Camera-Based Method for Identification of the Layout of a Robotic Workcell

Camera Arrangement Optimization for Workspace Monitoring in Human-Robot Collaboration

Human-robot interaction is becoming an integral part of practice. There is a greater emphasis on safety in workplaces where a robot may bump into a worker. In practice, there are solutions that control the robot based on the potential energy in a collision or a robot re-planning the straight-line trajectory. However, a sensor system must be designed to detect obstacles across the human-robot shared workspace. So far, there is no procedure that engineers can follow in practice to deploy sensors ideally. We come up with the idea of classifying the space as an importance index, which determines what part of the workspace sensors should sense to ensure ideal obstacle sensing. Then, the ideal camera positions can be automatically found according to this classified map. Based on the experiment, the coverage of the important volume by the calculated camera position in the workspace was found to be on average 37% greater compared to a camera placed intuitively by test subjects. Using two cameras at the workplace, the calculated positions were 27% more effective than the subjects' camera positions. Furthermore, for three cameras, the calculated positions were 13% better than the subjects' camera positions, with a total coverage of more than 99% of the classified map.

Distributed Camera Subsystem for Obstacle Detection

This work focuses on improving a camera system for sensing a workspace in which dynamic obstacles need to be detected. The currently available state-of-the-art solution (MoveIt!) processes data in a centralized manner from cameras that have to be registered before the system starts. Our solution enables distributed data processing and dynamic change in the number of sensors at runtime. The distributed camera data processing is implemented using a dedicated control unit on which the filtering is performed by comparing the real and expected depth images. Measurements of the processing speed of all sensor data into a global voxel map were compared between the centralized system (MoveIt!) and the new distributed system as part of a performance benchmark. The distributed system is more flexible in terms of sensitivity to a number of cameras, better framerate stability and the possibility of changing the camera number on the go. The effects of voxel grid size and camera resolution were also compared during the benchmark, where the distributed system showed better results. Finally, the overhead of data transmission in the network was discussed where the distributed system is considerably more efficient. The decentralized system proves to be faster by 38.7% with one camera and 71.5% with four cameras.

Finding the Optimal Pose of 2D LLT Sensors to Improve Object Pose Estimation

In this paper, we examine a method for improving pose estimation by correctly positioning the sensors relative to the scanned object. Three objects made of different materials and using different manufacturing technologies were selected for the experiment. To collect input data for orientation estimation, a simulation environment was created where each object was scanned at different poses. A simulation model of the laser line triangulation sensor was created for scanning, and the optical surface properties of the scanned objects were set to simulate real scanning conditions. The simulation was verified on a real system using the UR10e robot to rotate and move the object. The presented results show that the simulation matches the real measurements and that the appropriate placement of the sensors has improved the orientation estimation.