Heike Vallery

Christina Kohler, Michiel Plooij, Nuria Peña-Perez et al.

Designing robots for high-torque, high-fidelity haptic interaction is challenging. Parallel Elastic Actuators (PEAs) use elastic elements in parallel to smaller motors to complement torques, and Series Elastic Actuators (SEAs) use elastic elements in series to decouple motor impedance and improve force control. Recent work combines SEAs and PEAs to obtain both benefits but requires separate elastic elements or clutching. This paper presents the Series Parallel Integrated Nonlinear Elastic Actuator (SPINEA), which merges SEA and PEA such that a single elastic element takes on dual roles simultaneously, parallel and series. This is achieved by a nonlinear transmission in which the motor and load have misaligned rotation axes and are elastically connected. This geometry enables both high peak torque and precise torque tracking. We apply SPINEA to actuate lean of a haptic bicycle simulator, which requires high moments and precise rendering for safe and realistic rider interactions. We realized a prototype and performed experiments, both with an external excitation setup and with riders cycling. Our results confirm SPINEA's low impedance and precise torque tracking, up to 4.25 Hz with the bicycle frame fixed and up to 4 Hz with riders. The benefits may transfer to other applications requiring compact, high-performance actuation.

Chih-Chun Chen, Lipeng Tan, Shiyu Bai et al.

In Global Navigation Satellite System (GNSS)-degraded environments, pseudolites (PLs) provide additional signal sources to enhance positioning performance, but their integration in optimization-based frameworks remains limited. This paper presents a loosely coupled factor graph optimization (FGO) framework that fuses the GNSS/PL least-squares (LS) solutions with inertial measurement unit (IMU) data. The evaluation considers low GNSS visibility scenarios with four high-elevation GNSS satellites and up to two PL transmitters over an 80~s window. FGO achieves a 22.8\% to 41.3\% reduction in mean 3D error compared to standard LS methods. Compared to a GNSS-IMU baseline, incorporating PL transmitters further improves positioning accuracy, with performance depending on geometry.

Beatrice Luciani, Katherine Lin Poggensee, Heike Vallery et al.

Exoskeletons modulate human movement across diverse applications, from performance augmentation to daily-life assistance. These systems often enforce specific kinematic patterns to mitigate injury risks and motivate users to keep moving despite diminished capacity. However, little is known about users' perception of such robot-imposed guidance, especially when personalized to the uniqueness of individual human walk. Given the usually substantial computational cost for personalization, understanding its subjective impact is essential to justify its implementation over standard patterns. Ten unimpaired participants completed a within-subject experiment in a multi-planar treadmill-based exoskeleton that enforced three different gait patterns: personalized, standard, and a randomly selected pattern from a publicly available database. Personalization was achieved using a data-driven framework that predicts hip, knee, and pelvis trajectories from walking speed, anthropometric, and demographic data. The standard pattern was obtained by averaging gait patterns from the aforementioned database. After each condition, participants rated enjoyment, comfort, and perceived naturalness. Knee joint interaction forces were also recorded. Subjective ratings revealed no significant differences among patterns, despite all trajectories being executed with high accuracy. However, gait patterns experienced last were rated as significantly more comfortable and natural, indicating adaptation to the system. Higher interaction forces were observed only for the random vs. standard pattern. Personalizing gait kinematics had minimal short-term influence on user experience relative to the dominant effect of adaptation to the exoskeleton. These findings highlight the importance of integrating subjective feedback and accounting for user adaptation when designing personalized robot controllers.

Xu Chen, Kevin Kluge, Maximilian Basler et al.

This work addresses the challenge of ignition timing and load control in homogeneous charge compression ignition engines operating subject to uncertainty from complex combustion dynamics and external disturbances. To handle this issue, we propose a nonlinear stochastic model predictive control approach explicitly incorporating distributional information of uncertainties. Specifically, we integrate an uncertainty model learned from empirical residual data to capture realistic probabilistic characteristics and handle the nonlinear additive uncertainty propagation within the prediction horizon based on polynomial chaos expansion. Additionally, we introduce a novel cost function based on maximum mean discrepancy, enabling direct penalization of the discrepancy between predicted and desired distributions of combustion indicators. The simulation results demonstrate that our proposed method achieves over a 28 \% reduction on combustion phasing variation and more than a 26 \% improvement in load tracking accuracy compared to traditional nonlinear and Gaussian-based predictive control strategies. These findings indicate the effectiveness of explicitly modeling uncertainty distributions and highlight the advantages of distribution-level performance index in robust combustion control.

Haoming Zhang, Zhanxin Wang, Heike Vallery

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.

Marko Bjelonic, Prajish K. Sankar, C. Dario Bellicoso et al.

Wheeled-legged robots have the potential for highly agile and versatile locomotion. The combination of legs and wheels might be a solution for any real-world application requiring rapid, and long-distance mobility skills on challenging terrain. In this paper, we present an online trajectory optimization framework for wheeled quadrupedal robots capable of executing hybrid walking-driving locomotion strategies. By breaking down the optimization problem into a wheel and base trajectory planning, locomotion planning for high dimensional wheeled-legged robots becomes more tractable, can be solved in real-time on-board in a model predictive control fashion, and becomes robust against unpredicted disturbances. The reference motions are tracked by a hierarchical whole-body controller that sends torque commands to the robot. Our approach is verified on a quadrupedal robot with non-steerable wheels attached to its legs. The robot performs hybrid locomotion with a great variety of gait sequences on rough terrain. Besides, we validated the robotic platform at the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, where the robot rapidly mapped, navigated and explored dynamic underground environments.