检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Wei, Wen Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Accurately predicting the trajectory of surrounding vehicles is a critical challenge for autonomous vehicles. In complex traffic scenarios, there are two significant issues with the current autonomous driving system: the cognitive uncertainty of prediction and the lack of risk awareness, which limit the further development of autonomous driving. To address this challenge, we introduce a novel trajectory prediction model that incorporates insights and principles from driving behavior, ethical decision-making, and risk assessment. Based on joint prediction, our model consists of interaction, intention, and risk assessment modules. The dynamic variation of interaction between vehicles can be comprehensively captured at each timestamp in the interaction module. Based on interaction information, our model considers primary intentions for vehicles to enhance the diversity of trajectory generation. The optimization of predicted trajectories follows the advanced risk-aware decision-making principles. Experimental results are evaluated on the DeepAccident dataset;our approach shows its remarkable prediction performance on normal and accident scenarios and outperforms the state-of-the-art algorithms by at least 28.9% and 26.5%, respectively. The proposed model improves the proficiency and adaptability of trajectory prediction in complex traffic scenarios. The code for the proposed model is available at https://***/view/ir-prediction. Copyright © 2024, The Authors. All rights reserved.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

Mani-GPT: A Generative Model for Interactive robotic Manipulation

Mani-GPT: A Generative Model for Interactive Robotic Manipul...

引用

2023 International Conference on Biomimetic intelligence and robotics

作者： Zhang, Zhe Chai, Wei Wang, Jiankun Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Orthopedics the Fourth Medical Center of PLA General Hospital Beijing China National Clinical Research Center for Orthopedics Sports Medicine and Rehabilitation Beijing China

In real-world scenarios, human dialogues are multi-round and diverse. Furthermore, human instructions can be unclear and human responses are unrestricted. Interactive robots face difficulties in understanding human intents and generating suitable strategies for assisting individuals through manipulation. In this article, we propose Mani-GPT, a Generative Pre-trained Transformer (GPT) for interactive robotic manipulation. The proposed model has the ability to understand the environment through object information, understand human intent through dialogues, generate natural language responses to human input, and generate appropriate manipulation plans to assist the human. This makes the human-robot interaction more natural and humanized. In our experiment, Mani-GPT outperforms existing algorithms with an accuracy of 84.6% in intent recognition and decision-making for actions. Furthermore, it demonstrates satisfying performance in real-world dialogue tests with users, achieving an average response accuracy of 70%. © 2023 The Authors. Published by Elsevier B.V.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

An Automated Adolescent Idiopathic Scoliosis Assessment and Monitoring Model Based on Back Surface

SSRN

引用

SSRN 2024年

作者： Yang, Jie Li, Yemin Hu, Anjun Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Automated assessment and monitoring of adolescent idiopathic scoliosis (AIS) based on the back surface is crucial for diagnosing, treating, and surveilling scoliosis. Unharmonized quantitative assessment metrics, inadequate 3D spine status description, and accurate localization of spinous process (SP) tips during the Adams Forward Bending Test (AFBT) posture are the main challenges. We propose an efficient model that uses the angle of trunk rotation (ATR) and the spinous process angle (SPA) as uniform quantitative assessment metrics. The ATR measures the rotation of the spinal trunk in the sagittal plane, while the SPA measures the degree of spinal tilt in the coronal plane, providing a comprehensive 3D description of the spine. Additionally, the proposed model uses prior information about back contour curves and the spine to accurately locate SP tips on the back surface. It also includes a method for ATR and SPA calculation based on SP and transverse process (TP) tips for automated AIS assessment and monitoring. The experimental results indicate that the proposed model achieves excellent SP localization. The Euclidean distance from the ground truth (GT) is 0.66±0.37/0.30, 0.22±0.23/0.07, and 0.71±0.42/0.30 from coronal, sagittal, and 3D views, respectively. The correlation between the ATR and SPA results obtained by the proposed model and the GT is high, at 0.973 and 0.962, respectively. Additionally, the results obtained by the proposed model are in good agreement with the GT in the Bland and Altman method. More details can be found at https://***/view/2024scoliosis/home/. © 2024, The Authors. All rights reserved.

