Predicting object properties based on movement kinematics.

In order to grasp and transport an object, grip and load forces must be scaled according to the object's properties (such as weight). To select the appropriate grip and load forces, the object weight is estimated based on experience or, in the case of robots, usually by use of image recognition. We propose a new approach that makes a robot's weight estimation less dependent on prior learning and, thereby, allows it to successfully grasp a wider variety of objects. This study evaluates whether it is feasible to predict an object's weight class in a replacement task based on the time series of upper body angles of the active arm or on object velocity profiles. Furthermore, we wanted to investigate how prediction accuracy is affected by (i) the length of the time series and (ii) different cross-validation (CV) procedures. To this end, we recorded and analyzed the movement kinematics of 12 participants during a replacement task. The participants' kinematics were recorded by an optical motion tracking system while transporting an object, 80 times in total from varying starting positions to a predefined end position on a table. The object's weight was modified (made lighter and heavier) without changing the object's visual appearance. Throughout the experiment, the object's weight (light/heavy) was randomly changed without the participant's knowledge. To predict the object's weight class, we used a discrete cosine transform to smooth and compress the time series and a support vector machine for supervised learning from the achieved discrete cosine transform parameters. Results showed good prediction accuracy (up to [Formula: see text], depending on the CV procedure and the length of the time series). Even at the beginning of a movement (after only 300 ms), we were able to predict the object weight reliably (within a classification rate of [Formula: see text]).

Unravelling colloid filter cake motions in membrane cleaning procedures.

The filtration performance of soft colloid suspensions suffers from the agglomeration of the colloids on the membrane surface as filter cakes. Backflushing of fluid through the membrane and cross-flow flushing across the membrane are widely used methods to temporally remove the filter cake and restore the flux through the membrane. However, the phenomena occurring during the recovery of the filtration performance are not yet fully described. In this study, we filtrate poly(N-isopropylacrylamide) microgels and analyze the filter cake in terms of its composition and its dynamic mobility during removal using on-line laser scanning confocal microscopy. First, we observe uniform cake build-up that displays highly ordered and amorphous regions in the cake layer. Second, backflushing removes the cake in coherent pieces and their sizes depend on the previous cake build-up. And third, cross-flow flushing along the cake induces a pattern of longitudinal ridges on the cake surface, which depends on the cross-flow velocity and accelerates cake removal. These observations give insight into soft colloid filter cake arrangement and reveal the cake's unique behaviour exposed to shear-stress.