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Disassembly is a core procedure in remanufacturing. Disassembly is currently carried out mainly by human operators. It is important to reduce the labor content of disassembly through automation, to make remanufacturing more economically attractive. Threaded fastener removal is one of the most difficult disassembly tasks to be fully automated. This article presents a new method developed for automating the unfastening of screws. An electric nutrunner spindle with a geared offset adapter was fitted to the end of a collaborative robot. The position of a hexagonal headed screw in a fitted stage was known only approximately, and its orientation in the hole was unknown. The robot was programed to perform a spiral search motion to engage the tool onto the screw. A control strategy combining torque and position monitoring with active compliance was implemented. An existing robot cell was modified and utilized to demonstrate the concept and to assess the feasibility of the solution using a turbocharger as a disassembly case study. Note to Practitioners—Remanufacturing is known to generate substantial economic, social, and environmental benefits. Disassembly is the first operation in a remanufacturing process chain. Unfastening threaded parts (“unscrewing”) is a common disassembly task accounting for approximately 40% of all disassembly activity. Like other disassembly tasks, often, unscrewing has to be carried out manually in remanufacturing due to difficulties caused by the variable and unpredictable condition of the end-of-life (EoL) products to be remanufactured. Automating unscrewing operations should reduce the labor content of disassembly, thus lowering remanufacturing costs and promoting the adoption of remanufacturing. This article proposes the use of a collaborative robot to perform autonomous unfastening of hexagonal headed screws. Collaborative robots have built-in force sensors and can be programed to carry out operations involving not only position but also active force and compliance control. They can work safely alongside human operators, enabling the latter to focus on jobs requiring high cognitive or manipulation abilities. The article presents a novel spiral search technique developed to improve the rate of successful engagement between the robot end effector and the screw heads despite uncertainties in the location of the screws. The technique was successfully demonstrated on the dismantling of a turbocharger but can readily be applied to other EoL products with hexagonal headed screws. It can also be used with other kinds of screws (e.g., Phillips screws and slotted-head screws) simply by changing the tool and tuning the robot control parameters. A limitation of the proposed technique is that it can only deal reliably with undamaged screws. In our future research, we will consider screws that are in imperfect conditions through usage and develop appropriate solutions for their removal by robots. [ABSTRACT FROM AUTHOR]

Visual tracking methods have been successful in recent years. Correlation filter (CF) based methods significantly advanced state-of-the-art tracking. The advancement in CF tracking performance is predominantly attributed to powerful features and sophisticated online learning formulations. However, there would be trouble if the tracker indiscriminately learned samples. Particularly, when the target is severely occluded or out-of-view, the tracker will continuously learn the wrong information, resulting target loss in the following frames. In this study, aiming to avoid incorrect training when occlusions occur, we propose a regional color histogram-based occlusion estimating agency (RCHBOEA), which estimates the occlusion level and then instructs, based on the result, the tracker to work in one of two modes: normal or lost. In the normal mode, an occlusion level-based self-adopting learning rate is used for tracker training. In the lost mode, the tracker pauses its training and conducts a search and recapture strategy on a wider searching area. Our method can easily complement CF-based trackers. In our experiments, we employed four CF-based trackers as a baseline: discriminative CFs (DCF), kernelized CFs (KCF), background-aware CFs (BACF), and efficient convolution operators for tracking: hand-crafted feature version (ECO_HC). We performed extensive experiments on the standard benchmarks: VIVID, OTB50, and OTB100. The results demonstrated that combined with RCHBOEA, the trackers achieved a remarkable improvement. [ABSTRACT FROM AUTHOR]