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arXiv 2023年

作者： Song, Liang Wang, Guangming Liu, Jiuming Fu, Zhenyang Miao, Yanzi Wang, Hesheng Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

In recent studies, the generalization of neural radiance fields for novel view synthesis task has been widely explored. However, existing methods are limited to objects and indoor scenes. In this work, we extend the generalization task to outdoor scenes, trained only on object-level datasets. This approach presents two challenges. Firstly, the significant distributional shift between training and testing scenes leads to black artifacts in rendering results. Secondly, viewpoint changes in outdoor scenes cause ghosting or missing regions in rendered images. To address these challenges, we propose a geometric correction module and an appearance correction module based on multi-head attention mechanisms. We normalize rendered depth and combine it with light direction as query in the attention mechanism. Our network effectively corrects varying scene structures and geometric features in outdoor scenes, generalizing well from object-level to unseen outdoor scenes. Additionally, we use appearance correction module to correct appearance features, preventing rendering artifacts like blank borders and ghosting due to viewpoint changes. By combining these modules, our approach successfully tackles the challenges of outdoor scene generalization, producing high-quality rendering results. When evaluated on four datasets (Blender, DTU, LLFF, Spaces), our network outperforms previous methods. Notably, compared to MVSNeRF, our network improves average PSNR from 19.369 to 25.989, SSIM from 0.838 to 0.889, and reduces LPIPS from 0.265 to 0.224 on Spaces outdoor scenes. © 2023, CC BY.

关键词： Rendering (computer graphics)

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control for Tractor-Trailer Trajectory Tracking with Unknown Parameters

arXiv

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arXiv 2023年

作者： Wang, Zehao Zhang, Han Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC. © 2023, CC BY-NC-SA.

关键词： Model predictive control

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Distributed Generalized Nash Equilibria Learning for Online Stochastic Aggregative Games

arXiv

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arXiv 2025年

作者： Du, Kaixin Meng, Min Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai200092 China Department of Control Science and Engineering College of Electronics and Information Engineering National Key Laboratory of Autonomous Intelligent Unmanned System Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai Research Institute for Intelligent Autonomous Systems Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China

This paper investigates online stochastic aggregative games subject to local set constraints and time-varying coupled inequality constraints, where each player possesses a time-varying expectation-valued cost function relying on not only its own decision variable but also an aggregation of all the players’ variables. Each player can only access its local individual cost function and constraints, necessitating partial information exchanges with neighboring players through time-varying unbalanced networks. Additionally, local cost functions and constraint functions are not prior knowledge and only revealed gradually. To learn generalized Nash equilibria of such games, a novel distributed online stochastic algorithm is devised based on push-sum and primal-dual strategies. Through rigorous analysis, high probability bounds on the regret and constraint violation are provided by appropriately selecting decreasing stepsizes. Moreover, for a time-invariant stochastic strongly monotone game, it is shown that the generated sequence by the designed algorithm converges to its variational generalized Nash equilibrium (GNE) almost surely, and the time-averaged sequence converges sublinearly with high probability. Finally, the derived theoretical results are illustrated by numerical simulations. Copyright © 2025, The Authors. All rights reserved.

关键词： Nash equilibrium

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Enhanced Obstacle Detection in Autonomous Vehicles using 3D LiDAR Mapping Techniques

Enhanced Obstacle Detection in Autonomous Vehicles using 3D ...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Muhammed Enes Tokgöz Abdullah Yusefi İbrahim Toy Akif Durdu Research and Development MPG Machinery Production Group Inc. Co. Konya Turkey Department of Electrical and Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Turkey

In this study, a method utilizing a 3D LiDAR(Light Detection and Ranging) sensor for mapping and obstacle detection in autonomous vehicles has been developed. The LiDAR sensor employs laser beams to detect the positions and distances of surrounding objects. Data from the LiDAR were processed to generate 2D maps from the 3D point cloud. During this process, obstacles within the vehicle's navigable height range, as well as those that wouldn't impede its movement were identified. Using a filtering method, points outside of these obstacles were removed to create a map. In experimental studies, it was observed that the developed method can accurately detect challenging obstacles such as fences made of thin wires. Consequently, it is evident that this method holds the potential to offer more reliable and safe obstacle detection for autonomous vehicles.

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Localization Using Two Different IMU Sensor-Based Dead Reckoning System

Localization Using Two Different IMU Sensor-Based Dead Recko...

