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arXiv 2024年

作者： Hu, Xuemin Chen, Pan Wen, Yijun Tang, Bo Chen, Long School of Artificial Intelligence Hubei University Wuhan430062 China Key Laboratory of Intelligent Sensing System and Security Hubei University Ministry of Education Hubei Wuhan430062 China Department of Electrical and Computer Engineering Worcester Polytechnic Institute WorcesterMA01609 United States State Key Laboratory of Multimodal Artificial Intelligence Systems State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi’an710049 China

Reinforcement learning (RL) has been widely used in decision-making and control tasks, but the risk is very high for the agent in the training process due to the requirements of interaction with the environment, which seriously limits its industrial applications such as autonomous driving systems. Safe RL methods are developed to handle this issue by constraining the expected safety violation costs as a training objective, but the occurring probability of an unsafe state is still high, which is unacceptable in autonomous driving tasks. Moreover, these methods are difficult to achieve a balance between the cost and return expectations, which leads to learning performance degradation for the algorithms. In this paper, we propose a novel algorithm based on the long and short-term constraints (LSTC) for safe RL. The short-term constraint aims to enhance the shortterm state safety that the vehicle explores, while the long-term constraint enhances the overall safety of the vehicle throughout the decision-making process, both of which are jointly used to enhance the vehicle safety in the training process. In addition, we develop a safe RL method with dual-constraint optimization based on the Lagrange multiplier to optimize the training process for end-to-end autonomous driving. Comprehensive experiments were conducted on the MetaDrive simulator. Experimental results demonstrate that the proposed method achieves higher safety in continuous state and action tasks, and exhibits higher exploration performance in long-distance decision-making tasks compared with state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Lagrange multipliers

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Real-time Trajectory Generation for Multiple Drones using Bézier Curves ⁎

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IFAC-PapersOnLine 2020年第2期53卷 9276-9281页

作者： Bahareh Sabetghadam Rita Cunha António Pascoal Laboratory of Robotics and Engineering Systems (LARSyS) ISR/IST University of Lisbon Lisbon Portugal

Practical applications of drones are expanding into many new areas due to their fast-evolving technology. Looking further into the future, it is very likely that applications will require more than one drone to tackle a specific task, calling for reliable and efficient algorithms that can generate collision-free trajectories for multiple drones, under timing constraints of real-time applications. In this paper, we study a motion planning method based on the Bézier parametrization of spatial paths with a special focus on the less addressed issue in this method, (inefficient) constraint evaluation, that might hinder its use in real-time trajectory generation for multi-drone applications. We take advantage of the Bézier curves properties to obtain a small-scale optimization problem and find a finite set of inequalities that guarantee constraints satisfaction. We also propose a method to lower the conservatism in the resulting set of inequalities without the need to use unnecessary high-degree Bézier curves. Numerical results illustrate the efficacy of the presented method in reducing the computational costs associated with generating collision-free trajectories for multiple drones and re-planning them online with a receding horizon.

关键词： Trajectory Generation Real-time Bezier Curves

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Corretions to “An Automatic Navigation Framework for Magnetic Fish-Like Millirobot in Uncertain Dynamic Environments”

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IEEE robotics and Automation Letters 2025年第4期10卷 3875-3875页

作者： Chenyao Tian Xinjian Fan Jingzhi Jia Zhan Yang Hui Xie State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Mechanical and Electrical Engineering Soochow University Suzhou China

The first author's name was misspelled as “Chengyao Tian” in [1], containing an extraneous “g”. The correct spelling is “Chenyao Tian”.

关键词： Navigation

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Position Sensorless Control of Permanent Magnet Synchronous Motor Based on Adaptive Filtering Sliding Mode Observer

Position Sensorless Control of Permanent Magnet Synchronous ...

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第34届中国控制与决策会议

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences

In the position sensorless control strategy of permanent magnet synchronous motor(PMSM),the back-EMF method based on sliding mode observer(SMO) is favored because of its *** this paper,a SMO based on the piecewise sigmoid function is *** on the back-EMF observation error,the gain adaptation of the SMO is realized,which effectively reduces the high-frequency chattering problem of the conventional *** low-pass filter of the conventional SMO is improved,and an adaptive filter based on the back-EMF low-pass filter is *** to the back-EMF observation error,the cut-off frequency of the low-pass filter is changed in real-time,and the estimation of rotor position is *** Matlab/Simulink simulation verification,the control strategy is feasible and achieves a better control effect.

关键词： Sensorless control SMO Adaptive gain and Adaptive filter

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Design of Cascaded Model Predictive Controller for Modular Robot Joints

Design of Cascaded Model Predictive Controller for Modular R...

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第34届中国控制与决策会议

作者： Wang Bolun Zhang Tao Shi Zhengjin Zhang Hualiang School of Automation and Electrical Engineering Shenyang University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

As the core driving force providers in the robot system of next generation,modular robot joint's motion and power performance directly affects the overall motion control effect of robot *** design starts from the servo control of the modular robot joint and follows the principle of model predictive control to devise a cascaded model predictive controller based on the mathematical model of a permanent magnet synchronous motor for the joint to substitute for the traditional dual-loop PI controller with current and velocity loops in the closed-loop vector control *** design also employs the MATLAB/Simulink platform to simulate the control *** simulation experiments illuminate that the design of the cascaded model predictive controller has better dynamic response performance than the traditional PI controller and effectively enhances system robustness.

