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Reinforcement Learning for Highway Driving Decision-making Using CDR (Curriculum based Domain Randomization)

Journal of Institute of control, robotics and Systems 2025年第2期31卷 114-123页

作者： Yong, Jae-Hyoung Kim, Min-Sung Park, Tae-Hyoung Department of Intelligent System and Robotics Chungbuk National University Korea Republic of Department of Control Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Chungbuk National University Hospital Korea Republic of

Incorrect autonomous driving decisions on highways can lead to traffic congestion and accidents. Therefore, accurate decision-making in highways is essential. However, decision-making in highways is a challenging task due to the complexity of traffic situations. Thus, reinforcement learning is an appropriate approach for decision-making in highways. However, a domain gap often arises between the training and testing environments due to differences in them. Therefore, a curriculum-based domain randomization approach is applied, which progressively adjusts the level of random parameters. The RL model is trained using the SUMO simulator and tested in the MORAI simulator, where white noise is added to simulate real-world conditions. Consequently, applying curriculum domain randomization has the lowest collision rate. © ICROS 2025.

关键词： Traffic congestion

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Camera-LiDAR Fusion for Detecting Traffic Law Violations in Surveillance Systems

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Journal of Institute of control, robotics and Systems 2025年第3期31卷 187-195页

作者： Kim, Taek-Lim Jang, Byoung-Jin Yeon, Joo-Yeon Park, Tae Hyoung Department of Control and Robot Engineering Chungbuk National University Korea Republic of Department of Intelligent System and Robotics Chungbuk National University Korea Republic of

Traffic surveillance systems are essential for ensuring public safety and optimizing urban traffic flow by accurately detecting, classifying, and monitoring traffic law violations such as illegal parking and jaywalking. However, detecting such violations in dynamic urban environments poses significant challenges due to unpredictable conditions. Traditional camera-based systems excel in capturing visual details but suffer performance degradation under low light, occlusion, or complex intersection scenarios, leading to false positives and missed detections. Therefore, we propose a camera-LiDAR fusion-based traffic violation detection system. LiDAR's independence from lighting conditions complements camera capabilities, especially in night scenarios or occluded environments. Experimental results in a real-world urban environment demonstrate the system’s effectiveness in reducing false positives and missed detections compared to traditional camera-only approaches. This study underscores the potential of sensor fusion to improve traffic management systems and points out future research opportunities in challenging conditions. © ICROS 2025.

关键词： Urban transportation

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Learning Agile Quadrotor Flight in Restricted Environments with Safety Guarantees

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年第9期9卷 1-8页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Wu, Xinyu Department of Control Science and Engineering Harbin Institute of Technology Shenzhen and Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

With the increasing requirement for agile and efficient controllers in safety-critical scenarios, controllers that exhibit both agility and safety are attracting attention, especially in the aerial robotics domain. This paper focuses on the safety issue of Reinforcement Learning (RL)-based control for agile quadrotor flight in restricted environments. To this end, we propose a unified Adaptive Safety Predictive Corrector (ASPC) to certify each output action of the RL-based controller in real-time, ensuring its safety while maintaining agility. Specifically, we develop the ASPC as a finite-horizon optimal control problem, formulated by a variant of Model Predictive control (MPC). Given the safety constraints determined by the restricted environment, the objective of minimizing loss of agility can be optimized by reducing the difference between the actions of RL and ASPC. As the safety constraints are decoupled from the RL-based control policy, the ASPC is plug-and-play and can be incorporated into any potentially unsafe controllers. Furthermore, an online adaptive regulator is presented to adjust the safety bounds of the state constraints with respect to the environment changes, extending the proposed ASPC to different restricted environments. Finally, simulations and real-world experiments are demonstrated in various restricted environments to validate the effectiveness of the proposed ASPC. IEEE

关键词： Reinforcement learning

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Aggressive and robust low-level control and trajectory tracking for quadrotors with deep reinforcement learning

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Neural Computing and Applications 2025年第3期37卷 1223-1240页

作者： Chen, Shiyu Li, Yanjie Lou, Yunjiang Lin, Ke Department of Control Science and Engineering Harbin Institute of Technology Shenzhen Shenzhen518055 China Guangdong Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics Shenzhen518055 China

