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Visual Sensor Aided By Image processing Based on Optimization Algorithm Research of Mobile robot Localization 5

Visual Sensor Aided By Image Processing Based on Optimizatio...

引用

5th international conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2023

作者： Peng, Hui Chongqing Vocational Institute of Engineering Chongqing402260 China

ISBN: (纸本)9781839539848

In recent years, with the rapid development of artificial intelligence technology, intelligent robots have become a popular research object in academia and industry. An indoor autonomous mobile robot must face three main problems: where am I, where am I going, and how do I get there. Among them, the "where am I" localization problem is particularly important. This paper focuses on a vision-based multi-sensor fusion indoor localization scheme, which is based on binocular vision and fuses wheeled ranging and IMU, and proposes a high accuracy, high robustness and low cost multi-sensor fusion vision SLAM scheme based on ranging and IMU fusion, with pose update assisted vision ranging at the front end and graphics optimization at the back end. Finally, the shortcomings of traditional vision SLAM algorithms that rely too much on image information are compensated by the optimization and improvement of the algorithms. The EKF algorithm is used to combine the robotic wheeled ranger with the I MU's pose information-assisted vision program as a precursor to the SLAM algorithm. Finally, localization accuracy experiments and localization robustness experiments are conducted, which prove that the algorithm proposed in this paper has high localization accuracy and robustness. © The Institution of Engineering & Technology 2023.

关键词： Mobile robots

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Fast Point to Mesh Distance by Domain Voxelization

Fast Point to Mesh Distance by Domain Voxelization

引用

IEEE/RSJ international conference on Intelligent robots and Systems (IROS)

作者： Gutow, Geordan Choset, Howie CMU Robot Inst Pittsburgh PA 15213 USA

ISBN: (纸本)9781665491907

Computing the distance from a point to a triangle mesh is a key computational step in robotics pipelines such as registration and collision detection, with applications to path planning, SLAM, and RGB-D vision. Numerous techniques to accelerate this computation have been developed, many of which use a cheap pre-processing step to construct a hierarchical decomposition of the mesh. If the mesh is fixed and known ahead of time, there is an opportunity to conduct more expensive pre-computations to accelerate the subsequent distance queries. This work presents a voxelization approach, implemented on both CPU and GPU, to compute point to mesh distance that constructs for each voxel a near-minimal set of triangles that is guaranteed to include every triangle that is closest to at least one point in the voxel. Theoretical and numerical comparisons with six alternative distance algorithms demonstrate the speed advantages of the proposed method.

关键词： Motion planning

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Fuzzy Controller Design for Table Tennis robot to Hit Spinning Balls 6

Fuzzy Controller Design for Table Tennis Robot to Hit Spinni...

引用

6th IEEE international conference on Knowledge Innovation and Invention, ICKII 2023

作者： Wu, Wen-Jia Chuang, Ying-Shu Sun, Chung-Hsun Chen, Hsiang-Chieh National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan National Central University Department of Mechanical Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350323535

A fuzzy controller was designed for a table tennis robot to strike a ball with topspin or backspin. First, the topspin or backspin was classified by comparing the difference between the binocular-vision-based measurement of a ball trajectory and the predicted trajectory using an ideal flying model. The striking signal, including the contact position and hitting time, of our table tennis robot can be estimated by inputting the trajectory features into a corresponding model. The face angle of a racket at the hitting moment was derived by a fuzzy inference system and was then used to control the robot. The average error of the contact position predicted by the proposed method was about 3 cm, and the average error of hitting timing was less than 13.5 ms. Experimental results verified the effectiveness of the proposed controller for striking spin balls. © 2023 IEEE.

关键词： Fuzzy control

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vision-based Pill Blister Package Inspection System using CNN 23

Vision-based Pill Blister Package Inspection System using CN...

引用

13th international conference on Biomedical Engineering and Technology, ICBET 2023

作者： Gobee, Suresh Durairajah, Vickneswari Prea, Laurent Eddie Sylvester Asia Pacific University of Technology and Innovation Malaysia

ISBN: (纸本)9798400707438

This research aimed to develop an automated blister package sorting system based on defects detected using image data. The proposed approach aims to reduce the lead time incurred by manual sorting in the production environment and provide a low-cost automated alternative. The image detection model to detect the defects was developed using YOLOv5 pre-trained object detection model from the PyTorch framework. The dataset collected 350 labeled images, including 40% of those which were augmented and duplicated using Roboflow image processing. The object detection results were leveraged in multiple ways including detecting the presence of blister package in the frame and for pose estimation, among the mainstream defect detection. The image coordinate was converted to a robot arm coordinate to enable effective manipulation. Dobot Magician robot arm was used to actuate the blister package and was controlled using its native API. The image processing and robot arm manipulation module was integrated using Python. The detection model held a mean average precision(mAP) of 86.1% for any random rotations of the blister package, and the sorting rate averaged 7.68s per iteration.. © 2023 ACM.

