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A strictly predefined-time convergent and anti-noise fractional-order zeroing neural network for solving time-variant quadratic programming in kinematic robot control

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arXiv 2025年

作者： Yang, Yi Li, Xiao Wang, Xuchen Liu, Mei Yin, Junwei Li, Weibing Voyles, Richard M. Ma, Xin Multi-Scale Medical Robotics Center The Chinese University of Hong Kong 999077 Hong Kong Department of Mechanical and Automation Engineering The Chinese University of Hong Kong 999077 Hong Kong Qingdao266580 China School of Mechanical Engineering Dalian Jiaotong University Dalian116028 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China School of Engineering Technology Purdue University West LafayetteIN47907 United States

This paper proposes a strictly predefined-time convergent and anti-noise fractional-order zeroing neural network (SPTC-AN-FOZNN) model, meticulously designed for addressing time-variant quadratic programming (TVQP) problems. This model marks the first variable-gain ZNN to collectively manifest strictly predefined-time convergence and noise resilience, specifically tailored for kinematic motion control of robots. The SPTC-AN-FOZNN advances traditional ZNNs by incorporating a conformable fractional derivative in accordance with the Leibniz rule, a compliance not commonly achieved by other fractional derivative definitions. It also features a novel activation function designed to ensure favorable convergence independent of the model’s order. When compared to five recently published recurrent neural networks (RNNs), the SPTC-AN-FOZNN, configured with 0 © 2025, CC BY.

关键词： Quadratic programming

Path planning of mobile robot based on improved DDQN 2

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Path planning of mobile robot based on improved DDQN

2nd International Conference on computer Vision and Data Mining, ICVDM 2021

作者： Yang, Yunxiao Wangjun Zhang, Hualiang Dai, Shilong School of Computer Science Shenyang University of Chemical Technology Shenyang110142 China Industrial Control Network and System Laboratory Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institudes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Mobile robots

Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots for Groups with Teenagers

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Ice-Breakers, Turn-Takers and Fun-Makers: Exploring Robots f...

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Sarah Gillet Katie Winkle Giulia Belgiovine Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden DIBRIS Department CONTACT Unit at Istituto Italiano di Tecnologia Università degli Studi di Genova

ISBN: (数字)9781728188591

ISBN: (纸本)9781665406802

Successful, enjoyable group interactions are important in public and personal contexts, especially for teenagers whose peer groups are important for self-identity and self-esteem. Social robots seemingly have the potential to positively shape group interactions, but it seems difficult to effect such impact by designing robot behaviors solely based on related (human interaction) literature. In this article, we take a user-centered approach to explore how teenagers envisage a social robot "group assistant". We engaged 16 teenagers in focus groups, interviews, and robot testing to capture their views and reflections about robots for groups. Over the course of a two-week summer school, participants co-designed the action space for such a robot and experienced working with/wizarding it for 10+ hours. This experience further altered and deepened their insights into using robots as group assistants. We report results regarding teenagers’ views on the applicability and use of a robot group assistant, how these expectations evolved throughout the study, and their repeat interactions with the robot. Our results indicate that each group moves on a spectrum of need for the robot, reflected in use of the robot more (or less) for ice-breaking, turn-taking, and fun-making as the situation demanded.

关键词： Shape Social robots Training data Reflection Behavioral sciences Interviews Robots

An Improvement of Loop Closure Detection Based on BoW for RatSLAM

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An Improvement of Loop Closure Detection Based on BoW for Ra...

Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Jianjun Xu Nanya Yan Fengzhen Tang Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781665465373

The visual perception of the original RatSLAM algorithm works directly on the intensities of image pixels and thus is greatly affected by illuminations, weathers, and other factors. The algorithm may result in perception errors and mapping failures in some situations. Therefore, this paper proposes an image sequence matching algorithm based on a visual dictionary model termed as Bag of Words (BoW) and the dynamic island mechanism to realize fast and accurate scene recognition. The feature vector of scene image is generated by the ORB algorithm, and then the image matching is transformed into the calculation of the distance between image feature vectors, improving the efficiency of image matching. The dynamic island mechanism is used as a detection algorithm to realize the sequence matching of the environment, improving the accuracy of loop closure detection. Experiments are performed in different environments, showing that the visual processing performance and mapping performance of the proposed algorithm are significantly better than the existing RatSLAM algorithm.

关键词： Visualization Dictionaries Heuristic algorithms Image matching Image retrieval Lighting Feature extraction

Towards efficiently diversifying dialogue generation via embedding augmentation

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arXiv 2021年

作者： Cao, Yu Ding, Liang Tian, Zhiliang Fang, Meng School of Computer Science The University of Sydney Australia Tencent Robotics X China Department of CSE The Hong Kong University of Science and Technology Hong Kong

Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore the essential variety within a single sample using hard labels, we propose to promote the generation diversity of the neural dialogue models via soft embedding augmentation along with soft labels in this paper. Particularly, we select some key input tokens and fuse their embeddings together with embeddings from their semantic-neighbor tokens. The new embeddings serve as the input of the model to replace the original one. Besides, soft labels are used in loss calculation, resulting in multi-target supervision for a given input. Our experimental results on two datasets illustrate that our proposed method is capable of generating more diverse responses than raw models while remains a similar n-gram accuracy that ensures the quality of generated responses. © 2021, CC BY.

关键词： Embeddings

Design and Implementation of IoT Enabled Smart Assistive Systems for Healthcare Applications

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Design and Implementation of IoT Enabled Smart Assistive Sys...

