Robot arm and hand manipulation using vision-guided motion control directed by human gestures

使用由人类手势指导的视觉引导运动控制的机器人手臂和手部操作

• 批准号: RGPIN-2016-06365
• 负责人: Jagersand, Martin
• 金额: $ 3.93万
• 依托单位: University of Alberta
• 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=709493
• 关键词: Robot; arm; hand; manipulation; using

This research will focus on the geometric specification of robot (arm and hand) manipulation tasks in camera (and other sensory) coordinate frames. We propose to develop Human-Robot Interfaces (HRI) based on pointing and gestures. The gestures will be interpreted through computer vision and robot motions will be performed through visual servoing, and force servoing. The main objective is to solve general robot arm and hand manipulation tasks interactively with humans. Robotics research in both engineering and artificial intelligence has focused on autonomy. While autonomy works well in specific situations (such as factory automation), despite decades of research, robotics cannot solve human arm and hand manipulation tasks in everyday unstructured environments. However, in many cases autonomy may not be the goal. Humans naturally interact and collaborate on tasks. A human can easily guide another human how to perform complex tasks. Much of this interaction is non-verbal. For robots to work with humans they need to take visual cues from the human, and be easy to interact with, rather than working autonomously. Hence, with good HRI, humans could relatively effortlessly supervise tasks that are otherwise too complex for robots. We propose an integrated systems and experiment-driven approach to this research. In particular we will research: 1. Task specification. We will develop the so called "task-function" to handle a combination of camera image-, 3D world- and force sensor coordinates. 2. Video tracking of image features. We will research how to improve robustness of registration-based tracking. 3. Hybrid Visual Servoing. We will extend image-coordinate visual servoing to also work with 3D and force sensory feedback, while requiring a minimum of a-priori calibration. 4. HRI. We will research gesture based human-robot interaction, including what geometric specifications (2D image, 3D, force contacts) are most natural for a human in order to specify complete, provable 3D alignments; and then to sequence into full manipulation tasks. We will develop benchmarks for everyday human manipulation tasks, and study our task specification interfaces on these (e.g. task completion times and cognitive load.) The goal of our research will be a better understanding of visual and sensory-space motion control, both from a robotics and human interface perspective. Practical outcomes include integration of different tracking and sensory-motor control methods into modular ROS (Robot Operating System) software libraries, and test results of this software by humans on many different manipulation tasks.

这项研究将集中在机器人（手臂和手）操作任务的几何规格在相机（和其他感官）坐标系。我们建议开发基于指向和手势的人机界面（HRI）。手势将通过计算机视觉进行解释，机器人运动将通过视觉伺服和力伺服进行。主要目标是解决一般的机器人手臂和手操作任务与人类交互。 工程和人工智能领域的机器人研究都集中在自主性上。虽然自动化在特定情况下（如工厂自动化）效果很好，但尽管经过数十年的研究，机器人技术仍然无法解决日常非结构化环境中的人类手臂和手部操作任务。然而，在许多情况下，自主权可能不是目标。人类自然会在任务上进行互动和协作。一个人可以很容易地指导另一个人如何执行复杂的任务。这种互动大部分是非语言的。对于与人类一起工作的机器人来说，它们需要从人类那里获得视觉提示，并且易于互动，而不是自主工作。因此，有了良好的HRI，人类可以相对轻松地监督机器人过于复杂的任务。 我们提出了一个集成的系统和实验驱动的方法来进行这项研究。我们将特别研究： 1.任务说明。我们将开发所谓的“任务功能”来处理相机图像、3D世界和力传感器坐标的组合。 2.图像特征的视频跟踪。我们将研究如何提高基于配准的跟踪的鲁棒性。 3.混合视觉伺服。我们将扩展图像坐标视觉伺服也与3D和力的感觉反馈，同时需要最低限度的先验校准。 4. HRI我们将研究基于手势的人机交互，包括什么样的几何规范（2D图像，3D，力接触）是最自然的人，以指定完整的，可证明的3D对齐;然后顺序到完整的操作任务。我们将为日常人类操作任务开发基准，并研究我们的任务规范接口（例如任务完成时间和认知负荷）。 我们研究的目标是从机器人和人机界面的角度更好地理解视觉和感觉空间运动控制。实际成果包括将不同的跟踪和感觉电机控制方法集成到模块化ROS（机器人操作系统）软件库中，以及人类在许多不同操作任务中对该软件的测试结果。