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Evaluation of surface integrity for ultra-precision machined fused silica surface based on atomic point-defect density

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Journal of Manufacturing Processes 2025年 149卷 917-930页

作者： Dinghuai Yang Linjie Zhao Jian Cheng Qi Liu Zixiao Zhang Hongqin Lei Zican Yang Henan Liu Jinghe Wang Yazhou Sun Mingjun Chen State Key Laboratory of Robotics and System School of Mechatronics Engineering Harbin Institute of Technology Harbin 150001 China Centre for Precision Manufacturing DMEM University of Strathclyde Glasgow G1 1XJ UK Center for Precision Engineering School of Mechatronics Engineering Harbin Institute of Technology Harbin 150001 China

The degree of plasticity and brittleness (DPB) of the material significantly affects the service performance of fused silica manufacturing surfaces in many frontier fields. However, there is still no effective method to quantitatively and non-destructively characterize their DPB, severely restricting the application of fused silica. Herein, atomic manufacturing-induced point defects on fused silica manufacturing surfaces were determined. Based on this, the evolution laws of various types of point defects with the DPB were discussed. Interestingly, it was found that the DPB is closely related to the densities of specific ODCII point defects, whose proportion ( k ) was chosen as the characteristic parameter to judge surface brittle fractures. Additionally, it was discovered that k sharply increased at the material brittle-plastic transition boundary, which was also verified based on the molecular dynamics simulation. Accordingly, whether k is >15 % was proposed as a criterion to judge whether the material has undergone brittle fractures during manufacturing processes. Furthermore, the significant increment of ODCII point defects was found to be the main cause of the obvious increment of k . Moreover, two causes of the significant increment of ODCII point defects were determined. Finally, the effectiveness of the judge criterion in six specific manufacturing cases was verified. Summarily, the study proposes a powerful nondestructive approach to judge the brittle fractures. It could quantitatively and non-destructively characterize the DPB during manufacturing processes. It is also applicable to the quantitative DPB characterization and the early warning detection of fracture breakdown for other brittle silicate materials.

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Combined studies of surface evolution and crack healing for the suppression of negative factors during CO_(2) laser repairing of fused silica

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Chinese Optics Letters 2021年第4期19卷 44-49页

作者： Chao Tan Linjie Zhao Mingjun Chen Jian Cheng Zhaoyang Yin Qi Liu Hao Yang Wei Liao State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China Research Center of Laser Fusion China Academy of Engineering PhysicsMianyang 621900China

In order to reveal the evolution mechanism of repaired morphology and the material's migration mechanism on the crack surface in the process of CO_(2) laser repairing surface damage of fused silica optics, two multi-physics coupling mathematical models with different scales are developed, respectively. The physical problems, such as heat and mass transfer,material phase transition, melt flow, evaporation removal, and crack healing, are analyzed. Studies show that material ablation and the gasification recoil pressure accompanying the material splash are the leading factors in forming the Gaussian crater with a raised rim feature. The use of low-power lasers for a long time can fully melt the material around the crack before healing, which can greatly reduce the size of the residual air layer. Combined with the experimental research, the methods to suppress the negative factors(e.g., raised rim, deposited debris, air bubbles) in the CO_(2) laser repairing process are proposed.

关键词： CO_(2)laser repairing fused silica crack healing surface evolution

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Tightly-coupled lidar-inertial localization with visual landmarks in operational scenarios

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Industrial Robot 2025年

作者： Wang, Gongcheng Cao, Zexin Lv, Xin Wang, Han Wang, Chong Ding, Pengchao Wang, Weidong Key State Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Purpose: Simultaneous localization and mapping (SLAM) is widely used in autonomous robotics. Although LiDAR and vision-based methods have been widely deployed in large-scale environments, their submeter accuracy is insufficient for operational scenarios. This paper aims to present a tightly integrated SLAM framework combining point clouds, visual landmarks and IMU data to enhance localization accuracy. Design/methodology/approach: A novel method to resolve planar marker pose ambiguity is proposed. Edge constraints between the point cloud and marker correct marker’ pose and distortions. A tightly coupled coarse-to-fine optimization approach integrates point cloud features and marker corners during tracking and localization. Pose constraints are added to the objective function to improve accuracy, and bundle adjustment is applied to key frame and marker poses in the common view to prevent registration errors. Findings: The proposed method is evaluated in real-world environments. Experimental results demonstrate that it achieves superior localization and mapping accuracy compared to existing methods, thus validating the effectiveness of the framework. Originality/value: This paper introduces a tightly integrated framework that combines point clouds, visual landmarks and IMU measurements to improve small-scale localization accuracy in operational scenario. © 2025, Emerald Publishing Limited.

