Learning-based Position and Stiffness Feedforward Control of Antagonistic Soft Pneumatic Actuators using Gaussian Processes

Variable stiffness actuator (VSA) designs are manifold. Conventional model-based control of these nonlinear systems is associated with high effort and design-dependent assumptions. In contrast, machine learning offers a promising alternative as models are trained on real measured data and nonlinearities are inherently taken into account. Our work presents a universal, learning-based approach for position and stiffness control of soft actuators. After introducing a soft pneumatic VSA, the model is learned with input-output data. For this purpose, a test bench was set up which enables automated measurement of the variable joint stiffness. During control, Gaussian processes are used to predict pressures for achieving desired position and stiffness. The feedforward error is on average 11.5% of the total pressure range and is compensated by feedback control. Experiments with the soft actuator show that the learning-based approach allows continuous adjustment of position and stiffness without model knowledge.© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Improved Calibration Procedure for Wireless Inertial Measurement Units without Precision Equipment

Inertial measurement units (IMUs) are used in medical applications for many different purposes. However, an IMU's measurement accuracy can degrade over time, entailing re-calibration. In their 2014 paper, Tedaldi et al. presented an IMU calibration method that does not require external precision equipment or complex procedures. This allows end-users or personnel without expert knowledge of inertial measurement to re-calibrate the sensors by placing them in several suitable but not precisely defined orientations. In this work, we present several improvements to Tedaldi's method, both on the algorithmic level and the calibration procedure: adaptions for low noise accelerometers, a calibration helper object, and packet loss compensation for wireless calibration. We applied the modified calibration procedure to our custom-built IMU platform and verified the consistency of results across multiple calibration runs. In order to minimize the time needed for re-calibration, we analyzed how the calibration result accuracy degrades when fewer calibration orientations are used. We found that N=12 different orientations are sufficient to achieve a very good calibration, and more orientations yielded only marginal improvements. This is a significant improvement compared to the 37 to 50 orientations recommended by Tedaldi. Thus, we were reduced the time required to calibrate a single IMU from ca. 5 minutes to less than 2 minutes without sacrificing any meaningful calibration accuracy

Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation

Porrmann F, Pilz S, Stella A, et al. Acceleration of the SPADE Method Using a Custom-Tailored FP-Growth Implementation. Frontiers in Neuroinformatics. 2021;15(15): 723406.The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude