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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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A Lightweight YOLOv5 Model Integrating GhostNet and Attention Mechanism

A Lightweight YOLOv5 Model Integrating GhostNet and Attentio...

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Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Xinke Dou Ting Wang Shiliang Shao School of Information Engineering Shenyang University of Chemical Technology Shenyang China State Key Laboratory of Robotics Chinese Academy of Sciences Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China

Aiming at the problems of complex structure of neural network, large number of parameters and difficult deployment to terminal devices, this paper proposes an improved lightweight object detection model for the purpose of building a model with high classification accuracy and low complexity. First, the one-stage detector yolov5 is used as the base network. Considering the excessive redundant features of feature extraction, Ghost module is used to replace the traditional convolution, effectively allocate computing resources, to reduce the size of the model and improve the speed of model detection. When designing model bottleneck structure, Normalization-based Attention Module (NAM) is added to improve training efficiency and reduce model complexity. Experimental results show that compared with YOLOv5, the calculation speed of the proposed model is increased by 17% and the accuracy is reduced by less than 2%, which is of great significance in the environment of low-performance computing equipment.

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Multi-UAVs Cooperative Pollution Source Localization Based on Improved Particle Swarm Optimization Algorithm

Multi-UAVs Cooperative Pollution Source Localization Based o...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Meiyi Du Zhijun Gao Liying Yang Han Jiang Zhaoxiong Huang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

Accidents involving hazardous gas leakage from chemical plants occur frequently, highlighting the critical importance of accurately locating air pollution sources. Rapid localization of pollution sources through the collaborative efforts of multiple unmanned aerial vehicles (UAVs) can help prevent major disasters. To address this, this paper presents an air pollution source tracking algorithm based on an improved particle swarm optimization (IPSO) algorithm. By enhancing the inertia weight coefficient, the search efficiency and accuracy of the UAV swarm are significantly improved. The simulation environment considers a Gaussian plume model, which incorporates various atmospheric constraints such as temperature and wind speed, for UAV swarm navigation. The simulation results demonstrate that employing the particle swarm optimization algorithm to guide UAVs in locating pollution sources enables them to accurately identify the sources within a short period, exhibiting a high level of effectiveness and robustness.

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Encoding-Based Fusion Estimation for Multi-Sensor Systems: Applications to Target Tracking

Encoding-Based Fusion Estimation for Multi-Sensor Systems: A...

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Youth Academic Annual Conference of Chinese Association of automation (YAC)

作者： Kaiqun Zhu Qirong Tang Pengcheng Zhang State Key Laboratory of Internet of Things for Smart City University of Macau Macau China Laboratory of Robotics and Multibody System Tongji University Shanghai China School of Mechatronic Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350379228

ISBN: (纸本)9798350390780

This paper is concerned with the robust set-membership fusion estimation problem for multi-sensor systems under the influence of uncertain parameters and unknown-but-bounded noises. The sensor nodes and the remote fusion estimator are connected through bandwidth-constrained communication networks. To address this challenge, a dynamic encoding-based data compression mechanism is introduced, enabling the data to be efficiently encoded using a reduced number of bits. By thoroughly analyzing the impact of uncertain parameters, unknown-but-bounded noises, and data distortions on estimation performance, sufficient conditions are derived to ensure that the estimation error remains within the bounded ellipsoidal set. Moreover, the weight parameters are calculated by solving constrained optimization problems. Finally, the effectiveness of the proposed estimation algorithm is verified through a maneuvering target tracking scenario.

关键词： Sufficient conditions Estimation error Target tracking Noise Data compression Set theory Distortion

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Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach

Quadruped Guidance Robot for the Visually Impaired: A Comfor...

