Technische Universität München

Das Handbuch Organisationssoziologie liefert einen umfassenden Überblick über die Entwicklung, den Stand und die Zukunft der Organisationssoziologie als wissenschaftliche Disziplin. Dabei geht es sowohl um die systematische Aufnahme relevanter Theoriestränge, Methoden und Konzepte als auch um die Wechselbeziehungen, Überschneidungen und Komplementaritäten zu Nachbardisziplinen, die in einem Dialog aufgenommen werden. Das Handbuch vermittelt so einen eigenständigen Zugriff auf die Organisationssoziologie und bündelt gleichzeitig dessen Wissen auf dem neuesten Stand. Darüber soll es zu einem Standardwerk zur Organisationssoziologie im deutschsprachigen Raum werden.

Four-switch three-phase inverter (FSTPI) is widely applied as a fault-tolerant topology of six-switch three-phase inverter (SSTPI) with an open-phase fault or as a cost-effective topology due to the reduced semiconductor power switches. However, conventional single-vector-based predictive torque control (SV-PTC) for FSTPI-fed induction motor (IM) presents poor steady-state performance and requires high sampling frequency. To address these issues, a dual-vector-based PTC (DV-PTC) for FSTPI-fed IM with adaptive switching instant is proposed in this article. Therein, two optimal voltage vectors are determined in each sampling period, and a simplification strategy for selecting the optimal voltage vectors is established. A switching instant adaptation scheme is developed to improve the steady-state performance of machine control. An optimized overmodulation strategy is proposed to improve the transient-state performance of machine control and reduce the computational burden in digital implementation. Extensive experimental studies validate the effectiveness and superiority of the proposed DV-PTC scheme.

Three-level neutral-point-clamped (3L-NPC) power converters are necessary interfaces to form micro-energy systems. Naturally, designing a suitable control scheme, featuring superior dynamics, strong robustness, and simple structure, is a promising solution to guarantee more efficient operation of converter. This paper proposes a robust high-quality current control strategy for the 3L-NPC power converter in the stationary $\alpha \beta$ frame. A super-twisting algorithm coupled with a Luenberger observer current controller is proposed to deal with the poor sinusoidal current tracking issue due to the existing inductance/grid frequency deviations and the disturbance of the sinusoidal dynamic nature. Additionally, an extended sliding mode disturbance observer based proportional control is built to dramatically enhance voltage regulation performance, in the case of capacitance deviations and unknown dc-loads. Experimental data confirm the effectiveness of the proposed solution outperforms the conventional proportional-resonant/-integral control in terms of accurate tracking current/voltage, anti-disturbance, and grid current total harmonic distortion.

Deformable Linear Objects (DLOs) such as cables, wires, ropes, and elastic tubes are numerously present both in domestic and industrial environments. Unfortunately, robotic systems handling DLOs are rare and have limited capabilities due to the challenging nature of perceiving them. Hence, we propose a novel approach named RT-DLO for real-time instance segmentation of DLOs. First, the DLOs are semantically segmented from the background. Afterward, a novel method to separate the DLO instances is applied. It employs the generation of a graph representation of the scene given the semantic mask where the graph nodes are sampled from the DLOs center-lines whereas the graph edges are selected based on topological reasoning. RT-DLO is experimentally evaluated against both DLO-specific and general-purpose instance segmentation deep learning approaches, achieving overall better performances in terms of accuracy and inference time.

This article presents a direct model predictive con-trol (MPC) scheme for drive systems consisting of a three-phase three-level neutral-point-clamped (3L-NPC) inverter and an induction machine (IM). Even though the discussed MPC algorithm is a direct control strategy, it operates the inverter at a fixed switching frequency, while the output harmonic spectrum of the stator current is discrete, with harmonics at non-triplen, odd integer multiples of the fundamental frequency. As a result, the proposed method achieves similar or superior steady-state behavior than that of modulator-based control schemes. Moreover, thanks to its direct control nature, it exhibits the fast transient responses that characterize direct controllers due to the absence of an explicit modulator. Furthermore, the multiple control objectives of the system, i.e., stator current control and neutral point (NP) potential balancing, are addressed in one computational stage, thus avoiding any additional control loops in a cascaded or parallel structure. This favorable control structure is facilitated by the adopted modeling approach, according to which the system behavior is described by the gradient of the system output. In doing so, not only a simple, versatile system model is derived, but also the direct MPC can be formulated as a constrained quadratic program (QP), which can be easily solved in real time with an in-house solver. The effectiveness of the proposed control scheme is experimentally verified on a 4-kW drive system.

