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arXiv 2023年

作者： Harlow, Kyle Jang, Hyesu Barfoot, Timothy D. Kim, Ayoung Heckman, Christoffer Autonomous Robotics and Perception Group the University of Colorado Boulder BoulderCO United States Robust Perception and Mobile Robotics Lab Seoul National University Seoul Korea Republic of Autonomous Space Robotics Laboratory the University of Toronto Toronto Canada

We survey the current state of millimeter-wave (mmWave) radar applications in robotics with a focus on unique capabilities, and discuss future opportunities based on the state of the art. Frequency Modulated Continuous Wave (FMCW) mmWave radars operating in the 76–81GHz range are an appealing alternative to lidars, cameras and other sensors operating in the near visual spectrum. Radar has been made more widely available in new packaging classes, more convenient for robotics and its longer wavelengths have the ability to bypass visual clutter such as fog, dust, and smoke. We begin by covering radar principles as they relate to robotics. We then review the relevant new research across a broad spectrum of robotics applications beginning with motion estimation, localization, and mapping. We then cover object detection and classification, and then close with an analysis of current datasets and calibration techniques that provide entry points into radar research. Copyright © 2023, The Authors. All rights reserved.

关键词： Motion estimation

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Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism for Multi-view Data Action Recognition 10

Spatial-Temporal Hypergraph Neural Network based on Attentio...

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10th International Conference on Dependable Systems and Their Applications, DSA 2023

作者： Wu, Zhixuan Ma, Nan Zhi, Tao Xu, Genbao Beijing Union University Beijing Key Laboratory of Information Service Engineering College of Robotics Beijing100101 China Beijing University of Technology Faculty of Information Technology Beijing100124 China Beijing University of Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing100124 China Yunji Technology Beijing100081 China

ISBN: (纸本)9798350304770

To address the issue of large network computing parameters for spatial-temporal features of actions in multi-view video sequences, this paper proposes a Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism (STHGNN-AM). This method consists of a Temporal Attention Mechanism based on Trainable Threshold (TAM-TT) and a Multi-scale Spatial-Temporal Residual Module (MS-STRM), achieving multi-view data action recognition. Specifically, TAM-TT is constructed by using a learnable threshold to extract key frames of actions from different view video frames input to the module. MS-STRM is employed to further improve the model performance, and high-order semantic features of actions are learned in a hypergraph neural network. The MS-STRM extracts features using a multi-scale approach, modeling long-term and short-term semantic information to capture the temporal information changes between different frames. Comparative experiments on the NTU RGB+D and imitating traffic police gestures datasets evince the superior performance and heightened recognition accuracy exhibited by the proposed methodology, effectively enhancing the cognitive ability of machine-human body language interaction. © 2023 IEEE.

关键词： Semantics

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SocNavGym: A Reinforcement Learning Gym for Social Navigation

SocNavGym: A Reinforcement Learning Gym for Social Navigatio...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Aditya Kapoor Sushant Swamy Pilar Bachiller Luis J. Manso Tata Consultancy Services Research & Innovation India Birla Institute of Technology and Science Goa India Computer and Communication Technology Department Universidad de Extremadura Spain Computer Science Department Autonomous Robotics and Perception Laboratory Aston University UK

It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of code complexity or the number of variables that are handled. Unfortunately, the lack of safety guarantees and the large data requirements by DRL algorithms make learning in the real world unfeasible. To bridge this gap, simulation environments are frequently used. We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents. SocNavGym is lightweight, fast, easy to use, and can be effortlessly configured to generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals and to yield a variety of evaluation metrics to benchmark agents’ performance. Further, we also provide a case study where a Dueling-DQN agent is trained to learn social-navigation policies using SocNavGym. The results provide evidence that SocNavGym can be used to train an agent from scratch to navigate in simple as well as complex social scenarios. Our experiments also show that the agents trained using the data-driven reward function display more advanced social compliance in comparison to the heuristic-based reward function.

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Towards Open-Source and Modular Space Systems with ATMOS

arXiv

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arXiv 2025年

作者： Roque, Pedro Phodapol, Sujet Krantz, Elias Lim, Jaeyoung Verhagen, Joris Jiang, Frank J. Dörner, David Mao, Huina Tibert, Gunnar Siegwart, Roland Stenius, Ivan Tumova, Jana Fuglesang, Christer Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Engineering Sciences KTH Royal Institute of Technology Stockholm Sweden Autonomous Systems Laboratory ETH Zürich Zürich Switzerland Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

In the near future, autonomous space systems will compose many of the deployed spacecraft. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspections, assembly, and maintenance of orbiting space stations, as well as human-assistance tasks over shared workspaces. To promote replicable and reliable scientific results for autonomous control of spacecraft, we present the design of a space robotics laboratory based on open-source and modular software and hardware. The simulation software provides a software-in-the-loop architecture that seamlessly transfers simulated results to the hardware. Our results provide an insight into such a system, including comparisons of hardware and software results, as well as control and planning methodologies for controlling free-flying platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Space stations

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SocNavGym: A Reinforcement Learning Gym for Social Navigation

arXiv

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arXiv 2023年

作者： Kapoor, Aditya Swamy, Sushant Bachiller, Pilar Manso, Luis J. Tata Consultancy Services Research & Innovation India Birla Institute of Technology and Science Goa India Computer and Communication Technology Department Universidad de Extremadura Spain Autonomous Robotics and Perception Laboratory Computer Science Department Aston University United Kingdom

It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of code complexity or the number of variables that are handled. Unfortunately, the lack of safety guarantees and the large data requirements by DRL algorithms make learning in the real world unfeasible. To bridge this gap, simulation environments are frequently used. We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents. SocNavGym is lightweight, fast, easy to use, and can be effortlessly configured to generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals and to yield a variety of evaluation metrics to benchmark agents' performance. Further, we also provide a case study where a Dueling-DQN agent is trained to learn social-navigation policies using SocNavGym. The results provide evidence that SocNavGym can be used to train an agent from scratch to navigate in simple as well as complex social scenarios. Our experiments also show that the agents trained using the data-driven reward function display more advanced social compliance in comparison to the heuristic-based reward function. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Spatial-Temporal Hypergraph Neural Network based on Attention Mechanism for Multi-view Data Action Recognition

Spatial-Temporal Hypergraph Neural Network based on Attentio...

