Reith-Braun, MarcelMarcelReith-BraunLiang, KevinKevinLiangPfaff, FlorianFlorianPfaffMaier, GeorgGeorgMaierGruna, RobinRobinGrunaBauer, AlbertAlbertBauerKruggel-Emden, HaraldHaraldKruggel-EmdenLängle, ThomasThomasLängleBeyerer, JürgenJürgenBeyererHanebeck, Uwe D.Uwe D.Hanebeck2024-03-192024-03-192024https://publica.fraunhofer.de/handle/publica/464289In current state of the art sensor-based sorting systems, the length of the deflection windows, i.e., the period of nozzle activation and the number of nozzles to be activated, is commonly determined solely by the size of the particles. However, this comes at the cost of the sorting process not accounting for model discrepancies between actual and presumed particle motion, as well as for situations where the available information does not allow for precise determination of nozzle activations. To achieve a desired sorting accuracy, in practice, one is therefore usually forced to enlarge the deflection window to a certain degree, which increases the number of falsely co-deflected particles and compressed air consumption. In this paper, we propose incorporating the uncertainty of the prediction of particle motion of each individual particle into the determination of the deflection windows. The method is based on the predictive tracking approach for optical sorting, which tracks the particles while they move toward the nozzle array based on images of an area-scan camera. Given the extracted motion information from the tracking, we propose an approximation for the distribution of arrival time and location of the particle at the nozzle array assuming nearly constant-velocity or nearly constantacceleration particle motion behavior. By evaluating the quantile function of both distributions, we obtain a confidence interval for the arrival time and location based on prediction uncertainty, which we then combine with the particle size to form the final deflection window. We apply our method to a real sorting task using a pilot-scale chute sorter. Our results obtained from extensive sorting trials show that sorting accuracies can be remarkably improved compared with state-of-the-art industrial sorters and enhanced even further compared with predictive tracking while having the potential to reduce compressed air consumption.enSensor-based sortingdeflection windowsuncertainty quantificationfirst-passage timeconstant-velocity modelconstant-acceleration modelconference paper