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Disturbance Rejection Control for Autonomous Trolley Collection Robots with Prescribed Performance

Procedia Computer science 2024年 250卷 15-21页

作者： Rui-Dong Xi Tie-Nan Ma Bingyi Xia Xue Zhang Yixuan Yuan Jiankun Wang Max Q.-H. Meng Shenzhen Key Laboratory of Robotics Perception and Intelligence the Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen 518055 China Department of Electromechanical Engineering Faculty of Science and Technology University of Macau Taipa Macau SAR 999078 China Department of Electronic Engineering Chinese University of Hong Kong Jiaxing Research Institute Southern University of Science and Technology Jiaxing 314000 China

Trajectory tracking control of autonomous trolley collection robots (ATCR) is a challenging task due to the complex environment, significant noise, and external disturbances. To address these challenges, this work investigates a control scheme for ATCRs subject to severe environmental interference. A fast-convergent, kinematics model-based adaptive sliding mode disturbance observer (ASMDOB) is first proposed to estimate lumped disturbances. Building upon this, a robust controller with prescribed performance is designed using the backstepping technique, improving transient performance and guaranteeing fast convergence. Simulation results have been given to illustrate the effectiveness of the proposed control scheme.

关键词： Mobile robot Disturbance rejection control Prescribed performance control

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State Estimation of Continuum Robots: A Nonlinear Constrained Moving Horizon Approach

arXiv

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arXiv 2023年

作者： Abdelaziz, Hend Nada, Ayman Ishii, Hiroyuki El-Hussieny, Haitham Department of Mechatronics and Robotics Engineering Egypt-Japan University of Science and Technology Alexandria Egypt Faculty of Science and Engineering Waseda University Tokyo169-8050 Japan

Continuum robots, made from flexible materials with continuous backbones, have several advantages over traditional rigid robots. Some of them are the ability to navigate through narrow or confined spaces, adapt to irregular or changing environments, and perform tasks in proximity to humans. However, one of the challenges in using continuum robots is the difficulty in accurately estimating their state, such as their tip position and curvature. This is due to the complexity of their kinematics and the inherent uncertainty in their measurement and control. This paper proposes a moving horizon estimation (MHE) approach for estimating the robot's state, including its tip position and shape parameters. Our approach involves minimizing the error between measurement samples from an IMU attached to the robot's tip and the estimated state along the estimation horizon using an inline optimization problem. We demonstrate the effectiveness of our approach through simulation and experimental results. Our approach can potentially improve the accuracy and robustness of state estimation and control for continuum robots. It can be applied to various applications such as surgery, manufacturing, and inspection. © 2023, CC BY.

关键词： State estimation

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Enhancing Mobile Robot Navigation: A Graph Decomposition Submodule for TEA* 25

Enhancing Mobile Robot Navigation: A Graph Decomposition Sub...

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25th IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2025

作者： Cardoso, Filipe Matos, Diogo Miguel Brilhante, Marina Costat, Pedro Sobreira, Heber Silva, Carlos INESC TEC - Institute for Systems and Computer Engineering Technology and Science CRIIS - Centre of Robotics in Industry and Intelligent Systems Porto 4200-465 Portugal University of Porto Faculty of Engineering Porto 4200-465 Portugal Flowbotic Mobile Systems SA Mangualde 3530-259 Portugal

ISBN: (纸本)9798331538606

Rising industrial complexity demands efficient mobile robots to drive automation and productivity. Effective navigation relies on perception, localization, mapping, path planning, and motion control, with path planning being key. The Time Enhanced A ∗ (TEA *) algorithm extends A ∗ by adding time as a dimension to resolve temporal conflicts in multi-robot coordination. However, inconsistencies in edge lengths within the graph can hinder optimal path calculation. To address this, a Graph Decomposition submodule was developed to standardize edge lengths and temporal costs. Integrated into a ROS-based fleet coordination system, this approach significantly reduces execution time and improves coordination capacity. © 2025 IEEE.

