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IEEE International Conference on robotics and Biomimetics

作者： Huaiyu Liu Yueyuan Zhang Shuguang Liu Mengyang Zhao Lining Sun Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou China Robotics and Microsystems Center School of Mechanical and Electrical Engineering Soochow University Suzhou China

This paper introduces a UAV-based wheat rust detection system employing deep learning techniques. To address the limitations of traditional wheat rust disease detection methods, such as time and labor-intensive processes, lack of real-time monitoring, and excessive pesticide usage, In this paper, the YOLOv8 algorithm, representing the forefront of object detection methods is chosen for UAV wheat rust detection. We train the model on a prepared dataset. Subsequently, we evaluated 5 models, including YOLOv5, and YOLOv8, at two different scales (n, s) based on average precision (mAP@.5), precision, recall, and FPS. The experimental outcomes establish the superiority of the enhanced FasterNet-YOLOv8 model over YOLOv8 and YOLOv5 across all metrics, encompassing detection accuracy and processing speed. These findings affirm the viability of our suggested model within the UAV wheat rust monitoring system and underscore the efficacy of the model enhancements.

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Token-Label Alignment for Vision Transformers

arXiv

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arXiv 2022年

作者： Xiao, Han Zheng, Wenzhao Zhu, Zheng Zhou, Jie Lu, Jiwen Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China PhiGent Robotics

Data mixing strategies (e.g., CutMix) have shown the ability to greatly improve the performance of convolutional neural networks (CNNs). They mix two images as inputs for training and assign them with a mixed label with the same ratio. While they are shown effective for vision transformers (ViTs), we identify a token fluctuation phenomenon that has suppressed the potential of data mixing strategies. We empirically observe that the contributions of input tokens fluctuate as forward propagating, which might induce a different mixing ratio in the output tokens. The training target computed by the original data mixing strategy can thus be inaccurate, resulting in less effective training. To address this, we propose a token-label alignment (TL-Align) method to trace the correspondence between transformed tokens and the original tokens to maintain a label for each token. We reuse the computed attention at each layer for efficient token-label alignment, introducing only negligible additional training costs. Extensive experiments demonstrate that our method improves the performance of ViTs on image classification, semantic segmentation, objective detection, and transfer learning tasks. Code is available at: https://***/Euphoria16/TL-Align. Copyright © 2022, The Authors. All rights reserved.

关键词： Alignment

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SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving

arXiv

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arXiv 2023年

作者： Wei, Yi Zhao, Linqing Zheng, Wenzhao Zhu, Zheng Zhou, Jie Lu, Jiwen Beijing National Research Center for Information Science and Technology China Department of Automation Tsinghua University China School of Electrical and Information Engineering Tianjin University China PhiGent Robotics

3D scene understanding plays a vital role in vision-based autonomous driving. While most existing methods focus on 3D object detection, they have difficulty describing real-world objects of arbitrary shapes and infinite classes. Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy with multi-camera images. We first extract multi-scale features for each image and adopt spatial 2D-3D attention to lift them to the 3D volume space. Then we apply 3D convolutions to progressively upsample the volume features and impose supervision on multiple levels. To obtain dense occupancy prediction, we design a pipeline to generate dense occupancy ground truth without expansive occupancy annotations. Specifically, we fuse multi-frame LiDAR scans of dynamic objects and static scenes separately. Then we adopt Poisson Reconstruction to fill the holes and voxelize the mesh to get dense occupancy labels. Extensive experiments on nuScenes and SemanticKITTI datasets demonstrate the superiority of our method. Code and dataset are available at https://***/weiyithu/SurroundOcc. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Towards Efficient Path Finding and Moving Stability-Focused Trajectory Planning for Mobile Robots

Towards Efficient Path Finding and Moving Stability-Focused ...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Hou, Muyang Lei, Zhenya Hu, Haidong Wang, Zhong Wang, Yong Xie, Le Wang, Hesheng Shanghai Jiao Tong University Department of Automation Shanghai200240 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Beijing Institute of Control Engineering Beijing100190 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Department of Automation Shanghai200240 China

ISBN: (纸本)9781665481090

Nowadays, mobile robots play an important role in a variety of service scenarios. They need to plan and track trajectories to accomplish tasks such as delivery or guided tours. In such tasks, there are two remaining challenges: on one hand, the time and memory consumption of present path planners are still significant;on the other hand, service robots, which have a higher center of gravity, demand greater moving stability than that of current trajectory planners provide. To address these two challenges. Firstly, we propose a safety boundary first A∗ which fully utilizes environmental obstacle information to create a safety boundary and searches in it to quickly find an initial path. Secondly, we formulate the trajectory optimization problem as a nonlinear optimization problem, where the smoothness, safety, feasibility, and moving stability of the robot are taken into account. Furthermore, to constrain the robot's lateral acceleration, we design a time allocation algorithm based on non-uniform B-spline, enhancing the quality of the resulting trajectory. Simulations and real-world experiments demonstrate that our algorithms significantly improve path search efficiency, enhance moving stability, reduce the difficulty of tracking, and improve the quality of task completion. © 2024 IEEE.

