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Design of three-dimensional imaging lidar optical system for large field of view scanning

Chinese Physics B 2022年第7期31卷 284-289页

作者： Qing-Yan Li Yu Zhang Shi-Yu Yan Bin Zhang Chun-Hui Wang National Key Laboratory of Tunable Laser Technology Harbin Institute of TechnologyHarbin 150001China Geling Institute of Artificial Intelligence and Robotics Shenzhen 518063China

Three-dimensional(3D)lidar has been widely used in various *** MEMS scanning system is one of its most important components,while the limitation of scanning angle is the main obstacle to improve the demerit for its application in various *** this paper,a folded large field of view scanning optical system is *** structure and parameters of the system are determined by theoretical derivation of ray *** optical design software Zemax is used to design the *** optimization,the final structure performs well in collimation and beam *** results show that the scan angle can be expanded from±5°to±26.5°,and finally the parallel light scanning is *** spot diagram at a distance of 100 mm from the exit surface shows that the maximum radius of the spot is 0.506 mm with a uniformly distributed *** maximum radius of the spot at 100 m is 19 cm,and the diffusion angle is less than 2 *** energy concentration in the spot range is greater than 90%with a high system energy concentration,and the parallelism is *** design overcomes the shortcoming of the small mechanical scanning angle of the MEMS lidar,and has good performance in collimation and beam *** provides a design method for large-scale application of MEMS lidar.

关键词： 3D lidar MEMS scanning system large field of view scanning Zemax

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Healthcare Monitoring Companion: The Social Robot 3

Healthcare Monitoring Companion: The Social Robot

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3rd International Conference on artificial intelligence For Internet of Things, AIIoT 2024

作者： Goraksha, Rahul Bongale, Arunkumar Sayyad, Sameer Kumar, Satish Symbiosis Institute of Technology Department of Robotics and Automation Pune412115 India Symbiosis Centre for Applied Artificial Intelligence Department of Robotics and Automation Pune412115 India

ISBN: (纸本)9798350372120

There is a rising scarcity of healthcare professionals to meet the demand of taking care of aged people. Social robots are becoming more and more popular because of the benefits they provide;during the pandemic, in particular, several of their primary applications are advantageous. The purpose of this work is to build the prototype of social robots and carry out the integration of those robots into healthcare monitoring using Thing Speak and Raspberry Pi. This prototype of social robots with Thing Speak and Raspberry Pi hosts an array of sensors (for measuring a multitude of data from the body of humans) like heartbeat sensor, Dallas temperature sensor, Radio-Frequency Identification (RFID), ultrasonic sensor, and Infrared (IR) sensor along with other components like motor controlling module and DC motor. The data from these sensors are transmitted to the cloud platform Thing Speak, offering remote monitoring and data analysis capabilities and the Raspberry Pi acts as the central processing element in our design, which additionally helps in triggering corresponding alerts regarding the patient status. Finally, we experimentally validate our design using 2 cases and then we present the assessment of the patient alerting feature. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Neural network-based adaptive sliding mode control for underactuated dual overhead cranes suffering from matched and unmatched disturbances

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Autonomous Intelligent Systems 2022年第1期2卷 1-15页

作者： Tianci Wen Yongchun Fang Biao Lu Institute of Robotics and Automatic Information System College of Artificial IntelligenceNankai UniversityTianjin300353China

To improve transportation capacity,dual overhead crane systems(DOCSs)are playing an increasingly important role in the transportation of large/heavy cargos and ***,when trying to deal with the control problem,current methods fail to fully consider such factors as external disturbances,input dead zones,parameter uncertainties,and other unmodeled dynamics that DOCSs usually suffer *** a result,dramatic degradation is caused in the control performance,which badly hinders the practical applications of *** by this fact,this paper designs a neural network-based adaptive sliding mode control(SMC)method for DOCS to solve the aforementioned issues,which achieves satisfactory control performance for both actuated and underactuated state variables,even in the presence of matched and mismatched *** asymptotic stability of the desired equilibrium point is proved with rigorous Lyapunov-based ***,extensive hardware experimental results are collected to verify the efficiency and robustness of the proposed method.

