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: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=616261
• 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.

这项研究将集中在摄像机(和其他感官)坐标系中机器人(手臂和手)操作任务的几何规范。我们建议开发基于指点和手势的人-机器人接口(HRI)。这些手势将通过计算机视觉进行解释，机器人的运动将通过视觉伺服和力伺服来执行。它的主要目标是解决一般的机器人手臂和手的操作任务与人类交互。 工程学和人工智能领域的机器人研究都聚焦于自主。虽然自主在特定情况下工作得很好(如工厂自动化)，但尽管进行了数十年的研究，机器人技术无法在日常非结构化环境中解决人类手臂和手的操纵任务。然而，在许多情况下，自治可能不是目标。人类很自然地就任务进行交互和协作。一个人可以很容易地指导另一个人如何执行复杂的任务。这种互动很大程度上是非语言的。要让机器人与人类合作，它们需要从人类那里获得视觉提示，并易于与之互动，而不是自主工作。因此，有了好的HRI，人类可以相对毫不费力地监督那些否则对机器人来说过于复杂的任务。 我们提出了一种集成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“任务函数”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注册的跟踪的健壮性。 3.混合视觉伺服。我们将扩展图像坐标视觉伺服，以支持3D和强制感觉反馈，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-机器人交互，包括对人类来说最自然的几何规格(2D图像、3D、力接触)，以便指定完整的、可证明的3D对齐；然后对完整的操作任务进行排序。我们将为日常人类操纵任务制定基准，并在这些基准上研究我们的任务规范接口(例如，任务完成时间和认知负荷)。 我们的研究目标将是从机器人和人机接口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-马达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。