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IEEE International Conference on robotics and automation (ICRA)

作者： Yanbo Chen Zhengzhe Xu Zhuozhu Jian Gengpan Tang Liyunong Yang Anxing Xiao Xueqian Wang Bin Liang School of Mechanical Engineering and Automation at Harbin Institute of Technology Shenzhen China Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen China School of Computing National University of Singapore Singapore Singapore

Guidance robots that can guide people and avoid various obstacles, could potentially be owned by more visually impaired people at a fairly low cost. Most of the previous guidance robots for the visually impaired ignored the human response behavior and comfort, treating the human as an appendage dragged by the robot, which can lead to imprecise guidance of the human and sudden changes in the traction force experienced by the human. In this paper, we propose a novel quadruped guidance robot system with a comfort-based concept. We design a controllable traction device that can adjust the length and force between human and robot to ensure comfort. To allow the human to be guided safely and comfortably to the target position in complex environments, our proposed human motion planner can plan the traction force with the force-based human motion model. To track the planned force, we also propose a robot motion planner that can generate the specific robot motion command and design the force control device. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios. (Video 1 1 Video demonstration: https://***/gd-RcYOqGuo.)

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Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows

Spatio-Temporal Communication Compression in Distributed Pri...

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IEEE Conference on Decision and Control

作者： Zihao Ren Lei Wang Deming Yuan Hongye Su Guodong Shi College of Control Science and Engineering Zhejiang University P.R. China School of Automation Nanjing University of Science and Technology P.R. China Australia Centre for Field Robotics University of Sydney Australia

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we study distributed prime-dual flows for multi-agent optimization with spatio-temporal compressions. The central aim of multi-agent optimization is for a network of agents to collaboratively solve a system-level optimization problem with local objective functions and node-to-node communication by distributed algorithms. The scalability of such algorithms crucially depends on the complexity of the communication messages, and a number of communication compressors for distributed optimization have recently been proposed in the literature. First of all, we introduce a general spatio-temporal compressor characterized by the stability of the resulting dynamical system along the vector field of the compressor. We show that several important distributed optimization compressors such as the greedy sparsifier, the uniform quantizer, and the scalarizer all fall into the category of this spatio-temporal compressor. Next, we propose two distributed prime-dual flows with the spatio-temporal compressors being applied to local node states and local error states, respectively, and prove (exponential) convergence of the node trajectories to the global optimizer for (strongly) convex cost functions. Finally, a few numerical examples are present to illustrate our theoretical results.

关键词： Scalability Heuristic algorithms Linear programming Compressors Vectors Trajectory Dynamical systems Distributed algorithms Optimization Numerical stability

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Stiffness performance optimization method of drilling robot based on QPSO algorithm

Stiffness performance optimization method of drilling robot ...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Yufan Zhang Yong Tao Hongxing Wei Haitao Liu Jiahao Wan Ruijun Guo School of Large Aircraft Engineering Beihang University Beijing China Research Institute of Aero-Engine Beihang University Beijing China School of Mechanical Engineering and Automation Beihang University Beijing China Beijing C.H.L. Robotics Co. Ltd. Beijing China

ISBN: (数字)9798331506100

ISBN: (纸本)9798331506117

Due to its high flexibility and intelligence, industrial robots are being increasingly used in aeronautical processing tasks. However, the inherently low stiffness of robots seriously affects the processing quality of their operations. Studying its stiffness characteristics and optimization methods is an effective method to improve the stiffness performance of robot. This paper proposes a robot posture optimization method based on quantum particle swarm algorithm to solve the problem of vibration during drilling. Firstly, the kinematic model and static stiffness model of the robot are established, and then a stiffness index oriented to the machining plane is proposed. Next, a new performance evaluation index is obtained by normalization fusion with manipulability to quantitatively evaluate the stiffness performance of the robot. After this, the drilling posture is optimized by quantum particle swarm optimization algorithm under the boundary constraints and property constraints of the robot. Finally, drilling experiments were carried out on AUBO i5 robot to verify the accuracy of the performance evaluation coefficient and optimization method. Compared with non-optimization and particle swarm optimization, the robot performance was improved by 32.28% and 8.57% on average, indicating that this method can effectively improve the robot stiffness performance.

