Together at Last: Perception and Learning for Collaborative Robots Operating in Human-Centric Environments

最终在一起：在以人为中心的环境中运行的协作机器人的感知和学习

• 批准号: RGPIN-2018-06524
• 负责人: Kelly, Jonathan
• 金额: $ 2.04万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714298
• 关键词: Together; Last; Perception; Learning; Collaborative

Cobots, or collaborative robots, are a class of machines intended to physically interact with humans in a shared space. This definition encompasses a large variety of hardware systems and application domains (from manufacturing to autonomous vehicles). Over the next five years, this nascent market segment is expected to boom all major robotics manufacturers have established new products in this category. The goal of the proposed research program is to dramatically advance the state of the art in collaborative robotics, making cobots more capable and more safe than at present. This research will bridge the gap and help make cobots ubiquitous outside of laboratories. The program has four key research themes: 1) introspection, sensor fusion, and sensor and actuator self-calibration, 2) semantic mapping, 3) intention recognition for trust-based interaction, and 4) formal testing and validation. These themes are tied together by the unifying thread of advanced perceptual processing, with the aim of creating flexible cobot platforms for the real world. Research activities across the four themes (or project areas) will build upon results from robotics and machines learning. A general framework for hierarchical perception will be developed, beginning with low-level data fusion and leading to high-level semantic understanding. Data from novel sensing modalities will be fused into consistent environment representations; through self-calibration and introspection, a baseline level of functionality and safety will be maintained at all times. Multimodal sensor data will then be processed to generate semantic maps, which will include information about objects and their relationships (allowing for reasoning about interactions). The maps will be produced using clustering and supervised learning. By extending semantic perception, human intention and activity recognition will become possible. Intention recognition and response learning will significantly enhance safety, ensuring that each party (human and robot) is able to anticipate the actions of the other. Finally, all capabilities will be verified in an extensive test campaign that includes formal validation, using an advanced mobile manipulator. The proposed research program has the potential for transformative impact in a wide range of industries, fundamentally changing the way people work with robots and, in turn, leveraging robotics to enhance productivity and quality of life. Research outputs will create new technological solutions that will be readily transferable to Canadian companies, giving them the tools they need to enter new economic areas that have tremendous growth potential. Many of these markets, including service robotics and advanced manufacturing (for, e.g., aerospace) will be of critical importance to Canada in maintaining a competitive edge in the global marketplace.

协作机器人，或协作机器人，是一类旨在在共享空间中与人类进行物理交互的机器。这个定义涵盖了各种各样的硬件系统和应用领域（从制造业到自动驾驶汽车）。在接下来的五年里，这个新兴的细分市场预计将蓬勃发展，所有主要的机器人制造商都在这一类别中建立了新产品。拟议的研究计划的目标是大幅推进协作机器人技术的发展，使协作机器人比目前更有能力、更安全。这项研究将弥补这一差距，并帮助协作机器人在实验室之外无处不在。该计划有四个关键研究主题：1)自省，传感器融合，传感器和执行器自校准，2)语义映射，3)基于信任的交互意图识别，4)正式测试和验证。这些主题通过高级感知处理的统一线索联系在一起，目的是为现实世界创造灵活的协作机器人平台。