关键词： Asymptotic stability Sliding mode control Neural networks Unmatched disturbances Underactuated system

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Multi-Beams Forming and Steering Based on Optical Phased Array 8

Multi-Beams Forming and Steering Based on Optical Phased Arr...

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8th Optoelectronics Global Conference, OGC 2023

作者： Luo, Haipeng Sun, Caiming Li, Binghui Ding, Ning Zhang, Aidong The Chinese University of Hong Kong Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China

In this work, the use of a single sparse Optical Phased Array (OPA) to achieve arbitrary multi-beams forming and steering is studied and demonstrated. This approach can be efficient and cost-effective for multiple-use... 详细信息

ISBN: (纸本)9798350325324

关键词： Silicon photonics

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Object Stiffness Discrimination from Fingertip Forces 23

Object Stiffness Discrimination from Fingertip Forces

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6th International Conference on Advances in robotics, AIR 2023

作者： Dey, Abhijit Hazarika, Shyamanta Biomimetic Robotics and Artificial Intelligence Lab Indian Institute of Technology - Guwahati Assam Guwahati India

ISBN: (纸本)9781450399807

Tactile perception is crucial for detecting contact and identifying the attributes associated with it. In this research, we demonstrate that the mechanical properties of objects may be inferred by employing data-driven algorithms from fingertip contact. We collected information from a fingertip pressure sensor as it established contact with several objects using a power prehensile circular grasp. This study establishes a correlation between fingertip force and object stiffness by incorporating a clustering algorithm to assess two phases of force data for dynamic and steady state analysis. We employed the dynamic time warping (DTW) distance-based metric and the K-means clustering technique. For the two phases, we have identified three main clusters that may be characterized as pseudo labels: Soft, Medium, and Hard. To demonstrate the robustness of the model evaluation, we performed 10-fold cross-validation. For each fold, we calculated the Bouldin Index and Silhouette Coefficient and reported the mean value to evaluate the efficacy of the clusters. © 2023 ACM.

关键词： Stiffness

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DE-Unet: Dual-encoder U-Net for Ultra-high Resolution Remote Sensing Image Segmentation

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12290-12302页

作者： Liu, Ye Song, Shitao Wang, Miaohui Gao, Hao Liu, Jun Nanjing University of Posts and Telecommunications School of Automation and Artificial Intelligence China Shenzhen University Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Lancaster University School of Computing and Communications United Kingdom

In recent years, there has been a growing demand for remote sensing image semantic segmentation in various applications. The key to semantic segmentation lies in the ability to globally comprehend the input image. While recent transformer-based methods can effectively capture global contextual information, they suffer from high computational complexity, particularly when it comes to ultra-high resolution (UHR) remote sensing images, it is even more challenging for these methods to achieve a satisfactory balance between accuracy and computation speed. To address these issues, we propose in this paper a CNN-based dual-encoder U-Net for effective and efficient UHR image segmentation. Our method incorporates dual encoders into the symmetrical framework of U-Net. The dual encoders endow the network with strong global and local perception capabilities simultaneously, while the U-Net's symmetrical structure guarantees the network's robust decoding ability. Additionally, multi-path skip connections ensure ample information exchange between the dual encoders, as well as between the encoders and decoders. Furthermore, we proposes a context-aware modulation fusion module that guides the encoder-encoder and encoder-decoder data fusion through global receptive fields. Experiments conducted on public UHR remote sensing datasets such as the Inria Aerial and DeepGlobe have demonstrated the effectiveness of proposed method. Specifically on the Inria Aerial dataset, our method achieves a 77.42% mIoU which outperforms the baseline [1] by 3.14% while maintaining comparable inference speed. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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A Sparsity-Aware Autonomous Path Planning Accelerator with Algorithm-Architecture Co-Design 24

A Sparsity-Aware Autonomous Path Planning Accelerator with A...

