Bin Picking Approaches Based on Deep Learning Techniques: A State-of-the-Art Survey-Reference-Cited by-同舟云学术

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Bin Picking Approaches Based on Deep Learning Techniques: A State-of-the-Art Survey

Published:2022-04-29 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
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Author:

Cordeiro Artur¹,Rocha Luis F.¹,Costa Carlos²,Costa Pedro²,Silva Manuel F.¹

Affiliation:

1. Polytechnic Institute of Porto,ISEP/IPP - School of Engineering,Porto,Portugal

2. INESC TEC - INESC Technology and Science,Porto,Portugal

Funder

European Regional Development Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9784764/9784765/09784795.pdf?arnumber=9784795

Cited by 16 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Framework for Joint Grasp and Motion Planning in Confined Spaces;2024 13th International Workshop on Robot Motion and Control (RoMoCo);2024-07-02

2. Off-the-Shelf Bin Picking Workcell with Visual Pose Estimation: A Case Study on the World Robot Summit 2018 Kitting Task;2024 21st International Conference on Ubiquitous Robots (UR);2024-06-24

3. Competencies Development in YOLO-CNN and Stereo Camera Vision to Enhance Bin Picking in Simulated Environments;2024 IEEE Global Engineering Education Conference (EDUCON);2024-05-08

4. Analytic and Data-Driven Force Prediction for Vacuum-Based Granular Grippers;Machines;2024-01-12

5. An Approach for Bin Picking in High-Mix Low-Volume Manufacturing;Lecture Notes in Mechanical Engineering;2024

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