关键词： Modular Robot Joints Motor servo control Model predictive control

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CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

arXiv

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arXiv 2022年

作者： Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng School of Data Science Chinese University of Ho ng Kong Shenzhen Shenzhen China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2008 Australia

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has O(n) computational complexity. Experimental tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear equations

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Tactile grasp stability classification based on graph convolutional networks

Tactile grasp stability classification based on graph convol...

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2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Mi, Tingting Que, Dashun Fang, Senlin Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Yi, Zhengkun Wu, Xinyu School of Information Engineering Wuhan University of Technology Wuhan430070 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

One of the challenges for robots to grasp unknown objects is to predict whether objects will fall at the beginning of grasping. Evaluating robotic grasp state accurately and efficiently is a significant step to address this issue. In this paper, based on the different fusion approaches of multi-sensor tactile signals, we propose two novel methods based on Graph Convolution Network (GCN) for robotic stability classification. Specifically, we propose two deep learning methods including GCN based on data-level fusion (GCN-DF) and GCN based on feature-level fusion (GCN-FF). We explore the optimal parameters for transforming sensor signals into a graph structure. Furthermore, we verify the effectiveness of the proposed methods on the BioTac Grasp Stability (BiGS) dataset. The experimental results prove that the proposed approaches achieve higher classification accuracy than Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). © 2021 IEEE.

关键词： Convolution

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Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environments

arXiv

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arXiv 2023年

作者： Yue, Lu Zhou, Dongliang Xie, Liang Zhang, Feitian Yan, Ye Yin, Erwei The College of Engineering Peking University Beijing100871 China Defense Innovation Institute Academy of Military Sciences Beijing100071 China Tianjin Artificial Intelligence Innovation Center Tianjin300450 China Shenzhen518055 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

The task of vision-and-language navigation in continuous environments (VLN-CE) aims at training an autonomous agent to perform low-level actions to navigate through 3D continuous surroundings using visual observations and language instructions. The significant potential of VLN-CE for mobile robots has been demonstrated across a large number of studies. However, most existing works in VLN-CE focus primarily on transferring the standard discrete vision-and-language navigation (VLN) methods to continuous environments, overlooking the problem of collisions. Such oversight often results in the agent deviating from the planned path or, in severe instances, the agent being trapped in obstacle areas and failing the navigational task. To address the above-mentioned issues, this letter investigates various collision scenarios within VLN-CE and proposes a classification method to predicate the underlying causes of collisions. Furthermore, a new VLN-CE algorithm, named Safe-VLN, is proposed to bolster collision avoidance capabilities including two key components, i.e., a waypoint predictor and a navigator. In particular, the waypoint predictor leverages a simulated 2D LiDAR occupancy mask to prevent the predicted waypoints from being situated in obstacle-ridden areas. The navigator, on the other hand, employs the strategy of ‘re-selection after collision’ to prevent the robot agent from becoming ensnared in a cycle of perpetual collisions. The proposed Safe-VLN is evaluated on the R2R-CE, the results of which demonstrate an enhanced navigational performance and a statistically significant reduction in collision incidences. Copyright © 2023, The Authors. All rights reserved.

关键词： Navigation

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Coordinate Descent Clustering with Trace Difference Criterion for Joint Clustering and Feature Extraction

SSRN

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SSRN 2023年

作者： Wang, Quan Wang, Fei Li, Zhongheng Wang, Zheng Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Clustering with Trace Difference criterion (CDCTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDCTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDCTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDCTD uses the coordinate descent method for clustering, improving the ability to address the many-cluster problem. Extensive experiments show that CDCTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Robust LMI-Based State-Feedback Control of a Knee Exoskeleton Robotic System

Robust LMI-Based State-Feedback Control of a Knee Exoskeleto...

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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Sahar Jenhani Hassène Gritli Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies & RISC Lab (LR16ES07) University of Carthage Tunis Tunisia

Neurological conditions such as strokes, spinal cord injuries, and skeletal muscle weakness can significantly impair an older person’s ability to carry out essential everyday chores. Exoskeletons, also known as wearable technology, are becoming more popular as a way to help the elderly and people with limb disorders move around and recover. In this paper, we focus on the lower limb exoskeletons that are mainly used for physical movement assistance and rehabilitation. Our main objective is to control the actuated lower limb orthosis at the knee joint level. Then, to achieve this goal, we will rely on the dynamics of the shank-orthosis system, which is described via a second-order nonlinear model that takes into account solid and viscous frictions. Thus, in order to robustly stabilize the knee exoskeleton, we introduce a state-feedback control law. Moreover, we use the Schur complement lemma, the Young inequality, and the S-procedure lemma to design LMI conditions, guaranteeing the robust control of the angular position of the knee exoskeleton robotic system. Some simulation results are conducted showing the robustness against external perturbations and unknown friction parameters.

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