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2024 China Automation Congress, CAC 2024

作者： Jiang, Sen Wang, Bo School of HDU-ITMO Research Center for Intelligent Systems and Robotics Hangzhou Dianzi University Hangzhou310018 China School of Automation International Joint Research Laboratory for Autonomous Robotic Systems Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9798350368604

For the problem of jamming penetration under the one-to-many scenario with jammer, a real-time radar interference resource allocation method based on the Multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed. This method describes the interference resource allocation model as a Markov Decision Process (MDP), and designed the state space, action space and reward function for interference resource allocation problem. The jammer is divided into three heterogeneous agents, each responsible for selecting jamming targets, jamming patterns, and jamming power, respectively. These three agents coordinate to enhance the convergence ability of the high-dimensional action space algorithm. Simulation results indicate that the jamming resource allocation method designed in this paper provides better jamming effects on radar positions, higher stability, and effectively improves the operational performance of the jammer. © 2024 IEEE.

关键词： Radar interference

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Saliency-SLAM: Saliency Prediction for Visual SLAM

Saliency-SLAM: Saliency Prediction for Visual SLAM

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2023 Tianjin University-IET Electrical and Information Engineering Doctoral Forum, TJU IET 2023

作者： Jin, Sheng Meng, Qinghao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin China

ISBN: (纸本)9781839539824

This paper proposes a novel approach for visual simultaneous localization and mapping (vSLAM), called Saliency-SLAM. This method effectively emulates the visual attention mechanism by integrating a saliency prediction model. In contrast to conventional vSLAM methods, which treat all feature points as equally significant, salient feature points are given greater importance in Saliency-SLAM. Therefore, we construct a novel saliency dataset specifically for vSLAM task to train a saliency prediction model by integrating multi-level visual information. Then, we propose to use the salient value of predicted result as the weight of significance for the feature points during the optimization process. Exhaustive experiments have been conducted on the KITTI dataset, demonstrating that our proposed method outperforms existing approaches in terms of localization accuracy. © The Institution of Engineering & Technology 2023.

关键词： Forecasting

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Active Camera View Planning Based on Deep Reinforcement Learning in Indoor Exploration 42

Active Camera View Planning Based on Deep Reinforcement Lear...

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42nd Chinese Control Conference, CCC 2023

作者： Wang, Hao-Yu Meng, Qing-Hao Dai, Xu-Yang Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Tianjin300072 China

ISBN: (纸本)9789887581543

In the last decades, significant progress has been made in the field of active vSLAM (visual simultaneous localization and mapping). However, the majority of active vSLAM methods suffer from the problem of coupling the camera's viewpoint to the robot's motion, which limits the initiative during the exploration of unknown environments. To address this issue, this study proposes a camera view planning method based on deep reinforcement learning (DRL), which makes the camera rotate independently. First, feature encoding is performed on the reconstructed navigation map using a multi-layer convolutional neural network (CNN). Second, the DRL-based camera view planning method is trained through the proximal policy optimization (PPO) algorithm. Last, a significant number of tests is performed on a public dataset of indoor environments. The simulation results show that the proposed camera view planning method improves the exploration coverage by an average of 4% (33.86 m2 for traditional vs. 35.21 m2 for the suggested method). Meanwhile, the kind of semantic information newly observed in each step increases by 21% (2.9 for traditional vs. 3.5 for the proposed method) on average, indicating the improvement in the efficiency of information acquisition. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Reinforcement learning

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Diaphragmatic Breathing Guidance Using a Humanoid Robot with a Soft Robotic Haptic Interface for Anxiety Reduction 16th

Diaphragmatic Breathing Guidance Using a Humanoid Robot wit...

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16th International Conference on Social robotics, ICSR + AI 2024

作者： Saood, Adnan Liu, Yang Tapus, Adriana Autonomous Systems and Robotics Lab/U2IS ENSTA Paris Institute Polytechnique de Paris Palaiseau France Information Processing and Communications Laboratory/LTCI Telecom Paris Institute Polytechnique de Paris Palaiseau France

ISBN: (纸本)9789819635245

Anxiety disorders profoundly affect individuals’ well-being and daily lives, manifesting in both physical and emotional symptoms. Although pharmacological treatments and cognitive-behavioral therapies are commonly used, diaphragmatic breathing exercises have shown promise as a non-pharmacological intervention. This work presents an innovative device: a silicone haptic interface mounted on the chest of a humanoid robot, designed to simulate human heartbeat and breathing. This device, employing a baromorphic system design, offers a good representation of human respiration. Our study assesses the effectiveness of this haptic interface in guiding diaphragmatic breathing exercises to reduce anxiety levels. Eighteen participants underwent a standard anxiety-inducing procedure followed by a guided diaphragmatic breathing exercise with the robot. They were divided into two groups: ten with the haptic interface and eight without it. Both groups experienced similar reductions in anxiety levels. Those using the haptic interface reported the robot as more natural and supportive in post-experiment interviews. This exploratory study illustrates the potential of social robots with haptic interfaces to enhance therapeutic practices for anxiety reduction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Anthropomorphic robots