This paper focuses on indirect model predictive control (MPC) for variable speed drives, such as induction and synchronous machine drives. The optimization problem underlying indirect MPC is typically written as a standard constrained quadratic programming (QP) problem, which requires a QP solver to find the optimal solution. Although many mature QP solvers exist, solving the QP problems in industrial real-time embedded systems in a matter of a few tens of microseconds remains challenging. Instead of using the complex general-purpose QP solvers, this paper proposes a geometrical method for isotropic machine drives and an analytical method for anisotropic machine drives to find the optimal output voltage. This is done by examining and subsequently exploiting the geometry of the associated optimization problems. Both methods are simple, and easy to implement on industrial control platforms. The effectiveness of the proposed geometrical and analytical methods is demonstrated by experimental results for an induction machine drive and an interior permanent-magnet synchronous machine drive, respectively. Index Terms-Model predictive control (MPC), quadratic programming (QP), induction machine (IM), interior permanent-magnet synchronous machine (IPMSM).

Anticipating the motion of all humans in dynamic environments such as homes and offices is critical to enable safe and effective robot navigation. Such spaces remain challenging as humans do not follow strict rules of motion and there are often multiple occluded entry points such as corners and doors that create opportunities for sudden encounters. In this work, we present a Transformer based architecture to predict human future trajectories in human-centric environments from input features including human positions, head orientations, and 3D skeletal keypoints from onboard in-the-wild sensory information. The resulting model captures the inherent uncertainty for future human trajectory prediction and achieves state-of-the-art performance on common prediction benchmarks and a human tracking dataset captured from a mobile robot adapted for the prediction task. Furthermore, we identify new agents with limited historical data as a major contributor to error and demonstrate the complementary nature of 3D skeletal poses in reducing prediction error in such challenging scenarios. Project page: https://human-scene-transformer.github.io/</uri

When providing task demonstrations to a remote robot over the network via bilateral teleoperation, communication impairments are unavoidable, hindering the human operator from delivering high-quality demonstrations. Poor-quality demonstrations can negatively impact the robot's ability to learn and generalize. In this work, we propose to enhance learning performance by introducing a network-aware confidence weighting strategy for remote learning from demonstration. Our approach extends the Hidden Semi-Markov Model (HSMM) and its task-parameterized version (TP-HSMM) to their confidence-weighted versions, WHSMM and WTP-HSMM. We evaluated various weight metrics that serve as teleoperation transparency measures and demonstration quality indicators under varying communication delays. We validated the proposed approach in two different in-contact tasks using data collected from 18 participants. The results show that weighting improves task performance in reproduction by up to 42% in the force precision and 63% in the success rate, demonstrating the potential of the proposed approach to enhance the effectiveness of robot learning from remote demonstrations.

In this paper, we propose an effcient continuous-time LiDAR-Inertial-Camera Odometry, utilizing non-uniform B-splines to tightly couple measurements from the LiDAR, IMU, and camera. In contrast to uniform B-spline-based continuous-time methods, our non-uniform B-spline approach offers signifcant advantages in terms of achieving real-time effciency and high accuracy. This is accomplished by dynamically and adaptively placing control points, taking into account the varying dynamics of the motion. To enable effcient fusion of heterogeneous LiDAR-Inertial-Camera data within a short sliding-window optimization, we assign depth to visual pixels using corresponding map points from a global LiDAR map, and formulate frame-to-map reprojection factors for the associated pixels in the current image frame. This way circumvents the necessity for depth optimization of visual pixels, which typically entails a lengthy sliding window with numerous control points for continuous-time trajectory estimation. We conduct dedicated experiments on real-world datasets to demonstrate the advantage and effcacy of adopting non-uniform continuous-time trajectory representation. Our LiDAR-Inertial-Camera odometry system is also extensively evaluated on both challenging scenarios with sensor degenerations and large-scale scenarios, and has shown comparable or higher accuracy than the state-of-the-art methods. The codebase of this paper will also be open-sourced at https://github.com/APRIL-ZJU/Coco-LIC .

In safety-critical applications, microcontrollers have to be tested to satisfy strict quality and performances constraints. It has been demonstrated that on-chip ring oscillators can be used as speed monitors to reliably predict the performances. However, any machine-learning model is likely to be inaccurate if trained on an inadequate dataset, and labeling data for training is quite a costly process. In this paper, we present a methodology based on active learning to select the best samples to be included in the training set, significantly reducing the time and cost required. Moreover, since different speed measurements are available, we designed a multi-label technique to take advantage of their correlations. Experimental results demonstrate that the approach halves the training-set size, with respect to a random-labelling, while it increases the predictive accuracy, with respect to standard single-label machine-learning models.

