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CobotGear: Interaction with Collaborative Robots using Wearable Optical Motion Capturing Systems

机译:CobotGear:使用可穿戴光学运动捕捉系统与协作机器人进行交互

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In industrial applications, complex tasks require human collaboration since the robot doesn’t have enough dexterity. However, the robots are still implemented as tools and not as collaborative intelligent systems. To ensure safety in the human-robot collaboration, we introduce a system that presents a new method that integrates low-cost wearable mocap, and an improved collision avoidance algorithm based on the artificial potential fields. Wearable optical motion capturing allows to track the human hand position with high accuracy and low latency on large working areas. To increase the efficiency of the proposed algorithm, two obstacle types are discriminated according to their collision probability. A preliminary experiment was performed to analyze the algorithm behavior and to select the best values for the obstacle’s threshold angle $heta_{OBS}$, and for the avoidance threshold distance dAT. The second experiment was carried out to evaluate the system performance with $d_{AT}=0.2mathrm{m}$ and $heta_{OBS}=45$ degrees. The third experiment evaluated the system in a real collaborative task. The results demonstrate the robust performance of the robotic arm generating smooth collision-free trajectories. The proposed technology will allow consumer robots to safely collaborate with humans in cluttered environments, e.g., factories, kitchens, living rooms, and restaurants.
机译:在工业应用中,由于机器人的灵活性不足,因此复杂的任务需要人工协作。但是,机器人仍被实现为工具,而不是协作智能系统。为了确保人机协作的安全性,我们引入了一个系统,该系统提出了一种集成了低成本可穿戴Mocap的新方法以及一种基于人工势场的改进的防撞算法。穿戴式光学运动捕捉功能可以在较大的工作区域上以高精度和低延迟来跟踪人的手的位置。为了提高所提算法的效率,根据其碰撞概率来区分两种障碍物类型。进行了初步实验,以分析算法行为并为障碍物的阈值角\\ theta_ {OBS} $和避免阈值距离d选择最佳值 AT 。进行第二个实验以评估$ d_ {AT} = 0.2 \ mathrm {m} $和$ \ theta_ {OBS} = 45 $度的系统性能。第三次实验在一个真正的协作任务中评估了该系统。结果证明了机械臂产生平滑无碰撞轨迹的鲁棒性能。这项拟议的技术将使消费者机器人能够在杂乱无章的环境(例如工厂,厨房,客厅和餐厅)中与人类安全协作。

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