关键词： Object detection

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CNN-BASED LOCAL TONE MAPPING IN THE PERCEPTUAL QUANTIZATION DOMAIN 29

CNN-BASED LOCAL TONE MAPPING IN THE PERCEPTUAL QUANTIZATION ...

引用

IEEE international conference on Image processing (ICIP)

作者： You, Hongjie Chen, Hu Wang, Yichuan Cui, Kai Steinbach, Eckehard Huawei Munich Res Ctr Munich Germany Tech Univ Munich Media Technol Munich Germany Tech Univ Munich Munich Inst Robot & Machine Intelligence Munich Germany Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

A typical way of local tone mapping (TM) is based on multi-layer decomposition of the source image. For this, the source image is decomposed into a base layer and a detail layer to compress from high dynamic range (HDR) to low dynamic range (LDR). Perceptual quantization (PQ) is a standardized non-linear transfer function for HDR content. It mimics the non-linearity of human vision by compressing more strongly in bright regions and less in dark areas, when converting luminance values to electrical signals. We propose a CNN-based pipeline for local TM, which operates on the low-frequency base layer of the luminance signal, while keeping the detail layer unchanged. The proposed method works entirely in the PQ domain and is adaptable to display peak luminance. The tone-mapped LDR images obtained with our learning-based approach show significant improvements in PSNR, while the network size is reduced compared to previous work. Our experiments on the HDR datasets from Fairchild and Funt show PSNR improvements of 8 dB compared to the state-of-the-art approaches.

关键词： High dynamic range tone mapping perceptual quantization convolutional neural network

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Safe Reinforcement Learning using Data-Driven Predictive Control 5

Safe Reinforcement Learning using Data-Driven Predictive Con...

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5th international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Selim, Mahmoud Alanwar, Amr El-Kharashi, M. Watheq Abbas, Hazem M. Johansson, Karl H. Ain Shams Univ Cairo Egypt Jacobs Univ Bremen Germany KTH Royal Inst Technol Stockholm Sweden

ISBN: (纸本)9781665482370

Reinforcement learning (RL) algorithms can achieve state-of-the-art performance in decision-making and continuous control tasks. However, applying RL algorithms on safety-critical systems still needs to be well justified due to the exploration nature of many RL algorithms, especially when the model of the robot and the environment are unknown. To address this challenge, we propose a data-driven safety layer that acts as a filter for unsafe actions. The safety layer uses a data-driven predictive controller to enforce safety guarantees for RL policies during training and after deployment. The RL agent proposes an action that is verified by computing the data-driven reachability analysis. If there is an intersection between the reachable set of the robot using the proposed action, we call the data-driven predictive controller to find the closest safe action to the proposed unsafe action. The safety layer penalizes the RL agent if the proposed action is unsafe and replaces it with the closest safe one. In the simulation, we show that our method outperforms state-of-the-art safe RL methods on the robotics navigation problem for a Turtlebot 3 in Gazebo and a quadrotor in Unreal Engine 4 (UE4).

关键词： Reinforcement learning robot safety task and motion planning

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Boundary Cue Guidance and Contextual Feature Mining for Glass Segmentation 48

Boundary Cue Guidance and Contextual Feature Mining for Glas...

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48th IEEE international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Xiao, Qiquan Zhang, Yuan Li, Xuanya Hu, Kai Xiangtan University Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan411105 China Baidu Inc. Beijing100085 China

ISBN: (纸本)9781728163277

Glass is ubiquitous in the real world, and its perception has many applications, including robot navigation and drone tracking. However, due to the transparent property of glass, the interior of a glass area can be any surrounding scene or object, which brings challenges for computer vision. Inspired by the human senses, boundary cues are one of the crucial factors for people to judge the location of glass contours. Hence, we propose a boundary cue guidance and contextual feature mining network (BCNet) to accurately and efficiently segment glass. Specifically, we first design a multi-branch boundary extraction module (MBEM) for learning accurate boundary cues combined with multi-level encoded features. Second, we propose a boundary cue guidance module (BCGM), inject the boundary cues into the representation learning, and provide constraints with object structure semantics to guide feature extraction. Besides, we design a contextual feature mining module (CFMM) to dynamically capture the contextual information of different receptive fields for the detection of different sizes and shapes of the glass. Finally, extensive experiments on two benchmark glass datasets, GDD and GSD. The results demonstrate that our BCNet achieves state-of-the-art segmentation performance against existing methods. © 2023 IEEE.

