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

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

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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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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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Task Load Based Persistent Coverage control for Large-Scale Domains Using Multi-Agent Systems with Anisotropic Sensors

Task Load Based Persistent Coverage Control for Large-Scale ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Odoy, Kyle Rodríguez-Seda, Erick J. United States Naval Academy Department of Weapons Robotics and Control Engineering AnnapolisMD United States

ISBN: (纸本)9798331504847

Multi-agent coverage control refers to the task of coordinating autonomous robots to effectively survey and gather information across a task-domain. This paper introduces a novel approach that addresses agents equipped with anisotropic sensors and constrained by nonholonomic movement, aiming to ensure persistent coverage and maintain awareness over a large-scale domain. The approach employs redefined Voronoi partitions that leverage each agent's local coverage domain based on its relative task load. The task load herein refers to the level of awareness that each agent has about the domain and the size of its area of dominance, all indirectly encoded in the Voronoi's mass. To account for the vehicles' nonholonomic nature and velocity constraints, the proposed approach is extended using the minimum curvature-based distance when determining the agent's area of dominance. A set of simulations involving teams of three and five agents showcases the effectiveness of the proposed control approach. © 2025 IEEE.

关键词： Cloud computing

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Decentralized Task Assignment for Networked Unmanned Aerial Vehicles

Decentralized Task Assignment for Networked Unmanned Aerial ...

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2025 IEEE SoutheastCon, SoutheastCon 2025

作者： Szarafinski, Hava Kiriakidis, Kiriakos U.S. Naval Academy Department of Weapons Robotics and Control Engineering AnnapolisMD United States

ISBN: (纸本)9798331504847

Competing with the technical advancements of near-peer adversaries, the Navy is shifting towards increased use of Unmanned Aerial Vehicles (UAVs) to accomplish missions. Formations of networked UAVs are susceptible to single-point failure when assigned tasks centrally, leading to the need for decentralization. This paper investigates the optimal allocation of tasks among networked UAVs by means of distributed algorithms to avoid the single-point vulnerability. Furthermore, it addresses the issue of incomplete information about assignment costs, the result of limited direct connections within the formation. The research synthesizes the Hungarian and consensus classes of algorithms to achieve decentralized task allocation subjected to unknown assignment costs and communications being restricted to immediate neighbors. Within the scope of this paper, the number of tasks and agents are equal and agents know their own costs. Assignment costs of other agent-task pairs are not globally known. The success of the proposed approach is established by demonstrating that the synthesized algorithm reaches the same task assignment compared to centralized optimization using the global cost matrix. © 2025 IEEE.

关键词： Drones

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Learning-based robot assembly method for peg insertion tasks on inclined hole using time-series force information

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Biomimetic Intelligence & robotics 2025年第1期5卷 116-123页

作者： Zhifei Shen Zhiyong Jiang Jingwang Zhang Jun Wu Qiuguo Zhu Polytechnic Institute Zhejiang UniversityHangzhou 310015China Robotics Engineering Center the 21st Research Institute of China Electronics Technology Group CorporationShanghai 200233China Department of Control Science and Engineering Zhejiang UniversityHangzhou 310027China

This paper presents a novel method for learning force-aware robot assembly skills,specifically targeting the peg insertion task on inclined *** the peg insertion task involving inclined holes,we employ one-dimensional convolutional networks(1DCNN)and gated recurrent units(GRU)to extract features from the time-series force information during the assembly process,thereby identifying different contact states between the peg and the *** to the identification of contact states,corresponding pose adjustments are executed,and overall smooth interaction is ensured through admittance *** assembly process is dynamically adjusted using a state machine to fine-tune admittance control parameters and seamlessly switch the assembly *** the utilization of dual-arm clamping,we conduct key unlocking experiments on bases inclined at varying *** results demonstrate that the proposed method significantly improves the accuracy and success rate of state recognition compared to previous methods.

