检索结果-内蒙古大学图书馆

Enhanced Decentralized Autonomous Aerial Robot Teams with Group Planning

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Hou, Jialiang Zhou, Xin Gan, Zhongxue Gao, Fei Academy for Engineering and Technology Fudan University Shanghai200433 China State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China

Designing autonomous aerial robot team systems remains a grand challenge in robotics. Existing works in this field can be categorized as centralized and decentralized. Centralized methods suffer from scale dilemmas, while decentralized ones often lead to poor planning quality. In this paper, we propose an enhanced decentralized autonomous aerial robot team system with group planning. According to the spatial distribution of agents, the system dynamically divides the team into several groups and isolated agents. For conflicts within each group, we propose a novel coordination mechanism named group planning. The group planning consists of efficient multi-agent pathfinding (MAPF) and trajectory joint optimization, which can significantly improve planning quality and success rate. We demonstrate through simulations and real-world experiments that our method not only has applicability for a large-scale team but also has top-level planning quality. © 2022, CC BY-NC-ND.

关键词： Motion planning

A Machine Learning Based Risk Identification Method for Illegal Pig Slaughtering

学校读者我要写书评

暂无评论

A Machine Learning Based Risk Identification Method for Ille...

2023 International Conference on Intelligent Communication and Networking, ICN 2023

作者： Miao, Jiajun Shen, Yujun Zhang, Yan Liu, Lingjie Tian, Ye Zhao, Yu Zhou, Chunyan Harbin Engineering University College of Computer Science and Technology Harbin China Hangzhou Hikvision Digital Technology Co. Ltd Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China Environment and Food and Drug Crime Investigation Detachment of Hangzhou Public Security Bureau Hangzhou China Xidian University Hangzhou Institute of Technology Harbin China Key Laboratory of Peace-Building Big Data of Zhejiang Province Hangzhou China

ISBN: (纸本)9798350357677

With the impact of African swine fever, the survival rate of domestic pigs has sharply decreased, and pork prices continue to rise. Many unscrupulous elements obtain illegal profits by avoiding inspection and quarantine, processing sick and dead pigs, and injecting water, etc. Unquarantined meat products pose serious food safety risks. In this context, we propose a machine learning based risk identification method for illegal slaughter of live pigs. Firstly, we use the RetinaNet algorithm to detect live pig transport vehicles and use a dual residual network to Super-Resolution the vehicle area in the detected image to improve facial clarity. Subsequently, the ArcFace algorithm is used to match the facial information with the ex-convicts, and finally, the Location of vehicles passing through, time, frequency and facial analysis results are combined for calculation and scoring and provide the risk warning for illegal slaughter of live pigs. © 2023 IEEE.

关键词： Risk assessment

Adaptive Tracking and Perching for Quadrotor in Dynamic Scenarios

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Gao, Yuman Ji, Jialin Wang, Qianhao Jin, Rui Lin, Yi Shang, Zhimeng Cao, Yanjun Shen, Shaojie Xu, Chao Gao, Fei Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310027 China The Huzhou Institute Zhejiang University Huzhou313000 China DJI Technology Co. Shenzhen510810 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong

Perching on the moving platforms is a promising solution to enhance the endurance and operational range of quadrotors, which could benefit the efficiency of a variety of air-ground cooperative tasks. To ensure robust perching, tracking with a steady relative state and reliable perception is a prerequisite. This paper presents an adaptive dynamic tracking and perching scheme for autonomous quadrotors to achieve tight integration with moving platforms. For reliable perception of dynamic targets, we introduce elastic visibility-aware planning to actively avoid occlusion and target loss. Additionally, we propose a flexible terminal adjustment method that adapts the changes in flight duration and the coupled terminal states, ensuring full-state synchronization with the time-varying perching surface at various angles. A relaxation strategy is developed by optimizing the tangential relative speed to address the dynamics and safety violations brought by hard boundary conditions. Moreover, we take SE(3) motion planning into account to ensure no collision between the quadrotor and the platform until the contact moment. Furthermore, we propose an efficient spatiotemporal trajectory optimization framework considering full state dynamics for tracking and perching. The proposed method is extensively tested through benchmark comparisons and ablation studies. To facilitate the application of academic research to industry and to validate the efficiency of our scheme under strictly limited computational resources, we deploy our system on a commercial drone (DJI-MAVIC3) with a full-size sport-utility vehicle (SUV). We conduct extensive real-world experiments, where the drone successfully tracks and perches at 30 km/h (8.3 m/s) on the top of the SUV, and at 3.5 m/s with 60° inclined into the trunk of the SUV. © 2023, CC BY-NC-ND.

