A Method for Attitude Control Based on a Mathematical Model for an Inverted Pendulum-Type Mobile Robot

A method for attitude control based on a mathematical model for an inverted pendulum-type mobile robot was proposed. The inverted pendulum-type mobile robot was designed and the mathematical modeling was conducted. The parameters of the mobile robot were estimated and the state-space model of mobile robot was obtained by the substitution of the estimated parameters into the mathematical model. The transfer function of the mobile robot is applied to generate the root-locus diagram used for the estimation of the gains of the PID controller. The attitude control method including a PID controller, non-linear elements, and integral saturation prevention was designed and simulated. The experiment was conducted by applying the method to the mobile robot. In the attitude control experiment, the performance of attitude recovery from ±12° tilted initial state with a settling time of 0.98s and a percent overshoot of 40.1% was obtained. Furthermore, the attitude maintaining robustness against disturbance was verified

Control System for Auto Moving Vehicle using for Artificial Turf Ground Performance Test

Recently, use of artificial turf has been increasing in a variety of international games as well as the change of school playgrounds to artificial turf from clay. Artificial turf is needs to obtain a standard certification to hold international games. This standard certification is based on a shock performance test during a specified period. The control system of the auto moving vehicle using for artificial turf ground performance tests is proposed and the control algorithm was adopted to improve the trajectory following performance of the auto moving vehicle. The control algorithm was optimized by computer simulation. Each part of the control system was developed and integrated into the auto moving vehicle. Experiments were conducted to verify the performance of the control system. From the performance test, the control system had a positioning resolution of 0.05m, maximum velocity of 1m/sec, and a trajectory following error of ±1.3deg for operating conditions

Adaptive perturbation control with feedforward compensation for robot manipulators

An adaptive perturbation control can track a time-based joint trajectory as closely as possible for all times over a wide range of manipulator motion and payloads. The adaptive control is based on the linearized perturbation equations in the vicinity of a nominal trajectory. The highly coupled nonlinear dynamic equations of a manipulator are expanded in the vicinity of a nominal trajectory to obtain the perturbation equations. The controlled system is characterized by feedforward and feedback components which can be computed separately and simulta neously. Given the joint trajectory set points, the feedforward component computes the corresponding nominal torques from the Newton-Euler equations of motion to compensate for all the interactions between joints. The feedback component, consisting of recursive least square identification and an optimal adaptive self-tuning control algorithm for the linearized system, computes the perturbation torques which reduce the position and veloc ity errors of the manipulator along the nominal trajectory. Because of the parallel structure, computations of the adaptive control may be implemented in low-cost microprocessors. This adaptive control strategy reduces the manipulator control prob lem from a nonlinear control to controlling a linear control system about a desired trajectory. Computer simulation results demonstrated its applicability to a three-joint PUMA robot arm.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68750/2/10.1177_003754978504400303.pd

Improved Chest Anomaly Localization without Pixel-level Annotation via Image Translation Network Application in Pseudo-paired Registration Domain

Image translation based on a generative adversarial network (GAN-IT) is a promising method for the precise localization of abnormal regions in chest X-ray images (AL-CXR) even without pixel-level annotation. However, heterogeneous unpaired datasets undermine existing methods to extract key features and distinguish normal from abnormal cases, resulting in inaccurate and unstable AL-CXR. To address this problem, we propose an improved two-stage GAN-IT involving registration and data augmentation. For the first stage, we introduce an advanced deep-learning-based registration technique that virtually and reasonably converts unpaired data into paired data for learning registration maps, by sequentially utilizing linear-based global and uniform coordinate transformation and AI-based non-linear coordinate fine-tuning. This approach enables the independent and complex coordinate transformation of each detailed location of the lung while recognizing the entire lung structure, thereby achieving higher registration performance with resolving inherent artifacts caused by unpaired conditions. For the second stage, we apply data augmentation to diversify anomaly locations by swapping the left and right lung regions on the uniform registered frames, further improving the performance by alleviating imbalance in data distribution showing left and right lung lesions. The proposed method is model agnostic and shows consistent AL-CXR performance improvement in representative AI models. Therefore, we believe GAN-IT for AL-CXR can be clinically implemented by using our basis framework, even if learning data are scarce or difficult for the pixel-level disease annotation

Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

BackgroundMagnetic resonance imaging (MRI) is important for the early detection of axial spondyloarthritis (axSpA). We developed an artificial intelligence (AI) model for detecting sacroiliitis in patients with axSpA using MRI.MethodsThis study included MRI examinations of patients who underwent semi-coronal MRI scans of the sacroiliac joints owing to chronic back pain with short tau inversion recovery (STIR) sequences between January 2010 and December 2021. Sacroiliitis was defined as a positive MRI finding according to the ASAS classification criteria for axSpA. We developed a two-stage framework. First, the Faster R-CNN network extracted regions of interest (ROIs) to localize the sacroiliac joints. Maximum intensity projection (MIP) of three consecutive slices was used to mimic the reading of two adjacent slices. Second, the VGG-19 network determined the presence of sacroiliitis in localized ROIs. We augmented the positive dataset six-fold. The sacroiliitis classification performance was measured using the sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). The prediction models were evaluated using three-round three-fold cross-validation.ResultsA total of 296 participants with 4,746 MRI slices were included in the study. Sacroiliitis was identified in 864 MRI slices of 119 participants. The mean sensitivity, specificity, and AUROC for the detection of sacroiliitis were 0.725 (95% CI, 0.705–0.745), 0.936 (95% CI, 0.924–0.947), and 0.830 (95%CI, 0.792–0.868), respectively, at the image level and 0.947 (95% CI, 0.912–0.982), 0.691 (95% CI, 0.603–0.779), and 0.816 (95% CI, 0.776–0.856), respectively, at the patient level. In the original model, without using MIP and dataset augmentation, the mean sensitivity, specificity, and AUROC were 0.517 (95% CI, 0.493–0.780), 0.944 (95% CI, 0.933–0.955), and 0.731 (95% CI, 0.681–0.780), respectively, at the image level and 0.806 (95% CI, 0.729–0.883), 0.617 (95% CI, 0.523–0.711), and 0.711 (95% CI, 0.660–0.763), respectively, at the patient level. The performance was improved by MIP techniques and data augmentation.ConclusionAn AI model was developed for the detection of sacroiliitis using MRI, compatible with the ASAS criteria for axSpA, with the potential to aid MRI application in a wider clinical setting