Learning-based NLOS Detection and Uncertainty Prediction of GNSS Observations with Transformer-Enhanced LSTM Network

The global navigation satellite systems (GNSS) play a vital role in transport systems for accurate and consistent vehicle localization. However, GNSS observations can be distorted due to multipath effects and non-line-of-sight (NLOS) receptions in challenging environments such as urban canyons. In such cases, traditional methods to classify and exclude faulty GNSS observations may fail, leading to unreliable state estimation and unsafe system operations. This work proposes a Deep-Learning-based method to detect NLOS receptions and predict GNSS pseudorange errors by analyzing GNSS observations as a spatio-temporal modeling problem. Compared to previous works, we construct a transformer-like attention mechanism to enhance the long short-term memory (LSTM) networks, improving model performance and generalization. For the training and evaluation of the proposed network, we used labeled datasets from the cities of Hong Kong and Aachen. We also introduce a dataset generation process to label the GNSS observations using lidar maps. In experimental studies, we compare the proposed network with a deep-learning-based model and classical machine-learning models. Furthermore, we conduct ablation studies of our network components and integrate the NLOS detection with data out-of-distribution in a state estimator. As a result, our network presents improved precision and recall ratios compared to other models. Additionally, we show that the proposed method avoids trajectory divergence in real-world vehicle localization by classifying and excluding NLOS observations.Comment: Accepted for the IEEE ITSC202

Adaptive Patientenunterstützung für Rehabilitationsroboter

Rehabilitationsroboter unterstützen die Rehabilitation von Patienten mit Bewegungsstörungen aufgrund von Schädigungen des Nervensystems. Neu entwickelte, patientenkooperative Regelungsansätze sollen es diesen Robotern ermöglichen, individuell an die Patienten angepasste, effektivere Trainingseinheiten durchzuführen, als dies bislang möglich war. Dieser Beitrag beschreibt zwei Ansätze zur automatischen Anpassung der Roboterunterstützung: Die iterativ lernende Vorsteuerung ermöglicht die Unterstützung von Bewegungen mit definiertem zeitlichem Ablauf. Das iterativ lernende, konservative Kraftfeld ermöglicht die Unterstützung von Bewegungen mit freiem zeitlichem Ablauf. Das Verhalten beider Verfahren wird an einer Beispielanwendung mit dem Gang-Rehabilitationsroboter Lokomat demonstrier

Cooperative Control Design for Robot-Assisted Balance During Gait

Avoiding falls is a challenge for many persons in aging societies, and balance dysfunction is a major risk factor. Robotic solutions to assist human gait, however, focus on average kinematics, and less on instantaneous balance reactions. We propose a controller that only intervenes when needed, and that avoids stability issues when interacting with humans: Assistance is triggered only when balance is lost, and this action is purely feed-forward. Experiments show that subjects who start falling during gait can be uprighted by such feed-forward assistive force

Künstliches Feedback für Oberschenkelamputierte - Theoretische Analyse

Dieser Beitrag untersucht auf Basis von Modellen der menschlichen Wahrnehmung den Einfluss künstlichen sensorischen Feedbacks auf posturale Kontrolle und Gangsymmetrie von Oberschenkelamputierten. In der Standphase wird ein vereinfachtes, statisches neuromechanisches Modell verwendet, in der Schwungphase ein Erweitertes Kalman- Filter, das dynamische Effekte berücksichtigt. Die Simulation lässt den Schluss zu, dass Rückmeldung des Fußdruckpunktes während der Standphase die Wahrnehmung verbessern könnte, künstliches Feedback während der Schwungphase jedoch nicht von Vorteil ist. Eine klinische Fallstudie wäre nötig, um die in der Simulation beobachteten Effekte sensorischen Feedbacks in der praktischen Anwendung mit Amputierten zu überprüfe

GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization

Accurate and robust vehicle localization in highly urbanized areas is challenging. Sensors are often corrupted in those complicated and large-scale environments. This paper introduces GNSS-FGO, an online and global trajectory estimator that fuses GNSS observations alongside multiple sensor measurements for robust vehicle localization. In GNSS-FGO, we fuse asynchronous sensor measurements into the graph with a continuous-time trajectory representation using Gaussian process regression. This enables querying states at arbitrary timestamps so that sensor observations are fused without requiring strict state and measurement synchronization. Thus, the proposed method presents a generalized factor graph for multi-sensor fusion. To evaluate and study different GNSS fusion strategies, we fuse GNSS measurements in loose and tight coupling with a speed sensor, IMU, and lidar-odometry. We employed datasets from measurement campaigns in Aachen, Duesseldorf, and Cologne in experimental studies and presented comprehensive discussions on sensor observations, smoother types, and hyperparameter tuning. Our results show that the proposed approach enables robust trajectory estimation in dense urban areas, where the classic multi-sensor fusion method fails due to sensor degradation. In a test sequence containing a 17km route through Aachen, the proposed method results in a mean 2D positioning error of 0.19m for loosely coupled GNSS fusion and 0.48m while fusing raw GNSS observations with lidar odometry in tight coupling.Comment: Revision of arXiv:2211.0540

