptive robotic gripper
Gao et al. In-hand pose estimation using hand-mounted RGB cameras and visuotactile sensors
Hogan et al. Finger-STS: Combined proximity and tactile sensing for robotic manipulation
Ummadisingu et al. Cluttered food grasping with adaptive fingers and synthetic-data trained object detection
CN116745576A (en) Recovering material properties using active illumination and cameras on a robotic manipulator
Wang et al. GraspFusionNet: a two-stage multi-parameter grasp detection network based on RGB–XYZ fusion in dense clutter
Cao et al. Fuzzy-depth objects grasping based on fsg algorithm and a soft robotic hand
Kristensen et al. Bin-picking with a solid state range camera
Lin et al. Vision based object grasping of industrial manipulator
Frank et al. Stereo-vision for autonomous industrial inspection robots
Zhuang et al. Lyrn (lyapunov reaching network): A real-time closed loop approach from monocular vision
US12350847B2 (en) Method for controlling a robot for manipulating, in particular picking up, an object