Automatic dismantling integrating optical flow into a machine vision-controlled robot system

In order to automatically run a dismantling task, first a workpiece has to be recognized and its pose has to be estimated. This step is solved by a model-based recognition process using the information from three stationary cameras. Our workpiece model could be extracted from CAD-data and consists of planar patches and circular features, where the circular features are suitable to model parts of connection elements like screws. Using the initial estimation of the workpiece pose we control the robot by evaluating the image sequence given by a camera mounted on the robot hand. We use a multi-level 3D tracking approach to continuously estimate the workpiece pose using an iterative extended Kalman-filter process and integrate velocity estimation based on optical flow. To provide enough computing power for real-time image processing, we combine our dedicated image processing hardware with a heterogeneous parallel computer. Our approach has been tested in numerous experiments on loosening screws with a powered screwdriver and dismantling a workpiece consisting of two blocks of different sizes connected by two screws