Automated Workspace Scanning with Local Areas of Interest in the Context of Industrial Robots as a Service (IRaaS)

This is the authors' abstract. We don't add key points for this paper.

Recent advances in robotics provide an extensive toolkit for tasks such as collision-free path planning, trajectory optimization, and object manipulation, facilitating flexible and adaptive programming in industrial environments. However, these solutions heavily rely on accurate digital 3D models of the robot workspace, which are typically outdated or unavailable in brownfield environments, making their deployment challenging. While 3D scanning is well explored in mobile robotics and navigation, its application in stationary industrial robotics remains underutilized. To address this gap, this paper presents a methodology that integrates eye-in-hand 3D scanning, automated collision map generation, and object detection. The point cloud data aquired by the 3D camera is used to generate a voxel-based occupancy map that enables robot path planning in unknown environments. In addition, we place localization markers near objects of interest, thereby defining localized search areas. These are used for the detection and registration of relevant objects, such as workpieces, within the point cloud. The methodology is implemented in ROS and validated through an industrial use case study that resembles a typical production environment. Our scanning approach eliminates the need to manually create or retrofit digital models of the robot workspace, as is typically done with CAD tools. As a result, our automated workspace scanning solution enables advanced robotic path planning and manipulation as required within the context of Industrial Robots as a Service (IRaaS).