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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Zhang, Yanjun Niu, Xiaoyu Zhang, Yifan Tian, Hongzheng Yu, Bo Liu, Shaoshan Huang, Sitao Beijing Institute of Technology China University of California Irvine United States Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (纸本)9798400710773

Path planning is a critical task in autonomous driving systems that is most susceptible to real-time constraints but often demands computationally intensive mathematical solvers, two contradictory goals. This conflict makes the computing of path planning a paramount challenge. At the heart of most path planners is the quadratic programming (QP) solver, which places excessive demands on the CPU in real-world autonomous driving applications. In this paper, we present an FPGA-based acceleration framework for path planning problems. Our approach leverages an operator splitting solver for quadratic programs (OSQP) and employs the preconditioned conjugate gradient (PCG) method for solving linear systems, which are customized to be more hardware-friendly than prior works. Specific memory management and parallel processing were tailored to the matrix pattern, and the incorporation of pipelining was executed to enhance throughput and execution speed. Our FPGA-based implementation achieves state-of-the-art performance against existing works, including an average 1.98× speedup compared with the state-of-the-art QP solver on Intel i7-11800H CPU, 3.90× speedup over an ARM Cortex-A57 embedded CPU, and 12.3× speedup over an NVIDIA RTX 3090 GPU. © 2024 Copyright is held by the owner/author(s).

关键词： Quadratic programming

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Accident-CLIP: Text-Video Benchmarking for Fine-Grained Accident Classification in Driving Scenes 3rd

Accident-CLIP: Text-Video Benchmarking for Fine-Grained Acc...

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3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023

作者： Yang, Kuan Fang, Jianwu Zhu, Tong Xue, Jianru College of Transportation Engineering Chang’an University Xi’an China The Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9789819710867

Road accident classification which is an essential but rarely explored problem in the safe driving field. The core issue of accident classification is to learn the feature representation for partitioning different kinds of accidents. Compared with accident detection or anticipation only with occurrence probability, feature representation learning in accident classification is more challenging because of the extremely imbalanced accident categories. In addition, the severe light or weather conditions, various occasions, and complex crashing-object movement exacerbate the challenges. In this work, we form a text-video benchmark for fine-grained road accident classification (named Accident-CLIP). Accident-CLIP owns 13,669 dashcam videos with 58 kinds of accidents, where each accident is annotated with the text description of the accident type and the accident window. In the benchmarking stage, six state-of-the-art methods are evaluated from different video frame sampling methods, frame mixup strategy, input frame length, and the adaptation for long-tailed accident distribution. From experiments, we observe that current video classification models need a large space (the best Top-1 value is 41.39% on 2000 testing videos) to adapt to the extremely imbalanced road accident classification, and the formed Accident-CLIP benchmark provides a promising evaluation platform. © Beijing HIWING Scientific and Technological Information institute 2024.

关键词： Roads and streets

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Tiny Det: accurately detecting small objects within 1 GFLOPs

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Science China(Information Sciences) 2023年第1期66卷 318-319页

作者： Shaoyu CHEN Tianheng CHENG Jiemin FANG Qian ZHANG Yuan LI Wenyu LIU Xinggang WANG School of Electronic Information and Communications Huazhong University of Science and Technology Institute of Artificial Intelligence Huazhong University of Science and Technology Horizon Robotics Inc. Google Inc.

Dear editor,Object detection plays an important role in computer vision,and has gradually become the technical foundation of many applications. However, the inference procedure of advanced detection models requires ma... 详细信息

关键词： Economic and social effects

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Abusive Activity Detection with Multi-Modality Based on Convolutional Neural Network 48

Abusive Activity Detection with Multi-Modality Based on Conv...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kim, Jisoo Ahn, Hyebin Yoo, Byounghyun Korea Institute of Science and Technology Center for Artificial Intelligence Korea Republic of Korea University of Science and Technology Artificial Intelligence and Robotics Kist School Korea Republic of

ISBN: (纸本)9781728163277

Abusive activity frequently occurs in various places, including nursing homes for the elderly. However, it is difficult to detect because it has various forms and is not easy to define. Therefore, in this study, we try to detect using the Convolutional Neural Network (CNN). Data based on multimodality (image and sound) was used to improve detection performance. In case of the image, a Motion History Image (MHI) was used to reflect motion information. The developed multi-modality-based detection system showed a performance of 0.807 (Accuracy) and 0.858 (F1 score). By comparing with the system using only image or sound, there is an improvement of 0.132 ~ 0.17 (Accuracy) and 0.094 ~ 0.138 (F1 score) © 2023 IEEE.

关键词： Abusive Activity Detection Convolutional Neural Network Motion History Image Multi-modality

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