This work presents SID-SLAM, a complete SLAM framework for RGB-D cameras. Our main contribution is a semi-direct approach that, for the first time, combines tightly and indistinctly photometric and feature-based image measurements. Additionally, SID-SLAM uses information metrics to reduce the state size with a minimal impact in the accuracy. Our evaluation on several public datasets shows that we achieve state-of-the-art performance regarding accuracy, robustness and computational footprint in CPU real time. In order to facilitate research on semi-direct SLAM, we record the Minimal Texture dataset, composed by RGB-D sequences challenging for current baselines and in which our pipeline excels.

Biological methanation of H2 and CO2 in trickle bed reactors is a promising energy conversion and storage approach that can support the energy transition towards a renewable-based system. Research in trickle bed reactor design and operation has significantly increased in recent years, but most studies were performed at laboratory scale and conditions. This review provides a comprehensive overview of the trickle bed reactor concept and current developments to support the decision-making process for future projects. In particular, the key design and operational parameters, such as trickling or nutrient provision, are presented, introducing the most recent advances. Furthermore, reactor operation, including the inoculation, long-term and dynamic operation, is described. To better assess the reactor upscaling, several parameters that enable reactor comparison are discussed. On the basis of this review, suitable operational strategies and further research needs were identified that will improve the overall trickle bed reactor performance.

This letter proposes the Multi-Area Hybrid Equivalent (MAHE) circuit partition scheme, which decouples interconnected power electronics blocks of series, parallel, and both into smaller independent subcircuits, overcoming the limitation of low efficiency in the intricate network of blocks. The MAHE scheme segregates each partition's series and parallel section apart from the internal section, then turns the series section into impedance and the parallel section into admittance. The proposed scheme is based on principles of impedance's additivity and Kirchhoff's Voltage Law (KVL) in series, and admittance's additivity and Kirchhoff's Current Law (KCL) in parallel, promoting the relationship of partitions from one-to-one to many-to-many and simplifying the calculation of interconnections. The Input Parallel and Output Series (IPOS) converter illustrates the scheme's principle and effectiveness. In simulations of IPOS converter, MAHE achieves efficient speed-ups compared with commercial simulation softwares.

The stability of Bernstein’s characterization of Gaussian distributions is extended to vectors by utilizing characteristic functions. Stability is used to develop a soft doubling argument that establishes the optimality of Gaussian vectors for certain communications channels with additive Gaussian noise, including two-receiver broadcast channels. One novelty is that the argument does not require the existence of distributions that achieve capacity.

This paper proposes a novel triple-port solid-state transformer (SST) topology based on a hybrid isolated modular multilevel converter (HI-MMC), which overcomes the limitation of voltage ratio R(pu) in the conventional MMC-based SST. By integrating a high-frequency link into the MMC structure and applying various types of isolated submodule (ISM), the proposed SST can function as a step-down rectifier, where UMVDC < UMVAC(p-p). In this case, the power electronics converters used to connect downstream MVDC-Link and distributed energy resources (DER) or battery storage require fewer active devices and may minimize the cost of protection equipment. In addition, the proposed SST retains the significant advantages of single-stage SSTs, such as single-stage power conversion, saving capacitor, and a simple control system. First, the topology and modulation strategy of two types of ISMs are discussed. In addition, this article provides a detailed analysis of operation principles, power flow, grid-tied control, and system reliability. Moreover, the proposed converter is compared to other SST topologies in terms of key performance indicators, such as peak efficiency, system volume, and number of active semiconductor devices. Finally, a 300 V/20 kW scaled-down prototype is developed, and the experimental results demonstrate the performance and verify the correctness of the proposed converter.