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International Conference on Dependable Systems and Their Applications (DSA)

作者： Zhixuan Wu Nan Ma Tao Zhi Genbao Xu Beijing Key Laboratory of Information Service Engineering College of Robotics Beijing Union University Beijing China Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing University of Technology Beijing China Yunji Technology Beijing China

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Stiffness Estimation and Intention Detection for Human-Robot Collaboration

Stiffness Estimation and Intention Detection for Human-Robot...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiongjun Chen Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In this paper, we propose a stiffness estimation and intention detection method for human-robot collaboration. The human arm endpoint stiffness can be obtained according to the muscle activation levels of the upper arm and the human arm configurations. The estimated endpoint stiffness of human arm is matching to the robot arm joint stiffness through an appropriate mapping. The motion intention of human arm is detected based on the wrist configuration which is recognized by a Myo armband attached at the forearm of the operator. In order to reduce the time of feature engineering to ensure the performance of real-time collaboration, the wrist configuration recognition is realised based on the neural learning algorithm. The sEMG of the human forearm is directly fed into the neural network after processing by filters and sliding windows. The force sensor at the end of the robot arm is embedded in the feedback loop to make the robot arm better adapted to the operator's movement. The results of experiments performed on Baxter robot platform illustrate a good performance and verifies the proposed method.

关键词： Wrist Neural networks Collaboration Estimation Robot sensing systems Manipulators Robots

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Online Parameter Estimation For Uncertain Robot Manipulators With Fixed-time Convergence

Online Parameter Estimation For Uncertain Robot Manipulators...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Chengzhi Zhu Yiming Jiang Chenguang Yang Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China National Engineering Laboratory for Robot Visual Perception and Control Hunan University Changsha China Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

For traditional parameter estimation schemes of uncertain robot, most of them were proposed to identify unknown parameter with desired precision, but few of them focused on the convergence time. Recently finite-time estimation techniques have been proposed by scholars to achieve estimation in finite time. In this paper, we proposed a novel estimation scheme for uncertain robot systems with fixed time instead of finite time. In order to avoid using acceleration signals during the estimation, a kind of auxiliary filtering technique was employed. Besides, a continuous and recursive update law was employed for the parameter estimation such that the computational burdens of real-time inversion of square matrices could be avoided. Finally the effectiveness of the identification algorithm is verified based on a 2-DOF uncertain robot model.

关键词： Parameter estimation Filtering Heuristic algorithms Estimation Real-time systems Manipulator dynamics Convergence

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Flexible disaster response of tomorrow final presentation and evaluation of the CENTAURO system

arXiv

引用

arXiv 2019年

作者： Klamt, Tobias Rodriguez, Diego Baccelliere, Lorenzo Chen, Xi Chiaradia, Domenico Cichon, Torben Gabardi, Maßimiliano Guria, Paolo Holmquist, Karl Kamedula, Malgorzata Karaoguz, Hakan Kashiri, Navvab Laurenzi, Arturo Lenz, Christian Leonardis, Daniele Hoffman, Enrico Mingo Muratore, Luca Pavlichenko, Dmytro Porcini, Francesco Ren, Zeyu Schilling, Fabian Schwarz, Max Solazzi, Maßimiliano Felsberg, Michael Frisoli, Antonio Gustmann, Michael Jensfelt, Patric Nordberg, Klas Roßmann, Jürgen Süß, Uwe Tsagarakis, Nikos G. Behnke, Sven Autonomous Intelligent Systems University of Bonn Germany Humanoids and Human-Centred Mechatronics Istituto Italiano di Tecnologia Genoa Italy Department of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden PERCRO Laboratory TeCIP Institute Sant'Anna School of Advanced Studies Pisa Italy Institute for Man-Machine Interaction RWTH Aachen University Germany Computer Vision Laboratory Linköping University Sweden Lausanne Switzerland Kerntechnische Hilfsdienst GmbH Karlsruhe Germany School of Electrical and Electronic Engineering University of Manchester United Kingdom

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RFID-based hybrid metric-topological SLAM for GPS-denied environments

RFID-based hybrid metric-topological SLAM for GPS-denied env...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christian Forster Deon Sabatta Roland Siegwart Davide Scaramuzza Artificial Intelligence Lab-Robotics and Perception Group University of Zurich Switzerland Eidgenossische Technische Hochschule Zurich Zurich ZH CH Autonomous Systems Laboratory ETH Zurich Switzerland

ISBN: (纸本)9781467356404

In this work, we propose a novel RFID-based hybrid metric-topological Simultaneous Localization and Mapping (SLAM) algorithm which enables autonomous navigation in GPS-denied environments. A method based on the normalized-cut is proposed for online clustering of strongly connected Radio Frequency Identification (RFID) tags to form topological nodes. A particle filter together with a sensor model which characterizes the received signal strength (RSS) as well as the tag detection probability is used to create metric submaps for each topological node. The hybrid framework is highly scalable, simplifies path planning and promises precision and robustness. The algorithm requires only odometry and RFID measurements to localize the RFID tags with a relative accuracy of approximately 0.3 meters. The ideas presented here are supported by experimental results.

关键词： Measurement RFID tags Simultaneous localization and mapping Partitioning algorithms

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