Executing accurate trajectory tracking tasks using a high-performance low-level controller is crucial for quadrotors to be applied in various scenarios, especially those involving uncertain disturbances. However, due to the uncertainties in disturbed environments, developing effective low-level controllers with traditional model-based control schemes is challenging. This paper presents an aggressive and robust reinforcement learning (RL)-based low-level control policy for quadrotors. The policy maps the observed quadrotor state directly to motor thrust commands, without requiring the quadrotor dynamics. Additionally, a trajectory generation pipeline is developed to improve the accuracy of trajectory tracking tasks based on differential flatness. With the learned low-level control policy, extensive simulations and real-world experiments are implemented to validate the performance of the policy. The results indicate that our RL-based low-level control policy outperforms traditional proportional–integral–derivative (PID) control methods and related learning-based policies in terms of accuracy and robustness, particularly in environments with uncertain disturbances. Furthermore, the proposed RL-based control policy exhibits an aggressive response in trajectory tracking, even when the speed of the desired trajectory is increased to 6 m/s. Moreover, the learned policy demonstrates strong vibration suppression capabilities and enables the quadrotor to recover to a hovering state from random initial conditions with shorter response time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Deep reinforcement learning

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All-Fabric Bi-directional Actuators for Multi-joint Assistance of Upper Limb

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Journal of Bionic Engineering 2023年第6期20卷 2661-2669页

作者： Junlin Ma Diansheng Chen Zhe Liu Jie Wei Xianglin Zhang Zihan Zeng Yongkang Jiang Institute of Robotics Beihang UniversityBeijing100191China Department of Control Science and Engineering Tongji UniversityShanghai201804China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210China

According to clinical studies,upper limb robotic suits are vital to reduce therapist fatigue and accelerate patient *** pneumatic actuators have drawn increasing attention for the development of wearable robots due to their low weight,flexibility,and high power-to-weight ***,most of current actuators were designed for the flexion assistance of a specific joint,and that for joint extension requires further ***,designing an actuator for diverse working scenarios remains a *** this paper,we propose an all-fabric bi-directional actuator to assist the flexion and extension of the elbow,wrist,and fingers.A mathematical model is presented that predicts the deformation and guides the design of the proposed bi-directional *** further validate the applicability and adaptability of the proposed actuator for different joints,we developed a 3-DOF soft robotic *** results show that the robotic suit can assist the motion of the elbow,wrist,and finger of the subject.

关键词： Soft robotic suits Fabric-based actuators Multi-DOF motion Upper limb rehabilitation

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Transfer Learning: An Approach on the Riemannian Manifold for Multiclass Motor Imagery EEG Brain-Computer Interface 30

Transfer Learning: An Approach on the Riemannian Manifold fo...

引用

30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Sun, Xu Chu, Yaqi Luo, Yang Zhao, Xingang Northeastern University Department of Control Engineering Shenyang110819 China China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China

ISBN: (纸本)9798350391916

Motor imagery (MI) EEG-based brain-computer interface (BCI) facilitates direct communication between the brain's intentions and a computer. In essence, it allows a person to control external equipment by decoding EEG signals during the mental imagery of limb movements. However, due to the intricate individual variances in EEG signals, creating a general decoding model with parameters applicable to all subjects is exceptionally challenging. Consequently, a prolonged calibration process is necessary to gather labeled subject-specific data for each individual, making it less user-friendly. A potential solution to this problem is transfer learning, a methodology that transfers knowledge from related domains to a target domain. To tackle the problem, this paper proposes a novel transfer learning approach on the Riemannian manifold framework in the context of multiclass MI EEG-based BCI classification. Particularly, a user-specific frequency band selection (FBS) method with MI EEG class distinctiveness is utilized to improve the accuracy and efficiency of Riemannian space calculations, which is measured using the inter-class distance and intra-class variance on the manifold. Then, the Riemannian space Alignment (RA) strategy is used to calibrate the MI EEG variances of different subjects. The comparative experiments between the proposed approach and baseline/conventional methods are conducted on a public dataset with four-class motor imagery EEG, including two-class transfer learning scenario and four-class transfer learning scenario. proposed transfer learning approach is outperformed. Compared with the baseline/conventional methods using a fixed wide frequency band, the preliminary results suggests that the proposed approach can significantly improve the transfer learning performance for the MI EEG signals from different subjects in Riemannian space. © 2024 IEEE.