关键词： Bending tests

来源：评论

学校读者我要写书评

暂无评论

Indoor Exploration and Simultaneous Trolley Collection Through Task-Oriented Environment Partitioning

Indoor Exploration and Simultaneous Trolley Collection Throu...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Junjie Gao Peijia Xie Xuheng Gao Zhirui Sun Jiankun Wang Max Q.-H. Meng The Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we present a simultaneous exploration and object search framework for the application of autonomous trolley collection. For environment representation, a task-oriented environment partitioning algorithm is presented to extract diverse information for each sub-task. First, LiDAR data is classified as potential objects, walls, and obstacles after outlier removal. Segmented point clouds are then transformed into a hybrid map with the following functional components: object proposals to avoid missing trolleys during exploration; room layouts for semantic space segmentation; and polygonal obstacles containing geometry information for efficient motion planning. For exploration and simultaneous trolley collection, we propose an efficient exploration-based object search method. First, a traveling salesman problem with precedence constraints (TSP-PC) is formulated by grouping frontiers and object proposals. The next target is selected by prioritizing object search while avoiding excessive robot backtracking. Then, feasible trajectories with adequate obstacle clearance are generated by topological graph search. We validate the proposed framework through simulations and demonstrate the system with real-world autonomous trolley collection tasks.

关键词： Point cloud compression Laser radar Motion segmentation Semantics Layout Search problems Robot sensing systems

来源：评论

学校读者我要写书评

暂无评论

A Landmark-Aware Network for Automated Cobb Angle Estimation from X-Ray Images

SSRN

引用

SSRN 2024年

作者： Yang, Jie Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Automated Cobb angle estimation based on X-ray images plays an important role in scoliosis diagnosis, treatment, and progression monitoring. The inadequate feature extraction and the noise in X-ray images are the main difficulties of automated Cobb angle estimation, and it is challenging to ensure that the calculated Cobb angle meets clinical requirements. To address these problems, we propose a Landmark-aware Network named LaNet with three components, Feature Robustness Enhancement Module (FREM), Landmark-aware Objective Function (LOF), and Cobb Angle Calculation Method (CACM), for automated Cobb angle estimation in this paper. To enhance feature extraction, FREM is designed to explore geometric and semantic constraints among landmarks, thus geometric and semantic correlations between landmarks are globally modeled, and robust landmark-based features are extracted. Furthermore, to mitigate the effect of background noise on landmark localization, LOF is proposed to focus more on the foreground near the vertebrae and ignore irrelevant background pixels by exploiting category prior information of landmarks. In addition, we also advance CACM to locate the bending segments first and then calculate the Cobb angle within the bending segment, which facilitates the calculation of the clinical standardized Cobb angle. The experiment results on the AASCE dataset demonstrate that our proposed LaNet can significantly improve the Cobb angle estimation performance and outperform other state-of-the-art methods. © 2024, The Authors. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Leveraging Semantic and Geometric Information for Zero-Shot Robot-to-Human Handover

arXiv

引用

arXiv 2024年

作者： Liu, Jiangshan Dong, Wenlong Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

Human-robot interaction (HRI) encompasses a wide range of collaborative tasks, with handover being one of the most fundamental. As robots become more integrated into human environments, the potential for service robots to assist in handing objects to humans is increasingly promising. In robot-to-human (R2H) handover, selecting the optimal grasp is crucial for success, as it requires avoiding interference with the human’s preferred grasp region and minimizing intrusion into their workspace. Existing methods either inadequately consider geometric information or rely on data-driven approaches, which often struggle to generalize across diverse objects. To address these limitations, we propose a novel zero-shot system that combines semantic and geometric information to generate optimal handover grasps. Our method first identifies grasp regions using semantic knowledge from vision-language models (VLMs) and, by incorporating customized visual prompts, achieves finer granularity in region grounding. A grasp is then selected based on grasp distance and approach angle to maximize human ease and avoid interference. We validate our approach through ablation studies and real-world comparison experiments. Results demonstrate that our system improves handover success rates and provides a more user-preferred interaction experience. Videos, appendixes and more are available at https://***/view/vlm-handover/. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Learning-based Detection of GPS Spoofing Attack for Quadrotors