关键词： Manipulator assembly Peg insertion Force information Contact state Dual-arm

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Long-Range Vehicle Detection of LiDAR using Adaboost Classification

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

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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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Deterministic Learning-Based Tracking control for Parabolic PDE Systems With Infinite-Dimensional Nonlinear Uncertain Dynamics

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IEEE Transactions on Automatic control 2025年第5期70卷 3272-3287页

作者： Zhang, Jingting Yuan, Chengzhi Wu, Fen Wang, Cong Cheng, Hong University of Electronic Science and Technology of China Center for Robotics School of Automation Engineering Chengdu611731 China University of Rhode Island Mechanical Industrial and Systems Engineering Department KingstonRI02881 United States North Carolina State University Mechanical and Aerospace Engineering Department RaleighNC27695 United States Shandong University School of Control Science and Engineering Jinan250061 China

In this article, we investigate the joint problem of dynamics learning and tracking control for a class of parabolic partial differential equation (PDE) systems with infinite-dimensional uncertain nonlinear dynamics. A new learning control scheme is proposed based on the deterministic learning (DL) theory. One key feature of the proposed scheme is its capability of accurately learning the system's nonlinear uncertain dynamics during real-time tracking control with provable stability and convergence of the overall PDE closed-loop system. Specifically, the Galerkin method is first employed to deal with the infinite dimensionality of the PDE system;a novel DL-based adaptive learning control scheme is then proposed using dual radial basis function neural networks (RBF NNs), in which a pair of RBF NNs are employed to address, respectively, the matched and unmatched components of uncertain nonlinear system dynamics. This control scheme is finally examined on the original PDE system, and it is rigorously proved that: first the PDE system's state tracks the prescribed reference trajectory with guaranteed closed-loop stability and tracking accuracy;and second locally accurate identification of the PDE system's dominant nonlinear uncertain dynamics can be achieved with provable convergence of associated NN weights to their optimal values, thereby the learned knowledge can be ultimately stored and represented by the convergent constant RBF NN models. Based on this, an experience-based control scheme is further proposed, which is capable of recalling the associated learned knowledge in real-time to further improve control performance and reduce computational complexity with maintained provable stabilization. It is worth stressing that although this work is focused particularly on parabolic PDE systems, it is groundbreaking with important technical breakthroughs that would facilitate a more complete extension of the DL theory from traditional ordinary differential equation syste

关键词： Galerkin methods

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Towards backward compatibility of ADRC: revisiting classical state-feedback control with integral compensator

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Bulletin of the Polish Academy of Sciences: Technical Sciences 2025年第2期73卷

作者： Mrotek, Mikolaj Michalski, Jacek Madonski, Rafal Pazderski, Dariusz Retinger, Marek Faculty of Control Robotics and Electrical Engineering Institute of Robotics and Machine Intelligence Poznan University of Technology Piotrowo 3a Poznan60-965 Poland Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Faculty of Control Robotics and Electrical Engineering Institute of Automatic Control and Robotics Poznan University of Technology Piotrowo 3a Poznan60-965 Poland

In this paper, the problem of backward compatibility of active disturbance rejection control (ADRC) is investigated. The goal is to contextualize ADRC to deliver its interpretations from the established field of linear control systems. For this study, a control algorithm, denoted here as integral disturbance rejection control (IDRC), is considered that combines classical state-feedback control with an integral compensator. At first, an interpretation of ADRC is involved in terms of existing state-space control approaches. Next, a transition to the frequency domain is performed, which is justified as a significant part of practical control engineering is conducted in that domain. For assumed specific plant structures, both ADRC and IDRC are then holistically compared in terms of transfer function representation and frequency characteristics, as well as steady-state convergence conditions. Such a juxtaposition helps to highlight the similarities and differences of both approaches, whereas the utilized bandwidth parameterization is shown to bring the control system to the same form, thus indicating some interesting practical aspects. Finally, the theoretical results concerning both considered control structures are validated in a set of numerical simulations and experiments conducted on a laboratory hardware testbed. © 2025 The Author(s).

关键词： Disturbance rejection

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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