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Ibrahim Toy Akif Durdu Abdullah Yusefi Research and Development MPG Machinery Production Group Inc. Co. Konya Turkey Department of Electrical and Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Turkey

Dead reckoning estimates the current position, speed, and direction of moving objects using known position information. Localization determines an object's location on the map, categorized into human and vehicle localization. Autonomous vehicles rely on accurate vehicle localization for effective task execution. While Global Navigation Satellite System (GNSS) is a popular method, weak or absent signals can pose challenges. This study utilizes Inertial Measurement Unit (IMU) sensors for localization, integrating a second IMU to enhance accuracy. Fusing data from two IMU sensors, a dead reckoning system achieves 1.02 degrees and 1.41 meters errors in rotation and translation with a single IMU, and 1.01 degrees and 1.04 meters with two IMUs, respectively.

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Large receptive field strategy and important feature extraction strategy in 3D object detection

arXiv

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arXiv 2024年

作者： Cui, Leichao Li, Xiuxian Meng, Min Jia, Guangyu Department of Control Science and Engineering College of Electronics and Information Engineering The Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201800 China The Centre for Robotics Research Department of Engineering King’s College London Strand LondonWC2R 2LS United Kingdom

The enhancement of 3D object detection is pivotal for precise environmental perception and improved task execution capabilities in autonomous driving. LiDAR point clouds, offering accurate depth information, serve as a crucial information for this purpose. Our study focuses on key challenges in 3D target detection. To tackle the challenge of expanding the receptive field of a 3D convolutional kernel, we introduce the Dynamic Feature Fusion Module (DFFM). This module achieves adaptive expansion of the 3D convolutional kernel’s receptive field, balancing the expansion with acceptable computational loads. This innovation reduces operations, expands the receptive field, and allows the model to dynamically adjust to different object requirements. Simultaneously, we identify redundant information in 3D features. Employing the Feature Selection Module (FSM) quantitatively evaluates and eliminates non-important features, achieving the separation of output box fitting and feature extraction. This innovation enables the detector to focus on critical features, resulting in model compression, reduced computational burden, and minimized candidate frame interference. Extensive experiments confirm that both DFFM and FSM not only enhance current benchmarks, particularly in small target detection, but also accelerate network performance. Importantly, these modules exhibit effective complementarity. Copyright © 2024, The Authors. All rights reserved.

关键词： Object detection

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A Hierarchical Deep Reinforcement Learning Strategy for Collective Pursuit-Evasion Game with Partial Observations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年

作者： Wang, Xiaoxiao Yi, Peng Hong, Yiguang Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Shanghai200092 China Tongji University School of Electronics and Information Shanghai200070 China Tongji University Control Science and Engineering Department Shanghai200070 China National Key Laboratory of Autonomous Intelligent Unmanned Systems China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China

This paper addresses the problem of Collective Pursuit-Evasion Game (C-PEG) with partial observation, aiming to enhance cooperation among cluster pursuers, reduce the game space, and optimize the overall pursuer strategy efficiency in complex dynamic environments. Firstly, we consider a distributed communication model suitable for cluster collaboration and model the multi-agent cooperative task as a Collective Decentralized Partially Observable Markov Decision Process (C-Dec POMDP). Then we propose a Hierarchical Regularization Game Multi-agent Deep Deterministic Policy Gradient (HRG-MADDPG) algorithm, which includes High-Level Strategy (HLS) and Low-Level Strategy (LLS). The HLS takes the current observation from the pursuers’ sensors and the pursuers’ status as inputs, and outputs target allocation strategies for the coalitions and individuals to guide the LLS in executing specific pursuit tasks. The LLS is further divided into centralized training and decentralized execution phases. In the centralized training phase, the guiding strategy provided by the HLS and the introduced regularization auxiliary term are combined to promote decision-making through the fusion of favorable situations and auxiliary terms. In the decentralized execution phase, a reliable policy improvement method based on partially observable information is designed. All in all, the HRG-MADDPG algorithm provides a reliable method for collective pursuit-evasion. It is trained in a single environment and further validated in multiple different test environments. Simulation results confirm the effectiveness of the proposed method, showing the adaptability, scalability, and rapid responsiveness of the proposed framework in various practical applications. © 2020 IEEE.