关键词： graph decomposition graph-based path planning multi-robot coordination spline-based trajectory

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Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data

arXiv

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arXiv 2024年

作者： Röder, Manuel Schleif, Frank-Michael Faculty of Computer Science and Business Information Systems TUAS Würzburg-Schweinfurt Würzburg Germany Faculty of Technology Bielefeld University Bielefeld Germany Center for Artificial Intelligence and Robotics Würzburg Germany

This extended abstract explores the integration of federated learning with deep transfer hashing for distributed prediction tasks, emphasizing resource-efficient client training from evolving data streams. Federated learning allows multiple clients to collaboratively train a shared model while maintaining data privacy - by incorporating deep transfer hashing, high-dimensional data can be converted into compact hash codes, reducing data transmission size and network loads. The proposed framework utilizes transfer learning, pre-training deep neural networks on a central server, and fine-tuning on clients to enhance model accuracy and adaptability. A selective hash code sharing mechanism using a privacy-preserving global memory bank further supports client fine-tuning. This approach addresses challenges in previous research by improving computational efficiency and scalability. Practical applications include Car2X event predictions, where a shared model is collectively trained to recognize traffic patterns, aiding in tasks such as traffic density assessment and accident detection. The research aims to develop a robust framework that combines federated learning, deep transfer hashing and transfer learning for efficient and secure downstream task execution. © 2024, CC BY.

关键词： Federated learning

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Developing a Framework for Natural Human Movement Mimicry of Low-Dynamic Motions in Mobile-Based Humanoids

Developing a Framework for Natural Human Movement Mimicry of...

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IEEE/SICE International Symposium on System Integration

作者： Simon Gormuzov Yushi Wang Pinchu Yang Tamon Miyake Tetsuya Ogata Shigeki Sugano Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Future Robotics Organization Waseda University Tokyo Japan Faculty of Science and Engineering Waseda University Tokyo Japan The National Institute of Advanced Science and Technology Tokyo Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

In this work, we propose a framework that facilitates natural whole-body movements for a mobile-based humanoid. The framework takes bipedal human motion animation as input. After re-targeting the motion to the robot rig, the joint space data is applied to a physics-enabled robot model for balance confirmation and then deployed one-shot to the real robot. This method is beneficial for: 1) mapping expressive and low-dynamic whole-body motions, such as walking, to mobile-based robots; and 2) serving as a basis for training more complex control policies for more dynamic motions. Experiments were conducted by deploying human walking animations to the robot, assessing its ability to mirror the movements, and evaluating the subjective feelings of humans observing the robot performing the motions generated by both the proposed method and the traditional method. The results indicated that the proposed method is effective for mimicking human movements and consistently delivered a better overall impression in the natural appearance of the motion, the human-like factor, and friendliness.

关键词： Legged locomotion Training Dynamics Humanoid robots System integration Aerospace electronics Animation Data models Mirrors

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Robot Trust and Self-Confidence Based Role Arbitration Method for Physical Human-Robot Collaboration

Robot Trust and Self-Confidence Based Role Arbitration Metho...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Qiao Wang Dikai Liu Marc G. Carmichael Chin-Teng Lin Faculty of Engineering and Information Technology Robotics Institute University of Technology Sydney Ultimo NSW Australia Faculty of Engineering and Information Technology Australian Artificial Intelligence Institute School of Computer Science University of Technology Sydney Ultimo NSW Australia

Role arbitration in human-robot collaboration (HRC) is a dynamically changing process that is affected by many factors such as physical workload, environmental changes and trust. In order to address this dynamic process, a trust-based role arbitration method is studied in this research. A computational model of robot trust and self-confidence (TSC) in physical human-robot collaboration (pHRC) is proposed. The TSC model is defined as a function of objective robot and human co-worker performance. A role arbitration method is then proposed based on the TSC model presented. The human-in-the-loop experiments with a collaborative robot are conducted to verify the TSC-based role arbitration method. The results show that the proposed method could achieve superior human-robot combined performance, reduce human co-workers' workload, and improve subjective preference.

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Trajectory Optimization Algorithm of Trajectory Rehabilitation Training Mode for Rehabilitation Robot

Trajectory Optimization Algorithm of Trajectory Rehabilitati...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Cen, Yuhui Yuan, Jianjun Ma, Shugen Luo, Jingjing Wang, Hongbo Human Phenome Institute Fudan University Shanghai200438 China The Shanghai Robotics Institute Shanghai University 99 Shangda RD Baoshan District Shanghai China The Department of Robotics Faculty of Science and Engineering Ritsumeikan University 1-1-1 Nojihigashi Shiga Kusatsu Japan Institute of AI and Robotics Academy for Engineering and Technology Fudan University Engineering Research Center of AI & Robotics Ministry of Education Shanghai210043 China