Limited resources and resulting energy crises occurring all over the world highlight the importance of energy efficiency in technological developments such as robotic manipulators. Efficient energy consumption of manipulators is necessary to make them affordable and spread their application in the future industry. Previously, the power consumption of the robot motion was the main factor considered in the evaluation of energy efficiency. Lately, the paradigm in industrial robotics shifted towards lightweight robot manipulators which require a new investigation on the disaggregation of robot energy consumption. In this paper, we propose a novel pipeline to identify and disaggregate the energy use of mechatronic devices and apply it to lightweight industrial robots. The proposed method allows the identification of the electronic components consumption, mechanical losses, electrical losses, and required mechanical energy for robot motion. We evaluate the pipeline and understand the distribution of energy consumption using four different manipulators, namely, Universal Robot's UR5e, UR10e, Franka Emika's FR3, and Kinova Gen3. The experimental results show that most of the energy (60 - 90%) is consumed by the electronic components of the robot control box. Using this knowledge, the approaches to further optimize their energy consumption need to shift towards efficient robot electronic design instead of efficient robot mass distribution or motion control. Finally, our disaggregation pipeline allows an understanding of the power consumption of any mechatronic device and thus enables deliberate optimization of energy consumption.

To enhance the integration of robots into daily human life and industrial settings, there is a growing focus on the development of robots capable of physical collaboration with humans. Studies have shown that haptic feedback serves as an essential channel of communication that allows humans to better collaborate with each other. In this study, we investigated the role of haptic communication in physical Human-Robot Interaction (pHRI) tasks, especially in the leader-follower role distribution. We have shown that participants adopted different roles when working with different agents. Haptic feedback promotes a more balanced role distribution between leaders and followers. Moreover, haptic feedback only improved coordination between humans and artificial agents when humans acted as followers. Our findings can potentially enhance robots' ability to anticipate human adaptation and improve their understanding of humans through haptic communication.

In this work, we present a learning method for both lateral and longitudinal motion control of an ego-vehicle for the task of vehicle pursuit. The car being controlled does not have a pre-defined route, rather it reactively adapts to follow a target vehicle while maintaining a safety distance. To train our model, we do not rely on steering labels recorded from an expert driver, but effectively leverage a classical controller as an offline label generation tool. In addition, we account for the errors in the predicted control values, which can lead to a loss of tracking and catastrophic crashes of the controlled vehicle. To this end, we propose an effective data augmentation approach, which allows to train a network that is capable of handling different views of the target vehicle. During the pursuit, the target vehicle is firstly localized using a Convolutional Neural Network. The network takes a single RGB image along with cars' velocities and estimates target vehicle's pose with respect to the ego-vehicle. This information is then fed to a Multi-Layer Perceptron, which regresses the control commands for the ego-vehicle, namely throttle and steering angle. We extensively validate our approach using the CARLA simulator on a wide range of terrains. Our method demonstrates real-time performance, robustness to different scenarios including unseen trajectories and high route completion. Project page containing code and multimedia can be publicly accessed here: https://changyaozhou.github.io/Autonomous-Vehicle-Pursuit/ .

Successful path planning for Unmanned Aerial Vehicles (UAVs) in challenging environments with narrow openings, such as disaster areas, requires attitude to be considered. State-of-the-art methods incorporate attitude only in the refinement stage. We introduce a first-of-a-kind global minimum cost path search method based on A* that considers attitude along the path. To make the problem tractable, our method exploits an adaptive and coarse-to-fine approach using global and local A* runs, plus an efficient method to introduce the UAV attitude in the process. We integrate our method with an SE(3) trajectory optimisation method based on a safe-flight-corridor, yielding a complete path planning pipeline. Extensive evaluation is undertaken using the AirSim flight simulator under closed loop control in a set of randomised maps, allowing us to quantitatively assess our method. We show that it achieves significantly higher success rates than the baselines, at a reduced computational burden.

A main challenge restricting the application of control barrier functions (CBFs) to complex scenarios is the absence of robustness against uncertainties induced by both measurements of the environment and robot dynamics. In this paper, we propose an uncertainty-aware, learning-based approach to construct a safe controller such that safety can be guaranteed with a high probability in a complex and unknown environment. Two types of CBFs, namely nominal CBF (NCBF) and uncertainty CBF (UCBF), are constructed by means of Gaussian processes (GPs) based on real-time measurements of the environment. They are then synthesized into an uncertainty-separated control barrier function (US-CBF), which serves as hard constraints in quadratic programming (QP) based controller. To handle the dynamic uncertainties, we exploit another GP to learn the residual dynamics of the robot. The mean prediction is then feedforwarded to the controller such that the residual dynamics can be compensated. The variance function is incorporated into the QP to constrain the trajectory of the robot within a high-confidence safety tube. Moreover, we prove that the solution to the QP is locally Lipschitz continuous, which guarantees a unique solution to the system. Our proposed method demonstrates good performance in addressing safe navigation tasks in highly very complex scenarios provided by the KITTI dataset. Additionally, its reliability and safety assurance have been verified in real-world scenarios using a quadrotor under external wind disturbance.