关键词： Boundary cue guidance Contextual feature Convolutional neural network Glass segmentation

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Mechatronics and Automation Technology - Proceedings of the 2nd international conference, ICMAT 2023

Mechatronics and Automation Technology - Proceedings of the ...

引用

2nd international conference on Mechatronics and Automation Technology, ICMAT 2023

ISBN: (纸本)9781643684826

The proceedings contain 77 papers. The topics discussed include: thermodynamic calculation and design for desulfurization wastewater flue gas evaporation zero discharge system of the 1,000 MW unit;simulation and analysis of multi-signal middle-orbit spacecraft positioning performance;research on vibration reduction design of railway wagons bearing based on dynamics;the solution of the control problem in installations implementing complex rotational-oscillatory motion of working elements;key issues and countermeasures of machine vision for fruit and vegetable picking robot;estimation of resonance echo signal of Kalman filter algorithm based on PCA under pipe string sound field model;development of a drilling cycle program with decreasing depth of pecking drilling;design and implementation of smart phone assistant system for the elderly based on WeChat mini program;and experimental study of active vibration control for flexible truss by using a Stewart platform manipulator.

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Grasping with Humanoid Hands based on In-Hand vision and Hardware-accelerated CNNs 22

Grasping with Humanoid Hands based on In-Hand Vision and Har...

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IEEE-RAS 22nd international conference on Humanoid robots (Humanoids)

作者： Hundhausen, Felix Hubschneider, Simon Asfour, Tamim Karlsruhe Inst Technol Inst Anthropomat & Robot Karlsruhe Germany

ISBN: (纸本)9798350303278

We present a vision-based grasping system for humanoid and prosthetic hands using hardware-accelerated CNNs for real-time object classification and class-aware pixel-wise segmentation. The system is implemented on the hand internal processing hardware of a humanoid hand using a System-on-Chip (SoC) comprising a Processor System (PS) and an FPGA. As a sensor system, the hand provides an integrated RGB camera, a multi-region Time-of-Flight (ToF) depth sensor, and an Inertial Measurement Unit (IMU). We propose an algorithm for 3D object shape estimation based on sensory information provided by the hand internal sensor system. The 3D object mesh in combination with the object relative pose of the hand is used as input for a reactive grasp controller. For the design of the CNN-based object recognition and segmentation networks, we use a resource-aware algorithm for Network-Architecture-Synthesis (NAS). We evaluate the visual perception accuracy and 3D model estimation accuracy in grasping experiments with six objects. We obtain a mean object segmentation accuracy of 84.4 % and a mean error for object diameter estimation of 44 mm.

关键词： Object recognition

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DESIGNING STRONG BASELINES FOR TERNARY NEURAL NETWORK QUANTIZATION THROUGH SUPPORT AND MASS EQUALIZATION 30

DESIGNING STRONG BASELINES FOR TERNARY NEURAL NETWORK QUANTI...

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30th IEEE international conference on Image processing (ICIP)

作者： Yvinec, Edouard Dapogny, Arnaud Bailly, Kevin Sorbonne Univ CNRS ISIR 4 Pl Jussieu F-75005 Paris France Datakalab 114 Blvd Malesherbes F-75017 Paris France

ISBN: (纸本)9781728198354

Deep neural networks (DNNs) offer the highest performance in a wide range of applications in computer vision. These results rely on over-parameterized backbones, which are expensive to run. This computational burden can be dramatically reduced by quantizing (in either data-free (DFQ), post-training (PTQ) or quantization-aware training (QAT) scenarios) floating point values to ternary values (2 bits, with each weight taking value in {-1, 0, 1}). In this context, we observe that rounding to nearest minimizes the expected error given a uniform distribution and thus does not account for the skewness and kurtosis of the weight distribution, which strongly affects ternary quantization performance. This raises the following question: shall one minimize the highest or average quantization error? To answer this, we design two operators: TQuant and MQuant that correspond to these respective minimization tasks. We show experimentally that our approach allows to significantly improve the performance of ternary quantization through a variety of scenarios in DFQ, PTQ and QAT and give strong insights to pave the way for future research in deep neural network quantization.

关键词： Quantization Deep Learning Computer vision

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