Complementary limb motion estimation for the control of active knee prostheses

To restore walking after transfemoral amputation, various actuated exoprostheses have been developed, which control the knee torque actively or via variable damping. In both cases, an important issue is to find the appropriate control that enables user-dominated gait. Recently, we suggested a generic method to deduce intended motion of impaired or amputated limbs from residual human body motion. Based on interjoint coordination in physiological gait, statistical regression is used to estimate missing motion. In a pilot study, this complementary limb motion estimation (CLME) strategy is applied to control an active knee exoprosthesis. A motor-driven prosthetic knee with one degree of freedom has been realized, and one above-knee amputee has used it with CLME. Performed tasks are walking on a treadmill and alternating stair ascent and descent. The subject was able to walk on the treadmill at varying speeds, but needed assistance with the stairs, especially to descend. The promising results with CLME are compared with the subject's performance with her own prosthesis, the C-Leg from Otto Boc

Performance of a Mobile 3D Camera to Evaluate Simulated Pathological Gait in Practical Scenarios

Three-dimensional (3D) cameras used for gait assessment obviate the need for bodily markers or sensors, making them particularly interesting for clinical applications. Due to their limited field of view, their application has predominantly focused on evaluating gait patterns within short walking distances. However, assessment of gait consistency requires testing over a longer walking distance. The aim of this study is to validate the accuracy for gait assessment of a previously developed method that determines walking spatiotemporal parameters and kinematics measured with a 3D camera mounted on a mobile robot base (ROBOGait). Walking parameters measured with this system were compared with measurements with Xsens IMUs. The experiments were performed on a non-linear corridor of approximately 50 m, resembling the environment of a conventional rehabilitation facility. Eleven individuals exhibiting normal motor function were recruited to walk and to simulate gait patterns representative of common neurological conditions: Cerebral Palsy, Multiple Sclerosis, and Cerebellar Ataxia. Generalized estimating equations were used to determine statistical differences between the measurement systems and between walking conditions. When comparing walking parameters between paired measures of the systems, significant differences were found for eight out of 18 descriptors: range of motion (ROM) of trunk and pelvis tilt, maximum knee flexion in loading response, knee position at toe-off, stride length, step time, cadence; and stance duration. When analyzing how ROBOGait can distinguish simulated pathological gait from physiological gait, a mean accuracy of 70.4%, a sensitivity of 49.3%, and a specificity of 74.4% were found when compared with the Xsens system. The most important gait abnormalities related to the clinical conditions were successfully detected by ROBOGait. The descriptors that best distinguished simulated pathological walking from normal walking in both systems were step width and stride length. This study underscores the promising potential of 3D cameras and encourages exploring their use in clinical gait analysis.Biomechatronics & Human-Machine Contro

Light-Weight Wearable Gyroscopic Actuators Can Modulate Balance Performance and Gait Characteristics:A Proof-of-Concept Study

Falling is a major cause of morbidity, and is often caused by a decrease in postural stability. A key component of postural stability is whole-body centroidal angular momentum, which can be influenced by control moment gyroscopes. In this proof-of-concept study, we explore the influence of our wearable robotic gyroscopic actuator “GyroPack” on the balance performance and gait characteristics of non-impaired individuals (seven female/eight male, 30 ± 7 years, 68.8 ± 8.4 kg). Participants performed a series of balance and walking tasks with and without wearing the GyroPack. The device displayed various control modes, which were hypothesised to positively, negatively, or neutrally impact postural control. When configured as a damper, the GyroPack increased mediolateral standing time and walking distance, on a balance beam, and decreased trunk angular velocity variability, while walking on a treadmill. When configured as a negative damper, both peak trunk angular rate and trunk angular velocity variability increased during treadmill walking. This exploratory study shows that gyroscopic actuators can influence balance and gait kinematics. Our results mirror the findings of our earlier studies; though, with more than 50% mass reduction of the device, practical and clinical applicability now appears within reach.</p