关键词： Motion planning

Intention-Aware Planner for Robust and Safe Aerial Tracking

学校读者我要写书评

暂无评论

Intention-Aware Planner for Robust and Safe Aerial Tracking

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Qiuyu Ren Huan Yu Jiajun Dai Zhi Zheng Jun Meng Li Xu Chao Xu Fei Gao Yanjun Cao The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China The Robotics Institute of Zhejiang University Yuyao China Huzhou Institute Zhejiang University Huzhou China Huzhou Laboratory of Autonomous Intelligent Systems Huzhou China College of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous target tracking with quadrotors has wide applications in many scenarios, such as cinematographic follow-up shooting or suspect chasing. Target motion prediction is necessary when designing the tracking planner. However, the widely used constant velocity or constant rotation assumption can not fully capture the dynamics of the target. The tracker may fail when the target happens to move aggressively, such as sudden turn or deceleration. In this paper, we propose an intention-aware planner by additionally considering the intention of the target to enhance safety and robustness in aerial tracking applications. Firstly, a designated intention prediction method is proposed, which combines a user-defined potential assessment function and a state observation function. A reachable region is generated to speci cally evaluate the turning intentions. Then we design an intention-driven hybrid A* method to predict the future possible positions for the target. Finally, an intention-aware optimization approach is designed to generate a spatial-temporal optimal trajectory, allowing the tracker to perceive unexpected situations from the target. Benchmark comparisons and real-world experiments are conducted to validate the performance of our method.

关键词： Target tracking Semantics Prediction methods Turning Prediction algorithms Robustness Safety Planning Trajectory optimization Quadrotors

Protection of Access Pattern 23

学校读者我要写书评

暂无评论

Protection of Access Pattern

Proceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence

作者： Biao Gao Shijie Jia Peng Yin Xueying Zhang State Key Laboratory of Information Security Institute of Information Engineering CAS China and School of Cyber Security University of Chinese Academy of Sciences China Defence Industry Secrecy Examination and Certification Center China and School of Cyber Security University of Chinese Academy of Sciences China China Industrial Control Systems Cyber Emergency Response Team China

ISBN: (纸本)9798400708688

Encryption is insufficient to ensure the system security because the access patterns of user will still reflect the information about the data and serve as an indicator for adversary to infer the sensitive information. We conclude the related work on the access pattern protection to provide a thorough literature review of history independence and oblivious random access machine, which address the issues of static and dynamic access pattern observations respectively.

关键词： History independence access pattern protection oblivious random access machine secure deletion

Technical report:PID design of second-order non-linear uncertain systems with fractional order operations

学校读者我要写书评

暂无评论

IET cyber-systems and Robotics 2021年第4期3卷 343-346页

作者： Song Chen Tehuan Chen Chao Xu Jian Chu School of Mathematical Sciences Zhejiang UniversityHangzhouZhejiangChina School of Mechanical Engineering and Mechanics Ningbo UniversityNingboZhejiangChina Department of Automation Shanghai Jiao Tong UniversityShanghaiChina Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingboZhejiangChina The State Key Laboratory of Industrial Control Technology Institute of Cyber‐Systems and ControlZhejiang UniversityHangzhouZhejiangChina

This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers(PI^(λ)D^(μ)).The stability result of PIλDμ-based control systems is provided,such that the closed-loop systems can accomplish global stabilisation under some suitable conditions related to the system ***,a simulation is demonstrated to verify the results.

