Predictive exposure control for vision-based robotic disassembly using deep learning and predictive learning

Lighting conditions can affect the performance of vision-based robots in manufacturing. This paper presents a predictive exposure control method that allows the acquisition of high-quality images in real time under poor lighting conditions. This technique is particularly useful in robotic disassembly where a fixed and optimised lighting environment is difficult to construct due to the uncertain conditions of used components, and the optimal exposure conditions for each used component are different. We first develop a region-of-interest (ROI) extraction module capable of identifying ROIs under poor light exposure, in which the states of captured images under various lighting conditions are hypothesised to enhance the extraction ability of a deep learning-based object detector. The extraction results can help a robot obtain an optimal capture position and are incorporated with information about entropy to assess the image quality of ROIs in the proposed ROI quality assessment module. We further design an exposure-entropy prediction model based on predictive learning. This lightweight model is crucial in assisting the exposure time prediction module to achieve real-time searching for the optimal exposure time. The performance of the proposed exposure control method is validated using a screw-removal case study in the application to end-of-life electric vehicle battery disassembly. Together with the ROI extraction module and the ROI quality assessment module, the exposure time prediction module enables the accurate and efficient estimation of optimal exposure time and delivers high-quality images under poor lighting conditions. With our exposure control method, the robot vision system achieves satisfactory performance in the robotic disassembly of electric vehicle batteries.