避碰的局部和全局传感器

Aquib Rashid, Kannan Peesapati, M. Bdiwi, Sebastian Krusche, W. Hardt, M. Putz
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引用次数: 5

摘要

大型工业机器人具有大的停车距离和大的自遮挡区域,实现敏捷生产单元中安全高效的人机协作是一项具有挑战性的任务。避免碰撞是实现这一任务所需的主要功能。实际上,它需要准确估计已知(机器人)和未知(人类或其他任何东西)物体之间的最短距离。这项工作提出了一种选择性融合全局和局部传感器的方法,分别代表大范围360°激光雷达和小范围RGB相机,用于动态速度和分离监测。对未知动态物体与机械臂关节之间的碰撞检测进行了安全功能评估。与围栏系统相比,该系统的效率为29-40%。重型工业机器人和受控线轴假人用于评估不同的机器人和场景配置。结果表明,当使用本地和全局设置时,效率和安全性更高。
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Local and Global Sensors for Collision Avoidance
Implementation of safe and efficient human robot collaboration for agile production cells with heavy-duty industrial robots, having large stopping distances and large self-occlusion areas, is a challenging task. Collision avoidance is the main functionality required to realize this task. In fact, it requires accurate estimation of shortest distance between known (robot) and unknown (human or anything else) objects in a large area. This work proposes a selective fusion of global and local sensors, representing a large range 360° LiDAR and a small range RGB camera respectively, in the context of dynamic speed and separation monitoring. Safety functionality has been evaluated for collision detection between unknown dynamic object to manipulator joints. The system yields 29-40% efficiency compared to fenced system. Heavy-duty industrial robot and a controlled linear axis dummy is used for evaluating different robot and scenario configurations. Results suggest higher efficiency and safety when using local and global setup.
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