关键词： fractional operations verify

One RING to Rule Them All: Radon Sinogram for Place Recognition, Orientation and Translation Estimation

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Lu, Sha Xu, Xuecheng Yin, Huan Chen, Zexi Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control and Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou310058 China Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Hong Kong

LiDAR-based global localization is a fundamental problem for mobile robots. It consists of two stages, place recognition and pose estimation, which yields the current orientation and translation, using only the current scan as query and a database of map scans. Inspired by the definition of a recognized place, we consider that a good global localization solution should keep the pose estimation accuracy with a lower place density. Following this idea, we propose a novel framework towards sparse place-based global localization, which utilizes a unified and learning-free representation, Radon sinogram (RING), for all sub-tasks. Based on the theoretical derivation, a translation invariant descriptor and an orientation invariant metric are proposed for place recognition, achieving certifiable robustness against arbitrary orientation and large translation between query and map scan. In addition, we also utilize the property of RING to propose a global convergent solver for both orientation and translation estimation, arriving at global localization. Evaluation of the proposed RING based framework validates the feasibility and demonstrates a superior performance even under a lower place density. Copyright © 2022, The Authors. All rights reserved.

关键词： Radon

I2EKF-LO: A Dual-Iteration Extended Kalman Filter Based LiDAR Odometry

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Yu, Wenlu Xu, Jie Zhao, Chengwei Zhao, Lijun Nguyen, Thien-Minh Yuan, Shenghai Bai, Mingming Xie, Lihua State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China The School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Hangzhou Guochen Robot Technology Company Limited Hangzhou311200 China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310000 China

LiDAR odometry is a pivotal technology in the fields of autonomous driving and autonomous mobile robotics. However, most of the current works focus on nonlinear optimization methods, and still existing many challenges in using the traditional Iterative Extended Kalman Filter (IEKF) framework to tackle the problem: IEKF only iterates over the observation equation, relying on a rough estimate of the initial state, which is insufficient to fully eliminate motion distortion in the input point cloud;the system process noise is difficult to be determined during state estimation of the complex motions;and the varying motion models across different sensor carriers. To address these issues, we propose the Dual-Iteration Extended Kalman Filter (I2EKF) and the LiDAR odometry based on I2EKF (I2EKF-LO). This approach not only iterates over the observation equation but also leverages state updates to iteratively mitigate motion distortion in LiDAR point clouds. Moreover, it dynamically adjusts process noise based on the confidence level of prior predictions during state estimation and establishes motion models for different sensor carriers to achieve accurate and efficient state estimation. Comprehensive experiments demonstrate that I2EKF-LO achieves outstanding levels of accuracy and computational efficiency in the realm of LiDAR odometry. Additionally, to foster community development, our code is open-sourced. Copyright © 2024, The Authors. All rights reserved.

关键词： state estimation

Learning stereopsis from geometric synthesis for 6D object pose estimation

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wu, Jun Liu, Lilu Wang, Yue Xiong, Rong State Key Laboratory of Industrial Control Technology and Institue of Cyber-Systems and Control Zhejiang University Zhejiang China

Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information. To make up this gap, this paper proposes a 3D geometric volume based pose estimation method with a short baseline two-view setting. By constructing a geometric volume in the 3D space, we combine the features from two adjacent images to the same 3D space. Then a network is trained to learn the distribution of the position of object keypoints in the volume, and a robust soft RANSAC solver is deployed to solve the pose in closed form. To balance accuracy and cost, we propose a coarse-to-fine framework to improve the performance in an iterative way. The experiments show that our method outperforms state-of-the-art monocular-based methods, and is robust in different objects and scenes, especially in serious occlusion situations. Copyright © 2021, The Authors. All rights reserved.

关键词： Geometry