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Data-Driven Battery Health Prognostics Using Time-Frequency Feature Maps and Spatial-Temporal Neural Network
IEEE Transactions on Vehicular Technology
Year: 2025 | Early Access Article |
Lithium-ion batteries have been widely used in various application scenarios, acting as the heart of power storage systems. Reliable prognostics and health management (PHM) play essential roles in the safe operation and reliable maintenance of battery systems. Within this context, a data-driven method based on time-frequency feature maps and spatial-temporal neural networks is proposed for state-o...Show More
Multipole Magnetic Tactile Sensor With Super-Resolution for 3-D Pose Estimation
Jiwei Huang;Yunjiang Lou;Xiaogang Xiong;Xiansheng Yang;Yanjie Li
Magnetic tactile sensors are widely used in tactile servo and robotic manipulations because of their compact size and high sensitivity. However, their spatial resolution is constrained by the number of Hall elements in a contact area, while their limited capacity to perceive 3-D information hinders their broader applications. This article introduces a grid multipole magnetization method for magnet...Show More
TARS: Tactile Affordance in Robot Synesthesia for Dexterous Manipulation
Qiwei Wu;Haidong Wang;Jiayu Zhou;Xiaogang Xiong;Yunjiang Lou
In the field of dexterous robotic manipulation, integrating visual and tactile modalities to inform manipulation policies presents significant challenges, especially in non-contact scenarios where reliance on tactile perception can be inadequate. Visual affordance techniques currently offer effective manipulation-centric semantic priors focused on objects. However, most existing research is limite...Show More
Error-Free Approximation of Explicit Linear MPC Through Lattice Piecewise Affine Expression
In this article, the disjunctive and conjunctive lattice piecewise affine (PWA) approximations of explicit linear model predictive control (MPC) are proposed. Training data consisting of states and corresponding affine control laws are generated in a control invariant set, and redundant sample points are removed to simplify the construction of lattice PWA approximations. Resampling is proposed to ...Show More
Time-Optimal Velocity Planning of Single-Axis Multipoint Motion With Global Dynamic Programming Algorithm
Ke Li;Xiaogang Xiong;Yunjiang Lou;Shanda Wang;Yuping Huang;Longfei Jia
To solve the time-optimal problem of velocity planning, various optimization-based methods were proposed in the literature, but these existing methods typically have limitations on completeness and real-time performance. For the scenario of single-axis multipoint (SAMP) motion, this article proposes a global dynamic programming algorithm with local greedy strategies to solve the time-optimal veloc...Show More
MPC-Guided Deep Reinforcement Learning for Optimal Charging of Lithium-Ion Battery With Uncertainty
Zhipeng Zhu;Guangzhong Dong;Yunjiang Lou;Li Sun;Jincheng Yu;Liangcai Wu;Jingwen Wei
Ensuring the safe and fast charging of lithium-ion battery (LIB) is a pivotal technology that plays a key role in advancing the wide application of electric vehicles (EVs). Currently, the majority of model-based charging methods are developed for deterministic models, lacking consideration for strategy failure and battery safety issues caused by model or data uncertainty. Learning-based charging m...Show More
Deep Transfer Learning Enabled State of Health Estimation of Lithium-Ion Battery Using Voltage Sample Entropy Under Fast Charging Profiles
Accurately monitoring the battery state of health (SOH) is of great significance in the safe, reliable, and efficient operations of battery energy storage systems. With the increasing demands for sophisticated battery control strategies and working conditions, this article proposes deep transfer learning (TL)-enabled SOH estimation techniques using voltage sample entropy under fast charging profil...Show More
Dynamic Object Removal of Static 3D Point-Cloud Map Building in Casualty Collection Point
Haidong Wang;Wanlei Li;Yijie Dai;Xiaogang Xiong;Yunjiang Lou
2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Year: 2024 | Conference Paper |
Understanding the environment is crucial for the autonomous navigation of vehicles. Accurately identifying and removing dynamic objects that cause occlusions and noisy pose issues is crucial to the task. The casualty collection point (CCP) is a designated location for treating casualties during disasters. These sites are commonly located in open fields to ensure that the injured receive timely and...Show More
DCBF-based Trajectory Planning for Mobile Manipulators in Complex and Dynamic Work Environments
2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Year: 2024 | Conference Paper |
Traditional trajectory planning methods are challenged by high-dimensional robot navigation, particularly in handling high-velocity obstacles and computation efficiency. This paper introduces a novel approach leveraging Dynamic Control Barrier Functions (DCBF) to address these issues. The proposed method ensures safety and precise obstacle avoidance in dynamic environments, demonstrated through su...Show More