关键词： SLAM robotics

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CVFormer: Learning Circum-View Representation and Consistency for Vision-Based Occupancy Prediction via Transformers

CVFormer: Learning Circum-View Representation and Consistenc...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhengqi Bai Wenjun Shi Dongchen Zhu Hanlong Kang Guanghui Zhang Gang Ye Yang Xiao Lei Wang Xiaolin Zhang Bo Li Jiamao Li Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Lotus Robotics Ltd. Hangzhou China ShanghaiTech University Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

With the increasing demands for perception accuracy in autonomous driving, there is a growing focus on fine-grained 3D semantic occupancy prediction. Effectively representing detailed three-dimensional scenes has become a significant challenge in the development of this task. In this paper, we present a novel transformer-based framework named CVFormer, which leverages two-dimensional circum-views from the ego to excavate three-dimensional features of the surrounding environment. Circum-views provide a novel solution for effectively addressing the representation of dense and fine-grained scenes. Specifically, a multi-attention module CTMA is designed for fusing temporal features from circum-views to fully exploit the spatiotemporal correlations between frames and capture more comprehensive clues. Furthermore, a novel 2D projection constraint is established by observing objects from different perspective directions, and multiple 3D constraints based on object invariance and semantic consistency are also conducted for supervising the network, which enhances its performance of understanding the scene. Experimental results on nuScenes dataset demonstrate that the proposed CVFormer obviously outperforms existing methods for occupancy prediction.

关键词： Training Solid modeling Three-dimensional displays Correlation Semantics Transformers Robot sensing systems

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Mixed Motivation Driven Social Multi-Agent Reinforcement Learning for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2025年第6期12卷 1272-1282页

作者： Long Chen Peng Deng Lingxi Li Xuemin Hu State Key Laboratory of Multimodal Artificial Intelligence Systems and the State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing WAYTOUS Inc. Beijing Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an China School of Computer Science and Information Engineering Hubei University Wuhan China Purdue School of Engineering and Technology Indiana University-Purdue University Indianapolis Indianapolis IN USA School of Artificial Intelligence Hubei University Wuhan Key Laboratory of Intelligent Sensing System and Security (Hubei University) Ministry of Education Wuhan China

Despite great achievement has been made in autonomous driving technologies, autonomous vehicles (AVs) still exhibit limitations in intelligence and lack social coordination, which is primarily attributed to their reliance on single-agent technologies, neglecting inter-AV interactions. Current research on multi-agent autonomous driving (MAAD) predominantly focuses on either distributed individual learning or centralized cooperative learning, ignoring the mixed-motive nature of MAAD systems, where each agent is not only self-interested in reaching its own destination but also needs to coordinate with other traffic participants to enhance efficiency and safety. Inspired by the mixed motivation of human driving behavior and their learning process, we propose a novel mixed motivation driven social multi-agent reinforcement learning method for autonomous driving. In our method, a multi-agent reinforcement learning (MARL) algorithm, called Social Learning Policy Optimization (SoLPO), which takes advantage of both the individual and social learning paradigms, is proposed to empower agents to rapidly acquire self-interested policies and effectively learn socially coordinated behavior. Based on the proposed SoLPO, we further develop a mixed-motive MARL method for autonomous driving combined with a social reward integration module that can model the mixed-motive nature of MAAD systems by integrating individual and neighbor rewards into a social learning objective for improved learning speed and effectiveness. Experiments conducted on the MetaDrive simulator show that our proposed method outperforms existing state-of-the-art MARL approaches in metrics including the success rate, safety, and efficiency. More-over, the AVs trained by our method form coordinated social norms and exhibit human-like driving behavior, demonstrating a high degree of social coordination.

关键词： Measurement Reinforcement learning Safety Driver behavior Autonomous vehicles Optimization

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Corrigendum to “Development of a miniature piezoelectric robot combining three unconventional inertial impact modes” [Sens. Actuators A Phys. 365 (2024) 114898]

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Sensors and Actuators A: Physical 2024年 367卷

作者： Jing Li Boliang Xu Jie Deng Weishan Chen Yingxiang Liu State Key Laboratory of Robotics and System Harbin Institute of Technology China

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Multisensory Measurement Synchronization and Industrial Robot Trajectory Error Compensation Based on Laser Tracker and Photoelectric Switch

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Chengzhi Wang Sikai Zhao Tian Xu Tianjiao Zheng Shize Zhao Ziyuan Yang Hegao Cai Jie Zhao Yanhe Zhu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

This article presents a multisensory measurement synchronization approach that combines a laser tracker and multiple photoelectric switches, which easily embed laser tracker into robotic machining systems to achieve high-accuracy error compensation and trajectory tracking. Previous composite error compensation systems rely on limited interfaces provided by equipment producers and require careful temporal logic arrangement, making the whole system complicated and task-specific. The proposed method utilizes portable photoelectric switches that work as an intermediary between the robot system and the laser tracker system to match the corresponding data from both sides. The switches could be mounted anywhere along the robot task trajectories, and a critic trigger adjustment is designed to effectively determine the trigger position. Various types of trajectory are executed and measured to validate the effectiveness and robustness of the proposed method, and subsequent error compensation experiments demonstrate a significant improvement in robot accuracy.