关键词： Brain computer interface

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Long-Range Vehicle Detection of LiDAR using Adaboost Classification

Long-Range Vehicle Detection of LiDAR using Adaboost Classif...

引用

2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Lim, Na-Young Lee, Jeon-Hyeok Park, Tae-Hyoung Chungbuk National University Department of Intelligent Systems & Robotics Cheongju Korea Republic of Chungbuk National University Department of Control & Robot Engineering Cheongju Korea Republic of

ISBN: (纸本)9798331531614

In recent developments, autonomous racing has garnered attention as it aims to overcome the limitations of standard autonomous driving systems. Achieving safe racing conditions necessitates both fast and long-range perception. However, current 3D LiDAR object detection methods face challenges with high computational costs and limited detection ranges. These issues make them unsuitable for racing scenarios. To address these challenges, this paper proposes a clustering-based long-range vehicle detection method that relies solely on LiDAR. First, the method removes ground points and foreground points and clusters the remaining points. Subsequently, these clusters are classified as vehicles using AdaBoost, generating 2D bounding boxes in the range view. Experimental results demonstrate superior performance, achieving a computational efficiency of 53 Hz and a long-range detection accuracy of over 80%, compared to voxel-based and range-based methods. This approach offers a viable solution for autonomous racing environments. © 2025 IEEE.

关键词： Autonomous vehicles

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A Position-Force control Approach for an Underwater Vehicle-Manipulator System

A Position-Force Control Approach for an Underwater Vehicle-...

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2023 International Conference on Ocean Studies, ICOS 2023

作者： Kabanov, Aleksey A. Dementiev, Kirill V. Sevastopol State University Department of Informatics and Control in Technical Systems Sevastopol Russia Sevastopol State University Robotics and Intelligent Control System Laboratory Sevastopol Russia

ISBN: (纸本)9798350382457

The paper proposes an approach to position-force control in the underwater vehicle-manipulator systems. There are two different strategies to realize position-force control systems: through a decoupled or coupled controller. The first one is more flexible and is used in the present article as a PID-controllers array. The paper considers the generalized system design methodology applicable to control objects of any kinematic structure. Compensation blocks for manipulator disturbances and calculation of additional end-effector offset to maintain a constant force on the operating point are proposed. The system also utilizes the desired underwater vehicle position calculation based on the relativity of the vehicle position and operating point for a heading set point. The modeling results showed that the system is able to perform qualitatively force operations in a given point of the working space. In addition, the graphs revealed the compensator unit efficiency and confirmed the necessity of the additional offset calculation unit. © 2023 IEEE.

关键词： Manipulators

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6-DOF AUV Identification with MIMO-NARX

6-DOF AUV Identification with MIMO-NARX

引用

2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Kabanov, Aleksey A. Lipko, Ivan Y. Dementiev, Kirill V. Sevastopol State University Department of Informatics and Control in Technical Systems Sevastopol Russia Sevastopol State University Robotics and Intelligent Control System Laboratory Sevastopol Russia

ISBN: (纸本)9781665475952

This paper applies the MIMO NARX neural model to identify AUV with six degrees of freedom. Strongly nonlinear dependencies are modeled using a MIMO NARX model trained using «input-output» experimental data. This results in a grey box model with a partially known internal structure that can be supervised. This is achieved by specifying a certain regressors type, which is feedback to the model input. Obtained results show that the neural model trained by the Levenberg-Marquardt numerical algorithm provides acceptable performance and accuracy. Validation of the model was performed using data from full-scale experiments, which allowed to identify the weaknesses and advantages of the resulting system more accurately. © 2023 IEEE.

关键词： Degrees of freedom (mechanics)

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Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 70-76页

作者：茅天阳赵文韬王景川陈卫东 Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile *** is a time-consuming process,and mechanical distortion will cause extrinsic calibration ***,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic *** the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different *** nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic *** experiments were carried out on both public datasets and real-world *** indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

关键词： autonomous vehicles multi-sensor fusion online calibration

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