Design and Autonomous Wearing of a Wristband with a Mobile Manipulator for Triage
2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Year: 2024 | Conference Paper |
In post-disaster scenarios, the lack of medical staff at Casualty Collection Points (CCPs) slows down injury triage. Using robots to apply wristbands to casualties may be a promising solution, but it could potentially pose a risk of further injury due to limb adjustments. To address this, this paper proposes deploying a search and rescue (SAR) robot equipped with a manipulator at CCPs. These robot...Show More
Time-Varying Multi-Goal Path Planning with Multi-Tree RRT* Algorithm for Quadruped Robots
Haidong Wang;Xiaogang Xiong;Haixiang Zhou;Ke Li;Yunjiang Lou
2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)
Year: 2024 | Conference Paper |
Quadruped robots are being widely deployed in various scenarios with uneven terrains, such as rescue and supervision, due to their ability to climb obstacles and carry heavy loads. However, these robots struggle when faced with complex tasks that require reaching time-varying multiple target locations or landmarks. The visiting order of the landmarks and the total travel cost can significantly imp...Show More
RTTF: Rapid Tactile Transfer Framework for Contact-Rich Manipulation Tasks
Qiwei Wu;Xuanbin Peng;Jiayu Zhou;Zhuoran Sun;Xiaogang Xiong;Yunjiang Lou
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
An increasing number of robotic manipulation tasks now use optical tactile sensors to provide tactile feedback, making tactile servo control a crucial aspect of robotic operations. This paper presents a rapid tactile transfer framework (RTTF) that achieves optical-tactile image sim2real transfer and robust tactile servo control using limited paired data. The sim2real aspect of RTTF employs a semi-...Show More
Whole-body Compliance Control for Quadruped Manipulator with Actuation Saturation of Joint Torque and Ground Friction
Tianlin Zhang;Xuanbin Peng;Fenghao Lin;Xiaogang Xiong;Yunjiang Lou
2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2024 | Conference Paper |
In normal operations, when quadruped manipulators with impedance control experience external disturbances, they may become unstable and lose balance due to actuation saturation, affecting their stability, safety, and compliance with the environment. To address this issue, we propose a whole-body compliance controller to prevent unstable behaviors like slip, oscillation, and overshoot, which arise ...Show More
Implicit Euler Discrete-Time Set-Valued Admittance Control for Impact–Contact Force Control
IEEE/ASME Transactions on Mechatronics
Year: 2024 | Early Access Article |
Admittance control is a commonly used strategy for regulating robotic systems, such as quadruped and humanoid robots, allowing them to respond compliantly to contact forces during interactions with their environments. However, it can lead to instability and unsafe behaviors, such as snapping back and overshooting due to torque saturation from impacts with unknown stiffness environments. This artic...Show More
Design and Robot Autonomous Wearing of Tactile Perception Nose Clip for Human Respiration Rate Detection
2024 IEEE 25th China Conference on System Simulation Technology and its Application (CCSSTA)
Year: 2024 | Conference Paper |
In order to autonomously sense the injured person’s physiological indices like respiration rate and thus take immediate robotic triage in complex disaster scenarios, a nose clip system with tactile perception was designed, and its autonomous wearing method with the help of tactile information and visual information was proposed. A test device for autonomous wearing of nose clip detection was set u...Show More
Proxy-Based Super-Twisting Algorithm for MEMS Mirror Control Under Input Saturation and Vibration
IEEE/ASME Transactions on Mechatronics
Year: 2024 | Early Access Article |
Microelectromechanical systems (MEMSs) scanning mirrors are widely used in imaging devices such as light detection and ranging (LIDAR) and head-up displays (HUDs). When installed in autonomous vehicles, these MEMS mirrors must accurately track set-points or continuous trajectories despite vibration caused by rugged environments and limited input voltage due to battery management. Super-twisting al...Show More
Discrete-Time Torque-Limited Set-Valued Admittance Control with Semi-Implicit Method
2024 43rd Chinese Control Conference (CCC)
Year: 2024 | Conference Paper |
The conventional admittance control is used to regulate robotic systems and ensure they react safely against external contact forces. It does this by suppressing the hardware dynamics of the system during contact with soft environments. However, it has limitations in adapting to unknown environments and can exhibit unsafe behaviors such as snapping back or overshooting when there are changes in th...Show More