ISBN: (纸本)9781665481090

It is a challenge for robot-assisted rehabilitation therapy to develop a training program with both customized and optimized characteristics. To optimize the scheme of the trajectory rehabilitation training mode, we design a trajectory optimization algorithm with an intelligent decision mechanism. A trajectory smoothing improvement algorithm is proposed to make the trajectory more suitable for rehabilitation training. In addition, we propose an improved genetic algorithm that reduces the optimization time and obtains a better solution. An adaptive variable time weight improved trapezoidal velocity algorithm is innovatively designed by fusing the above two algorithms with an optimal rehabilitation training program as a guide. Based on the experimental platform of the upper limb rehabilitation robot, it is demonstrated that the algorithm can provide comfortable and high-quality rehabilitation training for patients by adaptively optimizing the parameters of the trajectory model according to the training constraints. By combining an intelligent optimization algorithm with a rehabilitation robot trajectory planning algorithm, this paper realizes the unification of customization and optimization of a trajectory rehabilitation training program. © 2022 IEEE.

关键词： Trajectories

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Adaptive position tracking control of the lower limb exoskeleton robot with an uncertain dynamic model 18

Adaptive position tracking control of the lower limb exoskel...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Yuan, Ting Zhang, Chi Yi, Feng Lv, Pingping Li, Shupei Zhang, Meitong Ningbo University Ningbo Instituteof Materials Technology and Engineering Chinese Academy of Sciences Faculty of Electrical Engineering and Computer Science Ningbo315201 China Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology and Engineering Ningbo315201 China University of Chinese Academy of Sciences Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences College of Materials Science and Opto-Electronic Technology Ningbo315201 China Hebei University of Science and Technology Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences School of Electrical Engineering Ningbo315201 China

ISBN: (纸本)9798350312201

In this paper, an adaptive position control method combining proportional-derivative (PD) control, RBF neural network-based sliding mode (SM) control, and feedforward (FF) control, i.e., PD-FF-SM control, is proposed to deal with the dynamic uncertainties, disturbances, and slow response in lower limb exoskeleton robot system. The Lagrange method is utilized to establish dynamic models that ignore the uncertainties and disturbances in the swing phase and supporting phase. Then, the PD feedback controller is designed with a sliding surface function, to keep the system stable. The FF controller is adopted to improve the response by using dynamic models. And the RBF neural network-based SM controller is designed to compensate for the uncertainties and disturbances. The system's stability is verified by Lyapunov's theorem. The simulation results show that the proposed control method can effectively reduce the error of position tracking and response time with inaccurate dynamic modeling. © 2023 IEEE.

关键词： Sliding mode control

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Forecasting the Temperature of BEV Battery Pack Based on Field Testing Data 1

引用

3rd EAI International Conference on Edge Computing and IoT, EAI ICECI 2022

作者： Chou, Ka Seng Wong, Kei Long Aguiari, Davide Tse, Rita Tang, Su-Kit Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Department of Computer Science and Engineering Alma Mater Studiorum University of Bologna Bologna Italy Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates UCLA Samueli Computer Science University of California Los Angeles Los Angeles United States

ISBN: (数字)9783031289903

ISBN: (纸本)9783031289897

Monitoring electric vehicles’ battery situation and indicating the state of health is still challenging. Temperature is one of the critical factors determining battery degradation over time. We have collected more than 2.3 million discharging samples via a custom Internet of Thing device for more than one year to build a machine-learning model that can forecast the battery pack’s average temperature in real-world driving. Our best Bi-LSTM model achieved the mean absolute error of 2.92 ∘ C on test data and 1.7 ∘ C on cross-validation for prediction of 10 min on the battery pack’s temperature. © 2023, ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Lithium-ion batteries

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Analysis of fast prototyping of microcontroller-based ML software for acoustic signal classification

Analysis of fast prototyping of microcontroller-based ML sof...

引用

Signal Processing Algorithms, Architectures, Arrangements and Applications (SPA)

作者： Filip Marciniak Wojciech Marciniak Tomasz Marciniak Faculty of Computing and Telecommunications Poznan University of Technology Poznań Poland DECYBEL Student Science Club Poznan University of Technology Poznań Poland Faculty of Automatic Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Division of Electronic Systems and Signal Processing Poznan University of Technology Poznań Poland

The paper analyzes the preparation of software for acoustic signal classification with machine learning techniques for microcontrollers. The design process was tested for three types of devices: Nordic Thingy:53, *** and Arduino Nano 33 BLE Sense Lite. The classifier training process was carried out using the Edge Impulse platform. Experimental studies were carried out for the process of classifying sound signals generated by the vacuum cleaner motor. The results of the training and the model test were presented for different configurations.

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