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

拆卸是许多工业活动中必不可少的操作，包括维修、再制造和回收。拆卸往往是手工进行的-这是劳动密集型的，通常效率低下。拆卸需要高度灵巧的操作，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，机器人化可能更困难。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。拆卸自动化的现有程序和最先进的技术通常需要对拆卸任务进行全面分析，正确设计传感和遵从设施，有效的任务计划和可靠的系统集成。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我学习机制，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相应的控制策略。这种能力将有助于建立一个更即插即用的拆卸自动化系统，并减少拆卸自动化的技术困难和实现成本。预计下一代工业机器人可以用于更复杂和不确定的任务，如维护，清洁，维修，再制造和回收，其中许多过程是接触丰富的。拆卸是一个典型的接触量大的任务。首席研究员设想，自主学习机器人拆卸将提供关键的理解和技术，可用于未来其他类型的接触式任务的自动化，以鼓励英国工业更广泛地采用机器人。

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.