Experimental Validation of Suspension Structure through Vibration Equivalence
Ke Li;Man Huang;Linfan Yu;Yinan Liu;Yanbo Wang;Xiaogang Xiong;Yunjiang Lou
2024 IEEE International Conference on Mechatronics and Automation (ICMA)
Year: 2024 | Conference Paper |
Given the challenges in controlling the large suspension structure during physical vibration tests due to its large mass, volume, and complex connection structure, we propose an experimental platform system using an equivalent mass block vibration load. This system treats the airborne cantilever structure as a combination of a spring damping system and an equivalent mass block. By dynamically mode...Show More
Markless Visual Servoing for 3C Product Assembly with Quotient Kinematics Manipulator
Yixin Xie;Qiang Fu;Xiansheng Yang;Zhibin Li;Yanjie Li;Yunjiang Lou
2024 IEEE 18th International Conference on Control & Automation (ICCA)
Year: 2024 | Conference Paper |
Due to the small size of 3C (Computer, Communication and Consumer electronics) parts and their high assembly accuracy requirements, traditional industrial robots cannot meet the needs of flexible assembly. To address manufacturing errors and uncertainties in part poses, this study proposes a markerless visual servoing method. Through analysis of assembled 3C parts and manual assembly movements, a ...Show More
Operational Hybrid Neural Network Model for NO_{x} Forecast and Control in Real-World 2-GW Coal-Fired Power Plant
Yinghao Chu;Xiaogang Xiong;Yunjiang Lou;Peng Wang;Liwu Duan;Yingjie He
This study presents the development and implementation of an advanced hybrid neural network (HNN) model for predicting nitrogen oxide (NO_{x}) emissions and controlling ammonia (NH_{3}) injection in a 1-GW generator within a 2-GW operational coal-fired power plant. The HNN model, which integrates both endogenous and exogenous input features to effectively analyze complex relationships, shows s...Show More
Distributional Policy Gradient With Distributional Value Function
Qi Liu;Yanjie Li;Xiongtao Shi;Ke Lin;Yuecheng Liu;Yunjiang Lou
IEEE Transactions on Neural Networks and Learning Systems
Year: 2024 | Early Access Article |
In this article, we propose a distributional policy-gradient method based on distributional reinforcement learning (RL) and policy gradient. Conventional RL algorithms typically estimate the expectation of return, given a state–action pair. Furthermore, distributional RL algorithms consider the return as a random variable and estimate the return distribution that can characterize the probability o...Show More
Hybrid Physics and Data-Driven Electrochemical States Estimation for Lithium-ion Batteries
Guangzhong Dong;Guangxin Gao;Yunjiang Lou;Jincheng Yu;Chunlin Chen;Jingwen Wei
Accurate estimation of electrochemical states plays a fundamental role in guaranteeing safe, reliable, and efficient operations of lithium battery systems. However, current estimation algorithms rely on semi-empirical models that lack physical insights or expensive physics-based models that are hard to implement. Thus, this paper proposes an efficient hybrid physics-based and data-driven electroch...Show More
Augmented Hybrid Learning for Visual Defect Inspection in Real-World Hydrogen Storage Manufacturing Scenarios
Yinghao Chu;Yingjie He;Xiaogang Xiong;Yunjiang Lou;Congzheng Yu;Liwu Duan
Ensuring product quality while reducing costs is critical in manufacturing scenarios. However, real-world operational factories, particularly in the hydrogen storage industries, pose several challenges, including strict quality control standards and limited but extremely biased data available for model development. To address these challenges, we propose an augmented hybrid learning method for vis...Show More
Data Efficient Deep Reinforcement Learning With Action-Ranked Temporal Difference Learning
Qi Liu;Yanjie Li;Yuecheng Liu;Ke Lin;Jianqi Gao;Yunjiang Lou
In value-based deep reinforcement learning (RL), value function approximation errors lead to suboptimal policies. Temporal difference (TD) learning is one of the most important methodologies to approximate state-action (Q) value function. In TD learning, it is critical to estimate Q values of greedy actions more accurately because a more accurate target Q value enhances the estimation accura...Show More
Optimal Charging of Lithium-Ion Battery Using Distributionally Robust Model Predictive Control With Wasserstein Metric
Guangzhong Dong;Zhipeng Zhu;Yunjiang Lou;Jincheng Yu;Liangcai Wu;Jingwen Wei
Developing a fast and safe charging strategy has been one of the key breakthrough points in lithium battery development owing to its range anxiety and long charging time. The majority of current model-based charging strategies are developed for deterministic systems. Real battery dynamics are, however, affected by model mismatches and process uncertainties, which may lead to constraint violations ...Show More
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