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A ROS~2-based Navigation and Simulation Stack for the Robotino

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arXiv 2024年

作者： Borse, Saurabh Viehmann, Tarik Ferrein, Alexander Lakemeyer, Gerhard The Mobile Autonomous Systems and Cognitive Robotics Institute FH Aachen University of Applied Science Aachen52066 Germany Knowledge-Based Systems Group RWTH Aachen University Aachen52074 Germany

The Robotino, developed by Festo Didactic, serves as a versatile platform in education and research for mobile robotics tasks. However, there currently is no ROS 2 integration for the Robotino available. In this paper we describe our work on a Webots simulation environment for a Robotino platform extended by LIght Detection And Ranging (LIDAR) sensors. A ROS 2 integration and a pre-configured setup for localization and navigation using existing ROS packages from the Nav2 suite is provided. We validate our setup by comparing simulations with real-world experiments conducted by three Robotinos in a logistics environment in our lab. Additionally, we tested the setup using a ROS 2 hardware driver for the Robotino developed by team GRIPS of the RoboCup Logistics League. The results demonstrate the feasibility of using ROS 2 and Nav2 for navigation tasks on the Robotino platform showing great consistency between simulation and real-world performance. © 2024, CC BY.

关键词： Optical radar

Inferring Underwater Topography with FINN

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arXiv 2024年

作者： Horuz, Coşku Can Karlbauer, Matthias Praditia, Timothy Oladyshkin, Sergey Nowak, Wolfgang Otte, Sebastian Neuro-Cognitive Modeling University of Tübingen Germany Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany Department of Stochastic Simulation and Safety Research for Hydrosystems University of Stuttgart Germany

Spatiotemporal partial differential equations (PDEs) find extensive application across various scientific and engineering fields. While numerous models have emerged from both physics and machine learning (ML) communities, there is a growing trend towards integrating these approaches to develop hybrid architectures known as physics-aware machine learning models. Among these, the finite volume neural network (FINN) has emerged as a recent addition. FINN has proven to be particularly efficient in uncovering latent structures in data. In this study, we explore the capabilities of FINN in tackling the shallow-water equations, which simulates wave dynamics in coastal regions. Specifically, we investigate FINN’s efficacy to reconstruct underwater topography based on these particular wave equations. Our findings reveal that FINN exhibits a remarkable capacity to infer topography solely from wave dynamics, distinguishing itself from both conventional ML and physics-aware ML models. Our results underscore the potential of FINN in advancing our understanding of spatiotemporal phenomena and enhancing parametrization capabilities in related domains. © 2024, CC BY-NC-SA.

关键词： Wave equations

Balanced resonate-and-fire neurons 24

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Balanced resonate-and-fire neurons

Proceedings of the 41st International Conference on Machine Learning

作者： Saya Higuchi Sebastian Kairat Sander M. Bohté Sebastian Otte Adaptive AI Lab Institute of Robotics and Cognitive Systems University of Lübeck Germany Machine Learning Group Centrum Wiskunde & Informatica (CWI) Amsterdam The Netherlands

The resonate-and-fire (RF) neuron, introduced over two decades ago, is a simple, efficient, yet biologically plausible spiking neuron model, which can extract frequency patterns within the time domain due to its resonating membrane dynamics. However, previous RF formulations suffer from intrinsic shortcomings that limit effective learning and prevent exploiting the principled advantage of RF neurons. Here, we introduce the balanced RF (BRF) neuron, which alleviates some of the intrinsic limitations of vanilla RF neurons and demonstrates its effectiveness within recurrent spiking neural networks (RSNNs) on various sequence learning tasks. We show that networks of BRF neurons achieve overall higher task performance, produce only a fraction of the spikes, and require significantly fewer parameters as compared to modern RSNNs. Moreover, BRF-RSNN consistently provide much faster and more stable training convergence, even when bridging many hundreds of time steps during backpropagation through time (BPTT). These results underscore that our BRF-RSNN is a strong candidate for future large-scale RSNN architectures, further lines of research in SNN methodology, and more efficient hardware implementations.

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Optimization of Image Preprocessing and Background Influences using a Depth Camera for Person Re-Identification on a Mobile Robot

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Optimization of Image Preprocessing and Background Influence...

IEEE International Conference on Automation Science and Engineering (CASE)

作者： Sebastian Flores Zeynep Boztoprak Jana Jost Department of AI and Autonomous Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany Department of Robotics and Cognitive Systems Fraunhofer Institute for Material Flow and Logistics Dortmund Germany

In this paper, we optimize datasets with different image preprocessing techniques for person re-identification using the ResNet18 model on a mobile robot with limited hardware, e.g., computational power and depth camera. For this, we create 16 datasets for which we discovered that the inverted original images, from the IR gray value images of the depth camera, results in the highest values with an r1, r5 and mAP of 98.48 %,99.82 % and 80.86 %. Additionally, we explore the cross-dataset evaluation for the 16 datasets to examine the robustness of our model, which points to a low generalizability. The scores are associated with the similarity between the trained and evaluated dataset.

