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Item type:Publication, Access status: without files , From Aerobatics to Hydrobatics: Agile Trajectory Planning and Tracking for Micro Underwater Robots(2021-09); ; Aerobatic quadrotors have been a very active field of research for the last two decades. Their huge community boosted the development of computational light-weight planning and control algorithms. In contrast and despite recent progress, research on agile micro autonomous underwater vehicles (μAUV) is still in its infancy. Both vehicle classes share a close relationship. They achieve high speeds of multiple bodylengths per second. At the same time they are subject to limited onboard resources such as sensors and computing power.In this work, we explore and exploit the potential synergies between aerobatic drones and hydrobatic μAUVs. In order to demonstrate the possible transfer of concepts we build on a state-of-the-art quadrotor trajectory planning framework and extend it to incorporate hydrodynamic effects. Furthermore, we study in a series of experiments the performance of the transferred concepts and show that various quadrotor simplifications match well for hydrobatic μAUVs.Publicationtype: Conference Paper76 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Embedded Stochastic Field Exploration with Micro Diving Agents using Bayesian Optimization-Guided Tree-Search and GMRFs(2021-09); ; ; Exploration and monitoring of hazardous fields in marine environments is one of the most promising tasks to be performed by fleets of low-cost micro autonomous underwater vehicles (μAUVs). In contrast to vehicles in other domains, underwater robots are forced to perform all computations onboard as no powerful communication links are available underwater. This puts the focus on computationally efficient field exploration algorithms. We propose CBTS-GMRF-an extremely light-weight tree-search exploration framework suitable for embedded computing. With our framework we build on recent work in POMDP-exploration and field belief representations based on efficient Gaussian Markov random fields (GMRF). We propose a reward function for energy-efficient field exploration together with a sparse trajectory parameterization. By reducing both, energy consumption and computational complexity, we enable underwater field exploration with μAUVs. We benchmark the performance of our exploration framework in simulation against state-of-the-art exploratory planning schemes and provide an experimental study using a low-cost micro diving agent. In order to support community-wide algorithm benchmarking, our code and robot design can be accessed online.Publicationtype: Conference Paper75 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Micro AUV Localization for Agile Navigation with Low-cost Acoustic ModemsHigh-rate self-localization for agile navigation still constitutes a major bottleneck in micro autonomous underwater vehicle (μAUV) research. Hereby, key challenges arise from the system being small-size and low-cost, which excludes common approaches such as relying on sophisticated ring-laser gyroscopes. In this context, we investigate two promising sensor fusion strategies for μAUVs based on low-cost sensors and motion models. We benchmark our findings in experiments against a baseline linear least-square estimator and a GPS reference track.Publicationtype: Conference Paper218 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards an Open-Source Micro Robot Oceanarium: A Low-Cost, Modular, and Mobile Underwater Motion-Capture SystemMicro autonomous underwater vehicles (μAUVs) allow early-stage experimental testing even in small tanks. In contrast to large facilities, these tanks are usually not equipped with high-fidelity motion capture systems due to high cost and reduced required accuracy for proof-of-concept testing.In this work, we introduce low-cost and open-source motion capture architecture based on fiducial markers for small research tanks. We propose a highly modular approach which allows straight-forward adaptation to individual user needs. We demonstrate the performance of our architecture in two experimental setups.Publicationtype: Conference Paper270 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Towards reinforcement learning-based control of an energy harvesting pendulum(2019-06); ; ; Harvesting energy from the environment, e.g. ocean waves, is a key capability for the long-term operation of remote electronic systems where standard energy supply is not available. Rotating pendulums can be used as energy converters when excited close to their eigenfrequency. However, to ensure robust operation of the harvester, the energy of the dynamic system has to be controlled. In this study, we deploy a light-weight reinforcement learning algorithm to drive the energy of an Acrobot pendulum towards a desired value. We analyze the algorithm in an extensive series of simulations. Moreover, we explore the real world application of our energy-based reinforcement learning algorithm using a computationally constrained hardware setup based on low-cost components, such as the Raspberry Pi platform.Publicationtype: Conference Paper201 - 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 13 446 402 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , An integrated approach to navigation and control in micro underwater robotics using radio-frequency localizationNavigation and control are a largely unsolved problems for micro autonomous underwater vehicles (). The main challenges are due to the lack of accurate underwater localization systems, which fit on-board of . In this work, we present an integrated navigation and control architecture consisting of a low-cost embedded localization module and an underwater way-point tracking controller, which fulfills the requirements of . The performance of the navigation and control system is benchmarked in two different experimental scenarios.Publicationtype: Conference Paper211 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Reinforcement learning of depth stabilization with a micro diving agent(IEEE, 2018-09-10); ; ; ; Reinforcement learning (RL) allows robots to solve control tasks through interaction with their environment. In this paper we study a model-based value-function RL approach, which is suitable for computationally limited robots and light embedded systems. We develop a diving agent, which uses the RL algorithm for underwater depth stabilization. Simulations and experiments with the micro diving agent demonstrate its ability to learn the depth stabilization task.Publicationtype: Conference Paper208 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Micro underwater vehicle hydrobatics: a submerged furuta pendulum(IEEE, 2018-09); ; ; ; We present the new HippoCampus micro underwater vehicle, first introduced in [1]. It is designed for monitoring confined fluid volumes. These tightly constrained settings demand agile vehicle dynamics. Moreover, we adapt a robust attitude control scheme for aerial drones to the underwater domain. We demonstrate the performance of the controller with a challenging maneuver. A submerged Furuta pendulum is stabilized by HippoCampus after a swing-up. The experimental results reveal the robustness of the control method, as the system quickly recovers from strong physical disturbances, which are applied to the system.Publicationtype: Conference Paper329 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Low-cost monocular localization with active markers for micro autonomous underwater vehiclesWe present an approach for estimating the absolute poses of a swarm Micro Autonomous Underwater Vehicles (μAUVs) by decomposing the problem into few absolute position estimations and many relative pose estimations. As power constraints are critical to small mobile robots, we develop an extension of active marker pose estimation using color information to solve the marker correspondence problem, and show that this approach is more energy efficient than reflective estimation approaches. We show the feasibility of this approach by localizing a robot navigating in an underwater test tank environment. Detailed analysis is presented characterizing the noise and error properties when estimating robot poses from fixed on-board markers. Moreover, we provide comparisons in power and computational cost for other popular methods of underwater localization.Publicationtype: Conference Paper143