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Lessons learned:Symbiotic autonomous robot ecosystem for nuclear environments

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IET Cyber-systems and robotics 2023年第4期5卷 63-86页

作者： Daniel Mitchell Paul Dominick Emor Baniqued Abdul Zahid Andrew West Bahman Nouri Rahmat Abadi Barry Lennox Bin Liu Burak Kizilkaya David Flynn David John Francis Erwin Jose Lopez Pulgarin Guodong Zhao Hasan Kivrak Jamie Rowland Douglas Blanche Jennifer David Jingyan Wang Joseph Bolarinwa Kanzhong Yao Keir Groves Liyuan Qi Mahmoud A.Shawky Manuel Giuliani Melissa Sandison Olaoluwa Popoola Ognjen Marjanovic Paul Bremner Samuel Thomas Harper Shivoh Nandakumar Simon Watson Subham Agrawal Theodore Lim Thomas Johnson Wasim Ahmad Xiangmin Xu Zhen Meng Zhengyi Jiang James Watt School of Engineering Autonomous Systems and Connectivity DivisionUniversity of GlasgowGlasgowUK Department of Electrical and Electronic Engineering The University of ManchesterManchesterUK Bristol Robotics Laboratory University of the West of EnglandBristolUK Heriot-Watt University EdinburghUK

Nuclear facilities have a regulatory requirement to measure radiation levels within Post Operational Clean Out(POCO)around nuclear facilities each year,resulting in a trend towards robotic deployments to gain an improved understanding during nuclear decommissioning *** UK Nuclear Decommissioning Authority supports the view that human-in-the-loop(HITL)robotic deployments are a solution to improve procedures and reduce risks within radiation characterisation of nuclear *** authors present a novel implementation of a Cyber-Physical System(CPS)deployed in an analogue nuclear environment,comprised of a multi-robot(MR)team coordinated by a HITL operator through a digital twin *** development of the CPS created efficient partnerships across systems including robots,digital systems and *** was presented as a multi-staged mission within an inspection scenario for the hetero-geneous Symbiotic Multi-Robot Fleet(SMuRF).Symbiotic interactions were achieved across the SMuRF where robots utilised automated collaborative governance to work together,where a single robot would face challenges in full characterisation of *** contributions include the demonstration of symbiotic autonomy and query-based learning of an autonomous mission supporting scalable autonomy and autonomy as a *** coordination of the CPS was a success and displayed further challenges and improvements related to future MR fleets.

关键词： cyber-systems field robotics hazardous inspection human robot interaction industrial robotics mobile robots multi-agent systems multi-robot systems robotics service robots

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Multi-Object Graph Affordance Network: Goal-Oriented Planning through Learned Compound Object Affordances

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IEEE Transactions on Cognitive and Developmental systems 2024年

作者： Girgin, Tuba Uǧur, Emre Bogazici University Department of Computer Engineering Istanbul Turkey TUBITAK BILGEM Robotics and Autonomous Systems Laboratory Kocaeli41470 Turkey

Learning object affordances is an effective tool in the field of robot learning. While the data-driven models investigate affordances of single or paired objects, there is a gap in the exploration of affordances of compound objects composed of an arbitrary number of objects. We propose the Multi-Object Graph Affordance Network which models complex compound object affordances by learning the outcomes of robot actions that facilitate interactions between an object and a compound. Given the depth images of the objects, the object features are extracted via convolution operations and encoded in the nodes of graph neural networks. Graph convolution operations are used to encode the state of the compounds, which are used as input to decoders to predict the outcome of the object-compound interactions. After learning the compound object affordances, given different tasks, the learned outcome predictors are used to plan sequences of stack actions that involve stacking objects on top of each other, inserting smaller objects into larger containers and passing through ring-like objects through poles. We showed that our system successfully modeled the affordances of compound objects that include concave and convex objects, in both simulated and real-world environments. We benchmarked our system with a baseline model to highlight its advantages. © 2016 IEEE.

关键词： Adversarial machine learning

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Numerical study on the flow field for deep-sea hydrothermal plume tracking simulation 17

Numerical study on the flow field for deep-sea hydrothermal ...