arXiv

引用

arXiv 2025年

作者： Wang, Pengyu Yang, Zhaohua Li, Jialu Shi, Ling Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Safety-critical cyber-physical systems (CPS), such as quadrotor UAVs, are particularly prone to cyber attacks, which can result in significant consequences if not detected promptly and accurately. During outdoor operations, the nonlinear dynamics of UAV systems, combined with non-Gaussian noise, pose challenges to the effectiveness of conventional statistical and machine learning methods. To overcome these limitations, we present QUADFormer, an advanced attack detection framework for quadrotor UAVs leveraging a transformer-based architecture. This framework features a residue generator that produces sequences sensitive to anomalies, which are then analyzed by the transformer to capture statistical patterns for detection and classification. Furthermore, an alert mechanism ensures UAVs can operate safely even when under attack. Extensive simulations and experimental evaluations highlight that QUADFormer outperforms existing state-of-the-art techniques in detection accuracy. (Video1) Copyright © 2025, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

来源：评论

学校读者我要写书评

暂无评论

A Landmark-Aware Network for Automated Cobb Angle Estimation from X-Ray Images

SSRN

引用

SSRN 2024年

作者： Yang, Jie Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence the Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China

关键词：

来源：评论

学校读者我要写书评

暂无评论

Virtual Reality Based Robot Teleoperation via Human-Scene Interaction

Virtual Reality Based Robot Teleoperation via Human-Scene In...

引用

2023 International Conference on Biomimetic intelligence and robotics

作者： Meng, Lingxiao Liu, Jiangshan Chai, Wei Wang, Jiankun Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence Shenzhen China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Orthopedics the Fourth Medical Center of PLA General Hospital Beijing China National Clinical Research Center for Orthopedics Sports Medicine and Rehabilitation Beijing China

Robot teleoperation gains great success in various situations, including chemical pollution rescue, disaster relief, and long-distance manipulation. In this article, we propose a virtual reality (VR) based robot teleoperation system to achieve more efficient and natural interaction with humans in different scenes. A user-friendly VR interface is designed to help users interact with a desktop scene using their hands efficiently and intuitively. To improve user experience and reduce workload, we simulate the process in the physics engine to help build a preview of the scene after manipulation in the virtual scene before execution. We conduct experiments with different users and compare our system with a direct control method across several teleoperation tasks. The user study demonstrates that the proposed system enables users to perform operations more instinctively with a lighter mental workload. Users can perform pick-and-place and object-stacking tasks in a considerably short time, even for beginners. Our code is available at https://***/lingxiaomeng/VR_Teleoperation_Gen3. © 2023 The Authors. Published by Elsevier B.V.

关键词： Remote control

来源：评论

学校读者我要写书评

暂无评论

A Novel Serial-Parallel Hybrid Robot for Rehabilitation Training of Gait and Balance

A Novel Serial-Parallel Hybrid Robot for Rehabilitation Trai...

引用

Electrical Engineering, Control and robotics (EECR), International Conference on

作者： Mingsong Shi Liziyi Hao Zhaocheng Zhou Sihui Liu Jianda Han Ningbo Yu Tianjin Key Laboratory of Intelligent Robotics College of Artificial Intelligence Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen Guangdong China

Robot-assisted rehabilitation training for lowerlimb function recovery has gained increasing acceptability, but simultaneous training of gait and balance has not been well explored. In this paper, we propose a novel 5-DOF serial-parallel hybrid end-effort robot to provide gait training, balance training, as well as gait and balance training. Each side of the robot is composed of a 2-DOF gait generator and a 3-DOF parallel balance module. The gait generator takes the H-Bot mechanism, which distributes the heavy load of the patient to the two motors during walking, which are connected in serial. For the balance module, its virtual rotation center of the footplate is designed to be aligned with the ankle joint, enabling natural balance adjustment and training. Further, two displacement sensors and an inclinometer are employed to measure the pose, and closed-loop control of the 2-DOF position and 3-DOF orientation of the robot end-effector is realized. The robotic system has been built up, and achieved good performance on position and pose tracking, promising an effective rehabilitation platform for independent and simultaneous training of gait and balance.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：