关键词： Robots Trajectory Robot kinematics Measurement by laser beam Measurement uncertainty Service robots Error compensation Synchronization Accuracy Switches

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Imitation of Manipulation Skills Using Multiple Geometries

arXiv

引用

arXiv 2022年

作者： Ti, Boyang Gao, Yongsheng Zhao, Jie Calinon, Sylvain State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Idiap Research Institute MartignyCH-1920 Switzerland

Daily manipulation tasks are characterized by geometric primitives related to actions and object shapes. Such geometric descriptors are poorly represented by only using Cartesian coordinate systems. In this paper, we propose a learning approach to extract the optimal representation from a dictionary of coordinate systems to encode an observed movement/behavior. This is achieved by using an extension of Gaussian distributions on Riemannian manifolds, which is used to analyse a set of user demonstrations statistically, by considering multiple geometries as candidate representations of the task. We formulate the reproduction problem as a general optimal control problem based on an iterative linear quadratic regulator (iLQR), where the Gaussian distribution in the extracted coordinate systems are used to define the cost function. We apply our approach to object grasping and box opening tasks in simulation and on a 7-axis Franka Emika robot. The results show that the robot can exploit several geometries to execute the manipulation task and generalize it to new situations, by maintaining the invariant characteristics of the task in the coordinate system(s) of interest. Copyright © 2022, The Authors. All rights reserved.

关键词： Geometry

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PillarNet: Real-Time and High-Performance Pillar-based 3D Object Detection

arXiv

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arXiv 2022年

作者： Shi, Guangsheng Li, Ruifeng Ma, Chao State Key Laboratory of Robotics and System Harbin Institute of Technology China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China

Real-time and high-performance 3D object detection is of critical importance for autonomous driving. Recent top-performing 3D object detectors mainly rely on point-based or 3D voxel-based convolutions, which are both computationally inefficient for onboard deployment. In contrast, pillar-based methods use solely 2D convolutions, which consume less computation resources, but they lag far behind their voxel-based counterparts in detection accuracy. In this paper, by examining the primary performance gap between pillar- and voxel-based detectors, we develop a real-time and high-performance pillar-based detector, dubbed PillarNet. The proposed PillarNet consists of a powerful encoder network for effective pillar feature learning, a neck network for spatial-semantic feature fusion and the commonly used detect head. Using only 2D convolutions, PillarNet is flexible to an optional pillar size and compatible with classical 2D CNN backbones, such as VGGNet and ResNet. Additionally, PillarNet benefits from our designed orientation-decoupled IoU regression loss along with the IoU-aware prediction branch. Extensive experimental results on the large-scale nuScenes Dataset and Waymo Open Dataset demonstrate that the proposed PillarNet performs well over state-of-the-art 3D detectors in terms of effectiveness and efficiency. Code is available at https://***/agent-sgs/PillarNet. © 2022, CC BY-SA.

关键词： Object detection

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PI2-CMA Based Trajectory Planning Method for the Tractor-Trailer Vehicle

PI2-CMA Based Trajectory Planning Method for the Tractor-Tra...

引用

Pattern Recognition and Artificial Intelligence (PRAI), International Conference on

作者： You Wang Zehao Wang Jingchuan Wang Rui Guo Peng Xiao Institute of Medical Robotics and Department of Automation Ministry of Education Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Shanghai China State Grid Intelligence Technology CO. LTD

The motion primitive-based state lattice trajectory planning method has been widely used in the field of trajectory planning. However, due to the limitations of the search space and resolution of motion primitives, and the lack of consideration of scenario information during offline generation of motion primitives, the tractor-trailer vehicles can easily fall into "jackknife phenomenon" when making large pose adjustments in narrow scenarios, which brings dilemma for trajectory planning. In this paper, a trajectory planning algorithm is proposed based on a general model of tractor-trailer vehicle, which combines global planning and local optimization. The method first generates a series of motion primitives, including forward and backward driving conditions, and then an A ∗ based global state lattice planner is used to obtain a planning result, which is imported as the initial solution into the local trajectory optimizer based on PI 2 − CMA (Path Integral Policy Improvement with Covariance Matrix Adaptation) algorithm and dynamic potential method. Experiments show that this method significantly improves the performance in the obstacle avoidance, "jackknife phenomenon" avoidance and curvature compared to traditional motion primitive-based state lattice planning methods.

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