Vision-Based Hierarchical Recognition for Dismantling Robot Applied to Interior Renewal of Buildings

New robotic systems are going to play an essential role in the future dismantling service for renewing office interiors in buildings. In dismantling tasks, robots are expected to be able to find and remove very small parts such as screws and bolts. Such recognition of small parts is difficult for robots. The article describes a vision-based hierarchical recognition applied to dismantling tasks where large structures are detected at first, thus small parts attached to these structures are detected easier. Regarding the items in the ceiling side, after the dismantling task of the ceiling panels, it is necessary to remove carefully the screws that once held these panels to the light gauge steel (LGS), with the purpose of reusing it. With the pose detection of the large structure (LGS) and considering a robot arm with a stereo camera on its tip, a trajectory near that structure can be computed to detect the small parts, in this case the screws. The large structure is detected by using a process of line detection in 2D and its 3D pose is measured with the stereo camera. During the motion along the structure, the screws are detected by applying a multi-template matching process to every captured image. Followed by, the Support Vector Machine (SVM), which recognizes those screw candidates with high true positive rate and low false positive one. These rates are improved with a temporal multi-image integration for tracking the screw candidates. In the experiment, 10 actual screws distributed in 1.1 m along a linear segment on the LGS are successfully recognized with a few false positives and with a final computed 3D position of 2 mm in average. Feasibility of methodology is evaluated by experimentation under different lighting conditions in a realistic environment. Experimental results show that the method works well to be applied in an actual dismantling site.