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Item type:Publication, Access status: with files , ASV-aided AUV navigation: A field study on nonlinear estimation for localization of low-cost, scalable systems(IEEE, 2025-11-27); ; ; ; Benjamin, MichaelThis work investigates the use of multiple Autonomous Surface Vehicles (ASVs) as Communication/Navigation Aids (CNAs) to enhance the navigation and state estimation of an Autonomous Underwater Vehicle (AUV). Our approach builds on recent advancements in low-cost sensors and platforms, which enable novel AUV applications across fundamental science, commercial industries, and defense. We consider six different combinations of Kalman Filter and Factor Graph localization solutions on three datasets, covering 53 minutes and 3.1 kilometers of operation. We first present the solution using the measurements from all three ASVs, before occluding measurements from two of the ASVs to assess the effect of reduced observability on localization performance.Publicationtype: Conference Paper54 46 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Workshop: Adaptive Structures At Shore 2022 : 23rd & 24th of May 2022, Hamburg, Germany : conference proceedings(Technische Universität Hamburg, 2022-05-23); ; ; ; Adaptive structures at shore provide an opportunity to minimize the resource consumption and the impact on the marine ecosystems through an efficient adaptation to changing boundary conditions. A combination of impact prediction, monitoring and material resistance estimation, numerical modelling, and innovative design is the key to success. The workshop “Adaptive Structures at Shore 2022” hosted by Hamburg University of Technology (TUHH) brings together international scientists and professionals to discuss the latest developments in the field and to explore synergies.Publicationtype: Conference Proceedings309 173 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Learning, localization, and control of hydrobatic micro underwater robots for autonomous field exploration in confined environments(2022); Exploration and monitoring of hazardous environmental fields are among the most promising tasks to be performed by micro autonomous underwater vehicles (µAUVs). Despite recent progress, computationally efficient solutions for guidance, navigation, and control are largely unsolved for agile µAUVs. First, the hydrobatic micro robot platform HippoCampus is presented along with a control system that allows agile maneuvering in confined spaces. Furthermore, an embedded self-localization system is developed which consists of modules using visual, electromagnetic, and acoustic ranging. Finally, an informative path planning framework for autonomous field exploration with multiple robot agents is proposed. It combines a deep reinforcement learning planner with a stochastic representation of the environmental field.Publicationtype: Doctoral Thesis356 2512 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Nonlinear system identification of a furuta pendulum using machine learning techniquesUsually, dynamical systems can be described by differential equations. An accurate model is essential when designing and optimizing a controller. However, not every system can be modeled easily by physical models due to highly nonlinear behavior, such as friction or backlash. Then, a data based approach, such as machine learning, might be helpful. The focus in this work is set on modeling dynamical systems using neural networks and deep learning, which are growing subjects in research and industry to identify nonlinear dynamics.Publicationtype: Conference Paper237 281 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Localisation of unmanned underwater vehicles (UUVs) in complex and confined environments : a review(Multidisciplinary Digital Publishing Institute (MDPI), 2020-10-30); ; The inspection of aquatic environments is a challenging activity, which is made more difficult if the environment is complex or confined, such as those that are found in nuclear storage facilities and accident sites, marinas and boatyards, liquid storage tanks, or flooded tunnels and sewers. Human inspections of these environments are often dangerous or infeasible, so remote inspection using unmanned underwater vehicles (UUVs) is used. Due to access restrictions and environmental limitations, such as low illumination levels, turbidity, and a lack of salient features, traditional localisation systems that have been developed for use in large bodies of water cannot be used. This means that UUV capabilities are severely restricted to manually controlled low-quality visual inspections, generating non-geospatially located data. The localisation of UUVs in these environments would enable the autonomous behaviour and the development of accurate maps. This article presents a review of the state-of-the-art in localisation technologies for these environments and identifies areas of future research to overcome the challenges posed.Publicationtype: Journal ArticleScopus© Citations 30 247 542 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Predictability of vibration loads from experimental data by means of reduced vehicle models and machine learning(IEEE, 2020-09-28); ; ; ; Nowadays electric cars are in the spotlight of automotive research. In this context we consider data based approaches as tools to improve and facilitate the car design process. Hereby, we address the challenge of vibration load prediction for electric cars using neural network based machine learning (ML), a data-based frequency response function approach, and a hybrid combined model. We extensively study the challenging case of vibration load prediction of car components, such as the traction battery of an electric car. We show using experimental data from Fiat 500e and VWeGolf cars that the proposed ML approach is able to outperform the classical model estimation by means of ARX and ARMAX models. Moreover, we evaluate the performance of a hybrid-ML concept for combination of ML and ARMAX. Our promising results motivate further research in the field of vibration load prediction using machine learning based approaches in order to facilitate design processes.Publicationtype: Journal ArticleScopus© Citations 5 258 225 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Learning environmental field exploration with computationally constrained underwater robots : Gaussian processes meet stochastic optimal control(Multidisciplinary Digital Publishing Institute (MDPI), 2019-05-06); ; ; Autonomous exploration of environmental fields is one of the most promising tasks to be performed by fleets of mobile underwater robots. The goal is to maximize the information gain during the exploration process by integrating an information-metric into the path-planning and control step. Therefore, the system maintains an internal belief representation of the environmental field which incorporates previously collected measurements from the real field. In contrast to surface robots, mobile underwater systems are forced to run all computations on-board due to the limited communication bandwidth in underwater domains. Thus, reducing the computational cost of field exploration algorithms constitutes a key challenge for in-field implementations on micro underwater robot teams. In this work, we present a computationally efficient exploration algorithm which utilizes field belief models based on Gaussian Processes, such as Gaussian Markov random fields or Kalman regression, to enable field estimation with constant computational cost over time. We extend the belief models by the use of weighted shape functions to directly incorporate spatially continuous field observations. The developed belief models function as information-theoretic value functions to enable path planning through stochastic optimal control with path integrals. We demonstrate the efficiency of our exploration algorithm in a series of simulations including the case of a stationary spatio-temporal field.Publicationtype: Journal ArticleScopus© Citations 13 445 401 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent(Hindawi Publishing Corporation, 2018-12-30); ; ; ; Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.Publicationtype: Journal ArticleScopus© Citations 15 1703 1525 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Embedded spherical localization for micro underwater vehicles based on attenuation of electro-magnetic carrier signals(Multidisciplinary Digital Publishing Institute (MDPI), 2017-04-26); ; ; ; Self-localization is one of the most challenging problems for deploying micro autonomous underwater vehicles (μAUV) in confined underwater environments. This paper extends a recently-developed self-localization method that is based on the attenuation of electro-magnetic waves, to the μAUV domain. We demonstrate a compact, low-cost architecture that is able to perform all signal processing steps present in the original method. The system is passive with one-way signal transmission and scales to possibly large μAUV fleets. It is based on the spherical localization concept. We present results from static and dynamic position estimation experiments and discuss the tradeoffs of the system.Publicationtype: Journal ArticleScopus© Citations 17 710 671