Market Assessment for an innovative method to self-calibrate manipulator kinematic and sensor extrinsic**parameters

自校准机械手运动学和传感器外部**参数的创新方法的市场评估

• 批准号: 523435-2018
• 负责人: Kelly, Jonathan
• 金额: $ 1.08万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Idea to Innovation
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=655275
• 关键词: Market; Assessment; innovative; method; self

Collaborative robots, or cobots, are machines designed to work alongside humans in shared spaces. Many**cobots are mobile, and incorporate lightweight, person-safe robotic arms for manipulation tasks. These**(mobile) manipulators need to be correctly calibrated to ensure accurate and reliable operation. Calibration**typically involved determining sensor extrinsic parameters (i.e., external sensor pose relative to the manipulator**base) and manipulator kinematic parameters (e.g., joint biases and link length offsets). Calibration parameters**will often change over a robot's lifetime due to, for example, general wear and tear.**We have developed an innovative method for manipulator self-calibration using contact sensing. The method,**based on point cloud registration, can be applied to determine sensor extrinsic and manipulator kinematic**parameters. Requisite point cloud maps are generated from external visual or depth camera data, and by**monitoring end-effector contact with surfaces in the environment using a force-torque or tactile sensor and an**impedance controller. The process uses on-board hardware exclusively and does not rely on any external**measurement devices, fiducial markers, or calibration rigs; further, it is fully automatic in the general case.**This self-calibration method will very substantially reduce the costs of deploying person-safe, collaborative**manipulator systems, by decreasing or eliminating the need for human-in-the-loop maintenance and**adjustment. A wide range of robot application domains, from manufacturing to services, will benefit from**deploying the technology. This proposal seeks funding for a market assessment, to identify opportunities for**commercialization (within Canada), improving Canadian competitiveness in an emerging, critical market**sector with tremendous growth potential.

协作机器人或协作机器人是设计用于在共享空间中与人类一起工作的机器。许多**协作机器人都是可移动的，并采用轻型、对人员安全的机械臂来执行操作任务。这些**（移动）机械手需要正确校准，以确保准确可靠的操作。校准**通常涉及确定传感器外部参数（即，相对于操纵器**底座的外部传感器位姿）和操纵器运动学参数（例如，关节偏差和连杆长度偏移）。由于一般磨损等原因，校准参数**会在机器人的使用寿命内经常发生变化。**我们开发了一种使用接触传感进行机械手自校准的创新方法。该方法基于点云配准，可用于确定传感器外在参数和机械臂运动学参数。必要的点云图是根据外部视觉或深度相机数据生成的，并通过使用力扭矩或触觉传感器和阻抗控制器监控末端执行器与环境中表面的接触。该过程仅使用机载硬件，不依赖任何外部**测量设备、基准标记或校准装置；此外，在一般情况下它是全自动的。**这种自校准方法将通过减少或消除对人在环的需求，极大地降低部署人员安全的协作**操纵器系统的成本维护和**调整。从制造到服务的广泛机器人应用领域都将受益于**部署该技术。该提案寻求市场评估资金，以确定**商业化（加拿大境内）的机会，提高加拿大在具有巨大增长潜力的新兴关键市场**领域的竞争力。