We can only allow human-robot-cooperation in a common work cell if the human integrity is guaranteed. A surveillance system with multiple cameras can detect collisions without contact to the human collaborator. A failure safe system needs to optimally cover the important areas of the robot work cell with safety overlap. We propose an efficient algorithm for optimally placing and orienting the cameras in a 3D CAD model of the work cell. In order to evaluate the quality of the camera constellation in each step, our method simulates the vision system using a z-buffer rendering technique for image acquisition, a voxel space for the overlap and a refined visual hull method for a conservative human reconstruction. The simulation allows to evaluate the quality with respect to the distortion of images and advanced image analysis in the presence of static and dynamic visual obstacles such as tables, racks, walls, robots and people. Our method is ideally suited for maximizing the coverage of multiple cameras or minimizing an error made by the visual hull and can be extended to probabilistic space carving.
翻译:我们只能允许在共同工作单元中进行人类-机器人合作,如果人类完整性得到保证。一个带有多个照相机的监视系统可以探测碰撞,而无需与人类合作者接触。一个故障安全系统需要以安全重叠的方式最佳地覆盖机器人工作单元的重要领域。我们建议一个高效的算法,在工作单元的3D CAD模型中最佳地放置和引导照相机。为了评估摄像群每步的质量,我们的方法模拟视觉系统,使用z-butffer显示图像获取技术、重叠的 voxel空间以及保守的人类重建的精细视觉船体方法。模拟能够评估图像扭曲和高级图像分析的质量,因为存在静态和动态的视觉障碍,如表、架、墙、机器人和人。我们的方法非常适合尽量扩大多摄像头的覆盖范围或尽量减少视觉船体造成的错误,并且可以扩展到概率性空间雕刻。