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Hybrid Learning of Time-Series Inverse Dynamics Models for Locally Isotropic Robot Motion

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arXiv 2022年

作者： Çallar, Tolga-Can Böttger, Sven Institute for Robotics and Cognitive Systems Universität zu Lübeck Germany

Applications of force control and motion planning often rely on an inverse dynamics model to represent the high-dimensional dynamic behavior of robots during motion. The widespread occurrence of low-velocity, small-scale, locally isotropic motion (LIMO) typically complicates the identification of appropriate models due to the exaggeration of dynamic effects and sensory perturbation caused by complex friction and phenomena of hysteresis, e.g., pertaining to joint elasticity. We propose a hybrid model learning base architecture combining a rigid body dynamics model identified by parametric regression and time-series neural network architectures based on multilayer-perceptron, LSTM, and Transformer topologies. Further, we introduce a novel joint-wise rotational history encoding, reinforcing temporal information to effectively model dynamic hysteresis. The models are evaluated on a KUKA iiwa 14 during algorithmically generated locally isotropic movements. Together with the rotational encoding, the proposed architectures outperform state-of-the-art baselines by a magnitude of 103 yielding an RMSE of 0.14 Nm. Leveraging the hybrid structure and time-series encoding capabilities, our approach allows for accurate torque estimation, indicating its applicability in critically force-sensitive applications during motion sequences exceeding the capacity of conventional inverse dynamics models while retaining trainability in face of scarce data and explainability due to the employed physics model prior. Copyright © 2022, The Authors. All rights reserved.

关键词： Time series

Embedded Hierarchical MPC for Autonomous Navigation

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arXiv 2024年

作者： Benders, Dennis Köhler, Johannes Niesten, Thijs Babuška, Robert Alonso-Mora, Javier Ferranti, Laura Department of Cognitive Robotics Delft University of Technology Delft2628 CD Netherlands Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Institute for Dynamic Systems and Control ETH Zürich CH-8092 Switzerland

To efficiently deploy robotic systems in society, mobile robots need to autonomously and safely move through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real-time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor’s embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive control systems

Modular-integrative modeling: a new framework for building brain models that blend biological realism and functional performance

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National Science Review 2024年第5期11卷 13-16页

作者： Mario Senden Sacha J.van Albada Giovanni Pezzulo Egidio Falotico Ibrahim Hashim Alexander Kroner Anno C.Kurth Pablo Lanillos Vaishnavi Narayanan Cyriel Pennartz Mihai A.Petrovici Lea Steffen Tonio Weidler Rainer Goebel Department of Cognitive Neuroscience Maastricht University Maastricht Brain Imaging Centre Maastricht University Institute of Neuroscience and Medicine(INM-6) and Institute for Advanced Simulation(IAS-6) and JARA-Institut Brain Structure-Function Relationships(INM-10) Jülich Research Center Institute of Zoology University of Cologne Institute of Cognitive Sciences and Technologies The Bio Robotics Institute Scuola Superiore Sant'Anna RWTH Aachen University Donders Institute for Brain Cognition and BehaviorRadboud University Cognitive and Systems Neuroscience Group Swammerdam Institute for Life Sciences University of Amsterdam Department of Physiology University of Bern FZI Research Center of Information Technology

To cope with the immense complexity of the brain, neuroscientific research often focuses on isolated brain structures or circumscribed perceptual, cognitive or motor functions [1]. This reductionist approach overlooks the intricate and multifaceted interplay among various structural and functional *** instance, even simple movements require the coordinated activation of numerous neuronal populations across multiple brain regions [2].

关键词： framework building modeling models brain that biological blend functional integrative

Sequential U-Net Architecture for Automatic Femoral Artery Segmentation in Ultrasound Images

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Sequential U-Net Architecture for Automatic Femoral Artery S...

作者： Estrada Lugo, Ana Bockelmann, Niclas Haxthausen, Felix Von University of Lübeck Lübeck Germany Institute for Robotics and Cognitive Systems University of Lübeck Lübeck Germany

This work compares three different approaches to automatically segment the femoral artery from 2D ultrasound images. Two of the architectures follow a sequential structure, where each ultrasound image is considered a slice of the whole vessel volume, and its previous segmentation result will be part of the input, thus leading to a spatial prior. The Dice score on test data show a better performance on the baseline U-Net (0.819) compared to the sequential U-Net approaches (0.633, 0.725) for the femoral artery segmentation. This could be attributed to the misalignment of the slices being used in those networks. A possible improvement could be assumed in the implementation of a spatially calibrated and tracked ultrasound probe. Overall, these results indicate promising approaches for an automatic segmentation of the femoral artery using 2D ultrasound data. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Ultrasonics

Like Robots, Like Humans: Pupil Dilation During Collaborative Object Manipulation

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Like Robots, Like Humans: Pupil Dilation During Collaborativ...

International Conference on Advanced robotics (ICAR)

作者： Linda Lastrico Nuno Ferreira Duarte Alessandro Carfí Francesco Rea Fulvio Mastrogiovanni José Santos-Victor Alessandra Sciutti Cognitive Architecture for Collaborative Technologies Unit (CONTACT) Italian Institute of Technology Genoa Italy Institute for Systems and Robotics (ISR) Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS)

It is well known that pupil dilation reflects an increase in cognitive load during tasks that demand mental effort. Inspired by this, we questioned whether the same effect occurs during social interactions, particularly in joint object manipulation tasks that require attention and coordination for successful completion. In the first scenario, we measure pupil dilation during human-human interactive actions (handover), either as the object giver or receiver, against an individual action (pick-and-place). In the second scenario, pupillometry is associated with human-to-robot handovers. We present the two publicly available datasets collected in these contexts. Our results show a strong link between pupil dilation and collaborative actions, with both humans and robots. Indeed, an increment in pupil size corresponds to the object handover, which presents the added complexity of mutual coordination. Conversely, individual actions do not elicit the same physiological response at the level of the pupil. In both scenarios, we used objects with different physical properties, cups with varying water levels, but this factor influenced pupil dilation (and presumably, the mental workload) only during the interaction with the robot. With this study, we explore pupillometry as a physiological metric to evaluate cognitive load for collaborative object manipulation during human-human or human-robot collaborations.

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