关键词： Trajectories

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive Control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang 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 Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy Systematics Wheelchairs Estimation Predictive models Real-time systems

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MADDPG Based Radar Interference Resource Allocation Decision

MADDPG Based Radar Interference Resource Allocation Decision

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Chinese automation Congress (CAC)

作者： Sen Jiang Bo Wang Center for Intelligent Systems and Robotics School of HDU-ITMO Research Hangzhou Dianzi University Hangzhou China International Joint Research Laboratory for Autonomous Robotic Systems School of Automation Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

For the problem of jamming penetration under the one-to-many scenario with jammer, a real-time radar interference resource allocation method based on the Multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed. This method describes the interference resource allocation model as a Markov Decision Process (MDP), and designed the state space, action space and reward function for interference resource allocation problem. The jammer is divided into three heterogeneous agents, each responsible for selecting jamming targets, jamming patterns, and jamming power, respectively. These three agents coordinate to enhance the convergence ability of the high-dimensional action space algorithm. Simulation results indicate that the jamming resource allocation method designed in this paper provides better jamming effects on radar positions, higher stability, and effectively improves the operational performance of the jammer.

关键词： Markov decision processes Simulation Radar Interference Stability analysis Sensors Resource management Jamming Thermal stability Convergence

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive Control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on robotics and automation (ICRA)

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

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

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.

关键词： Trajectory tracking Computational modeling Estimation Predictive models Control systems Data models Nonlinear dynamical systems

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Generating Realistic VRU-AV Scenarios via Social Force-Based Gradient Optimization 27

Generating Realistic VRU-AV Scenarios via Social Force-Based...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Long, Tingting Zong, Mengru Zhang, Chi Liu, Yuehu Ma, Shuangxun Li, Li National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Shaanxi Xi'an China School of Electrical Engineering Xi' An Jiaotong University Shaanxi Xi'an China School of Future Transportation Changan University Xi'an710064 China BNRist Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798331505929

Validating safe and reliable interaction with vulnerable road users (VRUs) is imperative for autonomous vehicles, especially in simulation testing procedures before on-road applications. However, existing VRU agents are either with predefined routes or with collision-oriented behaviors, which limits the fidelity and diversity of scenarios. Bridging the gap between the simulated world and reality requires realistic VRU behaviors. In this paper, we apply social force to capture natural human behavior, considering how individuals are influenced by their surroundings and adhere to real-world rules. To this end, we propose HybridSFM, an improved social force model designed for characterizing human-vehicle interacting behavior. Specifically, the steering and acceleration of the VRUs are parameterized and integrated with social norms to forecast trajectories. Based on this, we further implement a gradient optimization framework for generating VRU-AV safety-critical scenarios. Firstly, VRUs are positioned randomly around the ego-agent. Differentiable simulation updates states and unrolls trajectories while backpropagation optimizes the actions of VRUs. The modified scenario is further optimized in the subsequent time step until a collision happens. The experiments demonstrate that our approach can enhance realistic safety-critical scenarios at intersection scenarios, facilitating the testing of interaction capabilities for autonomous vehicles. © 2024 IEEE.

关键词： Autonomous vehicles

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CSSL-MHTR: Continual Self-Supervised Learning for Scalable Multi-script Handwritten Text Recognition

arXiv

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arXiv 2023年

作者： Dhiaf, Marwa Souibgui, Mohamed Ali Wang, Kai Liu, Yuyang Kessentini, Yousri Fornés, Alicia Rouhou, Ahmed Cheikh InstaDeep United Kingdom Computer Vision Center UAB Spain Digital Research Center of Sfax SM@RTS Tunisia State Key Laboratory of Robotics China Shenyang Institute of Automation Chinese Academy of Sciences China

Self-supervised learning has recently emerged as a strong alternative in document analysis. These approaches are now capable of learning high-quality image representations and overcoming the limitations of supervised methods, which require a large amount of labeled data. However, these methods are unable to capture new knowledge in an incremental fashion, i.e. when data is presented to the model sequentially, which is closer to the realistic scenario. In this paper, we explore the potential of continual self-supervised learning to alleviate the catastrophic forgetting problem in handwritten text recognition, as an example of sequence recognition. Our method consists in adding intermediate layers called adapters for each task, and efficiently distilling knowledge from the previous model while learning the current task. Our proposed framework is efficient in both computation and memory complexity. To demonstrate its effectiveness, we evaluate our method by transferring the learned model to diverse text recognition downstream tasks, including Latin and non-Latin scripts. As far as we know, this is the first application of continual self-supervised learning for handwritten text recognition. We attain state-of-the-art performance in English, Italian, and Russian, whilst adding only a few parameters per task. The code and trained models will be publicly available1 © 2023, CC BY.

关键词： Supervised learning

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UAV Path Planning Based on Simultaneous Optimization of Monitoring Frequency and Security

UAV Path Planning Based on Simultaneous Optimization of Moni...

引用

Chinese Control and Decision Conference, CCDC

作者： Yifei Shu Yang Chen Mian Hu Huaiyu Wu Xingang Zhao Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang

ISBN: (纸本)9781665478977

UAVs have great potential when applied to persistent monitoring, but there are still problems such as difficulty in ensuring the monitoring frequency and easy leakage of monitoring path information. Therefore, it is necessary to increase the UAV monitoring frequency of targets and the randomness of monitoring paths as much as possible on the premise of covering all monitoring tasks. In response to the above problems, this paper studies the UAV path planning problem of simultaneous optimization of monitoring frequency and path security. Through the evaluation of monitoring frequency and path security based on monitoring overdue time and path entropy, a mathematical model for UAV path planning is established. An improved ant colony algorithm based on the monitoring overdue time (Overdue-aware Ant Colony Optimization, OACO) is designed, and finally the UAV flight trajectory with high monitoring frequency and the high monitoring path security is obtained. The simulation results show that the method proposed in this paper can effectively improve the monitoring frequency of each monitoring node to be accessed, improve the security of the UAV monitoring path, which is of great significance for enhancing monitoring security and preventing intrusion.

关键词： Time-frequency analysis Simulation Mathematical models Entropy Trajectory Safety Security

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