关键词： Drilling Performance evaluation Vibrations Service robots Optimization methods Industrial robots Rigidity

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Edge Detectors Can Make Deep Convolutional Neural Networks More Robust

arXiv

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arXiv 2024年

作者： Ding, Jin Zhao, Jie-Chao Sun, Yong-Zhi Tan, Ping Wang, Jia-Wei Ma, Ji-En Fang, You-Tong School of Automation and Electrical Engineering Key Institute of Robotics of Zhejiang Province Zhejiang University of Science and Technology Hangzhou310023 China State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang University Hangzhou310027 China School of Electrical Engineering Zhejiang University Hangzhou310027 China

Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN’s robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Inspired by the principal way that human eyes recognize objects, i.e., largely relying on the shape features, this paper first employs the edge detectors as layer kernels and designs a binary edge feature branch (BEFB for short) to learn the binary edge features, which can be easily integrated into any popular backbone. The four edge detectors can learn the horizontal, vertical, positive diagonal, and negative diagonal edge features, respectively, and the branch is stacked by multiple Sobel layers (using edge detectors as kernels) and one threshold layer. The binary edge features learned by the branch, concatenated with the texture features learned by the backbone, are fed into the fully connected layers for classification. We integrate the proposed branch into VGG16 and ResNet34, respectively, and conduct experiments on multiple datasets. Experimental results demonstrate the BEFB is lightweight and has no side effects on training. And the accuracy of the BEFB integrated models is better than the original ones on all datasets when facing FGSM, PGD, and C&W attacks. Besides, BEFB integrated models equipped with the robustness enhancing techniques can achieve better classification accuracy compared to the original models. The work in this paper for the first time shows it is feasible to enhance the robustness of DCNNs through combining both shape-like features and texture features. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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Backstepping-Based Adaptive Cooperative Control For Multiple Euler-Lagrange Systems Under Unknown Harmonic Disturbances

Backstepping-Based Adaptive Cooperative Control For Multiple...

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Chinese automation Congress (CAC)

作者： Ying Chen Zi-Yi Zhao Peng Sun Xiang-Gui Guo Jian-Liang Wang Daniel Coutinho Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Collaborative Flying Robotics Hangzhou Innovation Institute of Beihang University HangZhou China Department of Automation and Systems Universidade Federal de Santa Catarina Florianópolis Brazil

This paper studies cooperative control problem for multiple Euler-Lagrange systems with unknown harmonic disturbances, where the disturbances are the superposition of sinusoidal components with unknown amplitude, phase, and frequency. A novel adaptive disturbance observer without requiring the disturbance frequency information is designed to estimate the disturbances. Over the directed network topology, a cooperative control scheme is proposed based on backstepping techniques, so that the tracking error is ultimately uniformly bounded. The scheme developed can be used to reject the harmonic disturbances in a uniform way, which means the characteristics of the disturbances can be described as a fixed parameter vector. Finally, an application is presented to demonstrate the effectiveness of the proposed control strategy in the context of a two-link manipulator system.

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RoboFold: A platform for Protein-folding Inspired Robot Swarms *

RoboFold: A platform for Protein-folding Inspired Robot Swar...

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WRC Symposium on Advanced robotics and automation (WRC SARA)

作者： Feng Zhang Yuping Gu Shuai Yuan Yongliang Yang School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Liaoning China School of Computer Science and Engineering and School of Electrical and Control Engineering Shenyang Jianzhu University Shenyang Liaoning China

Natural swarms have inspired various controlling algorithms for swarm robotics, while few of them were programmed in a similar way as the natural swarms. Defining a reliable programming method is still a daunting challenge in swarm robotics. In nature, the information of 3D protein structure is programmed in the sequence of amino acids of the protein. To mimic the programming method of protein folding, we present a platform for investigating protein-folding inspired programming method for robot swarms. This platform includes three parts: a vibration machine, protein-like robot sequences, and recording devices. The preliminary results presented here demonstrated the effectiveness of this platform and the feasibility of protein-folding inspired programming method for robot swarms.

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