Self-learning robotics for industrial contact-rich tasks (ATARI): enabling smart learning in automated disassembly

用于工业接触丰富任务的自学习机器人（ATARI）：在自动拆卸中实现智能学习

• 批准号: EP/W00206X/1
• 负责人: Yongjing Wang
• 金额: $ 38万
• 依托单位: University of Birmingham
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW00206X%2F1
• 关键词: Self; learning; robotics; industrial; contact

Disassembly is an essential operation in many industrial activities including repair, remanufacturing and recycling. Disassembly tends to be manually carried out - it is labour intensive and usually inefficient.Disassembly requires high-level dexterity in manipulations and thereby can be more difficult to robotise in comparison to the tasks that have no physical contacts (e.g. computer visual inspection) or simple contacts (e.g. cutting, welding, pick-and-place). Robotic disassembly has the potential to improve the productivity of repair, remanufacturing, recycling, all of which have been recognised as key components of a more circular economy. The existing procedure and state-of-the-art techniques for disassembly automation usually require a comprehensive analysis of a disassembly task, correct design of sensing and compliance facilities, efficient task plans, and a reliable system integration. It is usually a complex, expensive and time-consuming process to implement a robotic disassembly system. This project will develop a self-learning mechanism to allow robots to learn disassembly tasks and the respective control strategies autonomously, by combining multidimensional sensing and machine learning techniques. This capability will help build a more plug-and-play disassembly automation system, and reduce the technical difficulties and the implementation costs of disassembly automation. It is expected the next generation industrial robotics can be adopted in more complex and uncertain tasks such as maintenance, cleaning, repair, remanufacturing and recycling, where many processes are contact-rich. Disassembly is a typical contact-rich task. The Principal Investigator envisages that self-learning robotic disassembly will provide key understandings and technologies that can be adopted to the automation of other types of contact-rich tasks in the future to encourage a wider adoption of robots in the UK industry.

拆卸是包括维修、再制造和回收在内的许多工业活动中必不可少的操作。拆卸往往是人工进行的--它是劳动密集型的，通常效率很低。拆卸要求操作的高度灵活性，因此与没有物理接触(例如计算机视觉检查)或简单接触(例如切割、焊接、拾取和放置)的任务相比，拆卸可能更难实现机器人。机器人拆卸有可能提高维修、再制造和回收的生产率，所有这些都被认为是更循环经济的关键组成部分。现有的拆卸自动化程序和最先进的技术通常需要对拆卸任务进行全面分析，正确设计传感和合规设施，高效的任务计划，以及可靠的系统集成。实现机器人拆卸系统通常是一个复杂、昂贵和耗时的过程。该项目将开发一种自学习机制，通过结合多维传感和机器学习技术，使机器人能够自主学习拆卸任务和各自的控制策略。这一能力将有助于建立一个更即插即用的拆卸自动化系统，降低拆卸自动化的技术难度和实施成本。预计下一代工业机器人可以被用于更复杂和不确定的任务，如维护、清洁、修复、再制造和回收，这些任务中的许多过程都是接触丰富的。反汇编是一项典型的接触丰富的任务。首席调查员设想，自学习机器人拆卸将提供关键的理解和技术，可用于未来自动化其他类型的接触丰富的任务，以鼓励在英国行业更广泛地采用机器人。

项目成果

Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning

• DOI: 10.1016/j.rcim.2023.102619
• 期刊: Robotics Comput. Integr. Manuf.
• 作者: Wupeng Deng;QUAN LIU;D. Pham;Jiwei Hu;Kin-Man Lam;Yongjing Wang;Zude Zhou

Robotic Disassembly Sequence Planning With Backup Actions

• DOI: 10.1109/tase.2021.3072663
• 发表时间: 2022-07
• 期刊: IEEE Transactions on Automation Science and Engineering
• 影响因子: 5.6
• 作者: Y. Laili;Xiang Li;Yongjing Wang;Lei Ren;Xiaokang Wang

CuO-based materials for thermochemical redox cycles: the influence of the formation of a CuO percolation network on oxygen release and oxidation kinetics.

用于热化学氧化还原循环的 CuO 基材料：CuO 渗滤网络的形成对氧释放和氧化动力学的影响。

• DOI: 10.1007/978-3-319-46049-9_13
• 发表时间: 2022
• 期刊: Discover chemical engineering
• 作者: Imtiaz Q

Online Hierarchical Conformance Refinement Planning for Autonomous Robots

自主机器人在线分层一致性细化规划

• DOI: 10.1109/icac57885.2023.10275162
• 发表时间: 2023
• 作者: Kamperis O

Perspective of self-learning robotics for disassembly automation

• DOI: 10.1109/icac55051.2022.9911085
• 发表时间: 2022-09
• 期刊: 2022 27th International Conference on Automation and Computing (ICAC)
• 作者: Farzaneh Goli;Yongjing Wang;M. Saadat