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17th Asian Congress of Fluid Mechanics, ACFM 2023

作者： Xu, Xue-Han Liu, Jing-Cheng Hou, Hui-Rang Meng, Qing-Hao Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin300072 China Laboratory for High-speed Aerodynamics Department of Mechanics Tianjin University Tianjin300072 China

In this paper, the distribution of hydrothermal buoyancy plume in the deep-sea environment is studied numerically. Submarine hydrothermal vents are prominent manifestations of hydrothermal activities and are of great scientific and engineering importance. It is an important basis to obtain the temporal and spatial distribution information of different hydrothermal plumes for tracking and locating the source of hydrothermal plumes. Based on Reynolds-averaged Navier-Stokes equations and Realizable k-ϵ turbulence model, the plumes of hydrothermal vents at depths of 800 m, 1200 m, and 1400 m were calculated and studied to obtain the spatial distribution of variables such as velocity field, temperature field and turbulent kinetic energy. The research results of this paper provide an important basis for the simulation of deep-sea hydrothermal plume tracking based on autonomous underwater vehicles. © The Institution of Engineering & Technology 2023.

关键词： Flow fields

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GISR: Geometric Initialization and Silhouette-based Refinement for Single-View Robot Pose and Configuration Estimation

arXiv

引用

arXiv 2024年

作者： Bilić, Ivan Marić, Filip Bonsignorio, Fabio Petrović, Ivan University of Zagreb Faculty of Electrical Engineering and Computing Laboratory for Autonomous Systems and Mobile Robotics Croatia University of Toronto Institute for Aerospace Studies Space and Terrestrial Autonomous Robotic Systems Laboratory Canada

In autonomous robotics, measurement of the robot’s internal state and perception of its environment, including interaction with other agents such as collaborative robots, are essential. Estimating the pose of the robot arm from a single view has the potential to replace classical eye-to-hand calibration approaches and is particularly attractive for online estimation and dynamic environments. In addition to its pose, recovering the robot configuration provides a complete spatial understanding of the observed robot that can be used to anticipate the actions of other agents in advanced robotics use cases. Furthermore, this additional redundancy enables the planning and execution of recovery protocols in case of sensor failures or external disturbances. We introduce GISR - a deep configuration and robot-to-camera pose estimation method that prioritizes execution in real-time. GISR consists of two modules: (i) a geometric initialization module that efficiently computes an approximate robot pose and configuration, and (ii) a deep iterative silhouette-based refinement module that arrives at a final solution in just a few iterations. We evaluate GISR on publicly available data and show that it outperforms existing methods of the same class in terms of both speed and accuracy, and can compete with approaches that rely on ground-truth proprioception and recover only the pose. Our code is available at https://***/iwhitey/GISR-robot. © 2024, CC BY.

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Design and Control of a Micro Overactuated Aerial Robot with an Origami Delta Manipulator

Design and Control of a Micro Overactuated Aerial Robot with...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Eugenio Cuniato Christian Geckeler Maximilian Brunner Dario Strübin Elia Bähler Fabian Ospelt Marco Tognon Stefano Mintchev Roland Siegwart ETH Zurich Autonomous Systems Laboratory Switzerland ETH Zurich Environmental Robotics Laboratory Switzerland

This work presents the mechanical design and control of a novel small-size and lightweight Micro Aerial Vehicle (MAV) for aerial manipulation. To our knowledge, with a total take-off mass of only 2.0 kg, the proposed system is the most lightweight Aerial Manipulator (AM) that has 8-DOF independently controllable: 5 for the aerial platform and 3 for the articulated arm. We designed the robot to be fully-actuated in the body forward direction. This allows independent pitching and instantaneous force generation, improving the platform's performance during physical interaction. The robotic arm is an origami delta manipulator driven by three servomotors, enabling active motion compensation at the end-effector. Its composite multimaterial links help reduce the weight, while their flexibility allow for compliant aerial interaction with the environment. In particular, the arm's stiffness can be changed according to its configuration. We provide an in depth discussion of the system design and characterize the stiffness of the delta arm. A control architecture to deal with the platform's overactuation while exploiting the delta arm is presented. Its capabilities are experimentally illustrated both in free flight and physical interaction, highlighting advantages and disadvantages of the origami's folding mechanism.

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Design and Control of a Micro Overactuated Aerial Robot with an Origami Delta Manipulator

arXiv

引用

arXiv 2023年

作者： Cuniato, Eugenio Geckeler, Christian Brunner, Maximilian Strübin, Dario Bähler, Elia Ospelt, Fabian Tognon, Marco Mintchev, Stefano Siegwart, Roland Autonomous Systems Laboratory ETH Zurich Switzerland Environmental Robotics Laboratory ETH Zurich Switzerland

关键词： Antennas

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