关键词： Hierarchical systems

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Computer vision for road imaging and pothole detection:a state-of-the-art review of systems and algorithms

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Transportation Safety and Environment 2022年第4期4卷 3-18页

作者： Nachuan Ma Jiahe Fan Wenshuo Wang JinWu Yu Jiang Lihua Xie Rui Fan Department of Control Science and Engineering Frontiers Science Center for Intelligent Autonomous Systemsand State Key Laboratory of Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China Department of Civil Engineering McGill UniversityMontréalQC H3A 0C3Canada Department of Electronics and Computer Engineering the Hong Kong University of Science and TechnologyHong Kong SAR 999077P.R.China CTO Office ClearMotion Inc.BillericaMA 01821USA School of Electrical and Electronic Engineering Nanyang Technological University50 Nanyang Avenue639798Singapore

Computer vision algorithms have been utilized for 3-D road imaging and pothole detection for over two ***,there is a lack of systematic survey articles on state-of-the-art(SoTA)computer vision techniques,especially deep learningmodels,developed to tackle these *** article first introduces the sensing systems employed for 2-D and 3-D road data acquisition,including camera(s),laser scanners and Microsoft *** then comprehensively reviews the SoTA computer vision algorithms,including(1)classical 2-D image processing,(2)3-D point cloud modelling and segmentation and(3)machine/deep learning,developed for road pothole *** article also discusses the existing challenges and future development trends of computer vision-based road pothole detection approaches:classical 2-D image processing-based and 3-D point cloud modelling and segmentation-based approaches have already become history;and convolutional neural networks(CNNs)have demonstrated compelling road pothole detection results and are promising to break the bottleneck with future advances in self/un-supervised learning for multi-modal semantic *** believe that this survey can serve as practical guidance for developing the next-generation road condition assessment systems.

关键词： Computer vision road imaging pothole detection deep learning image processing point cloud modelling convolutional neural networks

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Camera/LiDAR Sensor Fusion-based Autonomous Navigation

Camera/LiDAR Sensor Fusion-based Autonomous Navigation

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International Symposium on INFOTEH-JAHORINA (INFOTEH)

作者： Abdullah Yusefi Akif Durdu Ibrahim Toy Department of Electrical and Electronics Engineering Robotics Automation Control Laboratory (RAC-LAB) Konya Technical University Konya Turkey Research and Development MPG Machinery Production Group Inc. Co. Konya Turkey

This research presents a novel approach for autonomous navigation of Unmanned Ground Vehicles (UGV) using a camera and LiDAR sensor fusion system. The proposed method is designed to achieve a high rate of obstacle detection, distance estimation, and obstacle avoidance. In order to thoroughly study the form of things and decrease the problem of object occlusion, which frequently happens in camera-based object recognition, the 3D point cloud received from the LiDAR depth sensors is used. The proposed camera and LiDAR sensor fusion design balance the benefits and drawbacks of the two sensors to produce a detection system that is more reliable than others. The UGV's autonomous navigation system is then provided with the region proposal to re-plan its route and navigate appropriately. The experiments were conducted on a UGV system with high obstacle avoidance and fully autonomous navigation capabilities. The outcomes demonstrate that the suggested technique can successfully maneuver the UGV and detect impediments in actual situations.

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Practical Applicability of PLA for Industrial Wireless Networks: Gaps and Directions

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IEEE Network 2025年

作者： Liang, Wei Wei, Fuyou Wang, Qi Zhan, Ming Wang, Ke Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control System Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shandong Normal University Jinan250014 China Taizhou University School of Electronics and Information Engineering Zhejiang Taizhou China

The rapid transformation of Industrial Cyber-Physical System (ICPS) has introduced significant cyber security challenges, particularly in the context of ICPS. Wireless networks are a critical component of these systems, but their open nature makes them vulnerable to security threats. Physical Layer Authentication (PLA) algorithms based on Channel State Information (CSI) are essential complements to existing security technologies in wireless networks. However, their practical application faces challenges such as restricted sampling rates and uneven sampling interval, which result in low-precision CSI, significant random phase offsets, and signal distortion. This study aims to explore the feasibility of deploying and applying existing representative algorithms on industrial wireless commercial devices. According to characteristics of authentication algorithms and the adopted authentication features, we selected five of the latest physical layer authentication algorithms including supervised, semi-supervised, and unsupervised algorithms and deployed them on commercial devices that comply with the WIA-FA standard. We conducted systematic experiments focusing on the motion parameters of devices, the CSI data sampling frequency, the interference caused by worker movement, the surface materials of the devices, and the types of attacks. This study explores the ability of these algorithms to resist attacks and environmental interference in commercial devices and industrial scenarios. Extensive experimental data were collected, and the results indicate that existing authentication algorithms still need improvement to meet the requirements of industrial applications. The experiment findings and the summarized application challenges of these algorithms provide significant guidance for future research and practical industrial implementations. © 1986-2012 IEEE.

关键词： Authentication

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