Communications and Information Technologies (GCCIT), Global Conference on

作者： Dankan Gowda V Srinivas D Rachakonda Srinivas Balaji Shesharao Ingole Priya Dongare Jadhav KDV Prasad Department of Electronics and Communication Engineering BMS Institute of Technology and Management Bangalore Karnataka India School of Business SR University Warangal Telangana India Department of Computer Science & Engineering Aditya Institute of Technology and Management Srikakulam Andhra Pradesh India Department of Robotics and Automation Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India (Research) Symbiosis Institute of Business Management Hyderabad Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The use of IoT technology in healthcare systems has been adopted in patient monitoring because IoT technology has been adopted rapidly in the systems. This paper discusses on design and implementation of an IoT integrated smart assistive health care system to enhance the precision and speed of patient health assessment. In the proposed system, wearable sensors are used to capture respiratory rate, pulse rate, blood pressure, oxygen saturation, and body temperature with data to be sent to the cloud server as and when it is received. Some of the features are, however, small data transmission delays, high accuracy in the physiological signals and long durations of monitoring due to the low power consumption. The simulations showed that the response time falls below 500ms, and physicians retain up to a 2 % variance to clinical grade devices, which actualized robust and effective patient care. The incorporation of predicting capacities, thus providing ways of early identification of health complications, makes the system applicable in chronic disease treatments and telemedicine.

关键词： Temperature sensors Temperature measurement Patient monitoring Accuracy Power demand Medical services Blood pressure Time factors Biomedical monitoring Wearable sensors

Jet mixing enhancement with Bayesian optimization, deep learning, and persistent data topology

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arXiv 2023年

作者： Li, Yiqing Noack, Bernd R. Wang, Tianyu Cornejo Maceda, Guy Y. Pickering, Ethan Shaqarin, Tamir Tyliszczak, Artur School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Harbin Institute of Technology Shenzhen518055 China Department of Mechanical Engineering Tafila Technical University Tafila66110 Jordan Faculty of Mechanical Engineering and Computer Science Czestochowa University of Technology Czestochowa42-201 Poland

We optimize the jet mixing using large eddy simulations (LES) at a Reynolds number of 3000. Key methodological enablers consist of Bayesian optimization, a surrogate model enhanced by deep learning, and persistent data topology for physical interpretation. The mixing performance is characterized by an equivalent jet radius (Req) derived from the streamwise velocity in a plane located 8 diameters downstream. The optimization is performed in a 22-dimensional actuation space that comprises most known excitations. The plant benefits from a 22-dimensional actuation space that comprises most known excitations. This search space parameterizes distributed actuation imposed on the bulk flow and at the periphery of the nozzle in the streamwise and radial directions. The momentum flux measures the energy input of the actuation. The optimization quadruples the jet radius Req with a 7-armed blooming jet after around 570 evaluations. The control input requires 2% momentum flux of the main flow, which is one order of magnitude lower than an ad hoc dual-mode excitation. Intriguingly, a pronounced suboptimum in the search space is associated with a double-helix jet, a new flow pattern. This jet pattern results in a mixing improvement comparable to the blooming jet. A state-of-the-art Bayesian optimization converges towards this double helix solution. The learning is accelerated and converges to another better optimum by including surrogate model trained along the optimization. Persistent data topology extracts the global and many local minima in the actuation space. These minima can be identified with flow patterns beneficial to the mixing. © 2023, CC BY-NC-ND.

关键词： Flow patterns

Semantics for robotic mapping, perception and interaction: A survey

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arXiv 2021年

作者： Garg, Sourav Sünderhauf, Niko Dayoub, Feras Morrison, Douglas Cosgun, Akansel Carneiro, Gustavo Wu, Qi Chin, Tat-Jun Reid, Ian Gould, Stephen Corke, Peter Milford, Michael QUT Centre for Robotics School of Electrical Engineering and Robotics Queensland University of Technology Brisbane Australia Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia School of Computer Science University of Adelaide Adelaide Australia College of Engineering and Computer Science Australian National University Canberra Australia

Physics-Model-Regulated Deep Reinforcement Learning Towards Safety & Stability Guarantees

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Physics-Model-Regulated Deep Reinforcement Learning Towards ...

IEEE Conference on Decision and Control

作者： Hongpeng Cao Yanbing Mao Lui Sha Marco Caccamo Hongpeng Cao is with School of Engineering and Design Technical University of Munich Munich Germany Yanbing Mao is with Engineering Technology Division Wayne State University Detroit MI USA Department of Computer Science University of Illinois at Urbana-Champaign Urbana IL USA School of Engineering and Design and Institute of Robotics and Machine Intelligence Technical University of Munich Munich Germany

Deep reinforcement learning (DRL) has demonstrated impressive success in solving complex control tasks by synthesizing control policies from data. However, the safety and stability of applying DRL to safety-critical systems remain a primary concern and challenging problem. To address the problem, we propose the Phy-DRL: a novel physics-model-regulated deep reinforcement learning framework. The Phy-DRL is novel in two architectural designs: a physics-model-regulated reward and residual control, which integrates physics-model-based control and data-driven control. The concurrent designs enable the Phy-DRL the mathematically provable safety and stability guarantees. Finally, the effectiveness of the Phy-DRL is validated by an inverted pendulum system. Additionally, the experimental results demonstrate that the Phy-DRL features remarkably accelerated training and enlarged reward.

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