Abstract
This work presents an industrial bin-picking framework for robotics called PickingDK. The proposed framework employs a plugin based architecture, which allows it to integrate different types of sensors, robots, tools, and available open-source software and state-of-the-art methods. It standardizes the bin-picking process with a unified workflow based on generally defined plugin interfaces, which promises the hybridization of functional/virtual plugins for fast prototyping and proof-of-concept. It also offers different levels of controls according to the user’s expertise. The presented use cases demonstrate flexibility when building bin-picking applications under PickingDK framework and the convenience of exploiting hybrid style prototypes for evaluating specific steps in a bin-picking system, such as parameter fine-tuning and picking cell design.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference40 articles.
1. Analysis and Observations From the First Amazon Picking Challenge
2. A Review of Physics Simulators for Robotic Applications
3. Reducing the Barrier to Entry of Complex Robotic Software: A MoveIt! Case Study;Coleman;arXiv,2014
4. Model Globally, Match Locally: Efficient and Robust 3D Object Recognition;Drost;Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,2010
5. Eye-in-hand vision-based robotic bin-picking with active laser projection
Cited by
3 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