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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

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Floor extraction and door detection for visually impaired guidance 16

Floor extraction and door detection for visually impaired gu...

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Berenguel-Baeta, B. Guerrero-Viu, M. Nova, A. Bermudez-Cameo, J. Perez-Yus, A. Guerrero, J. J. Univ Zaragoza Inst Invest Ingn Aragon Zaragoza 50018 Spain

ISBN: (纸本)9781728177090

Finding obstacle-free paths in unknown environments is a big navigation issue for visually impaired people and autonomous robots. Previous works focus on obstacle avoidance, however they do not have a general view of the environment they are moving in. New devices based on computer vision systems can help impaired people to overcome the difficulties of navigating in unknown environments in safe conditions. In this work it is proposed a combination of sensors and algorithms that can lead to the building of a navigation system for visually impaired people. Based on traditional systems that use RGB-D cameras for obstacle avoidance, it is included and combined the information of a fish-eye camera, which will give a better understanding of the user's surroundings. the combination gives robustness and reliability to the system as well as a wide field of view that allows to obtain many information from the environment. this combination of sensors is inspired by human vision where the center of the retina (fovea) provides more accurate information than the periphery, where humans have a wider field of view. the proposed system is mounted on a wearable device that provides the obstacle-free zones of the scene, allowing the planning of trajectories for people guidance.

关键词： Computer vision omnidirectional camera image processing floor extraction door detector RGBD camera fish-eye camera visually impaired guidance

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AUTOMATING CAMERA ROVER FOR SPORTS BROADCAST 12

AUTOMATING CAMERA ROVER FOR SPORTS BROADCAST

引用

12th international conference on Computing Communication and Networking Technologies, ICCCNT 2021

作者： Deshpande, Param Gaikwad, Omkar Jain, Manav Choksi, Krisha Kasambe, Prashant V. Rathod, Surendra Singh Electronics Department Sardar Patel Institute of Technology Mumbai India

ISBN: (纸本)9781728185958

A considerable amount of time and resources are spent on training pilots for a sports broadcast. A typical Camera Rover system requires multiple pilots to capture shots, first to control the rover, second to control the camera, and the third pilot to verify the output feed. Synchronization of these pilots is crucial for capturing the best shots. In today's world where Artificial Intelligence, robotics, and automation is helping the world reach new heights, automation in the Broadcast field still depends on human administration. this can be reduced by using algorithms for capturing shots and reducing the synchronization cost of pilots. this paper proposes a system to minimize this problem by automating the need for one pilot and providing a user-friendly interface for the extraction of the footage of the desired object. A user interface is provided to the pilot to select the object to be tracked and the system will try to follow the object based on the trajectory. the system consists of a camera rover, micro-controller unit, an onboard Computer, Gimbal, and a User Interface. © 2021 IEEE.

关键词： Computer vision

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A Machine vision Framework for Autonomous Inspection of Drilled Holes in CFRP Panels 6

A Machine Vision Framework for Autonomous Inspection of Dril...

引用

6th international conference on control, automation and robotics (ICCAR)

作者： Hernandez, Alejandro Maghami, Ali Khoshdarregi, Matt Technol Inst Veracruz Met Mech Engn Dept Veracruz Veracruz Mexico Univ Manitoba Dept Mech Engn Intelligent Digital Mfg Lab Winnipeg MB Canada

ISBN: (纸本)9781728161396

this paper presents a fully autonomous framework for the inspection of drilled holes in planar carbon fiber composite panels used in the aerospace and automotive industries. the proposed framework can automatically recognize a part and extract the geometrical information from an existing library of DXF files. It then determines the location and orientation of the part with respect to the motion platform without a need for explicit programming of the part coordinate system. Visual servoing and optimal motion planning techniques are used to autonomously move the end-effector's camera to each hole to capture high resolution images. Image processing techniques are used to determine the geometrical errors and delamination factors for each hole. All of the proposed computer vision modules have been implemented in Python and OpenCV, which are open source and thus readily available to the industry. Experimental results prove that the proposed framework can efficiently and autonomously inspect drilled holes in composite panels with minimal programming required of the end-user.

关键词： vision-based inspection visual servoing drilled hole aerospace panel composite material machine vision

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False Data Injection Attacks in Smart Grid Using Gaussian Mixture Model 16

False Data Injection Attacks in Smart Grid Using Gaussian Mi...

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Wang, Zhiwen Hu, Jiqiang Sun, Hongtao Lanzhou Univ Technol Natl Demonstrat Ctr Expt Elect & Control Engn Edu Lanzhou 730050 Peoples R China Lanzhou Univ Technol Coll Elect & Informat Engn Lanzhou 730050 Peoples R China Qufu Normal Univ Coll Engn Qufu 273165 Shandong Peoples R China

ISBN: (纸本)9781728177090

the application of network technology and high-tech equipment in power systems has increased the degree of grid intelligence, and malicious attacks on smart grids have also increased year by year. the wrong data injection attack launched by the attacker will destroy the integrity of the data by changing the data of the sensor and controller, which will lead to the wrong decision of the control system and even paralyze the power transmission network. this paper uses the measured values of smart grid sensors as samples, analyzes the attack vectors maliciously injected by attackers and the statistical characteristics of system data, and proposes a false data injection attack detection strategy. It is considered that the measured values of sensors have spatial distribution characteristics, the Gaussian mixture model of grid node feature vectors is obtained by training sample values, the test measurement values are input into the Gaussian mixture model, and the knowledge of clustering is used to detect whether the power grid is malicious data attacks. the power supplies of IEEE-18 and IEEE-30 simulation systems was tested, and the influence of the system statistical measurement characteristics on the detection accuracy was analyzed. the results show that the proposed strategy has better detection performance than the support vector machine method.

关键词： Smart power grids

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Loss Constrains Added Squeeze and Excitation Blocks for Pruning Deep Neural Networks 16

Loss Constrains Added Squeeze and Excitation Blocks for Prun...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Wang, Yiqin Li, Ming Su, Hongye Xie, Lei Li, Xiaochen Xu, Weihua Xu, Xiaozhou Zhejiang Univ Hangzhou 310007 Peoples R China Zhejiang Univ Coll Control Sci & Engn Inst Cyber Syst & Control Hangzhou Peoples R China

ISBN: (纸本)9781728177090

Deep neural networks are proved to be very effective to solve problems on image classification, object detection and segmentation. However, in cases where only limited hardware is acquired, it may be a problem to deploy big models with excellent performance as they are sometimes calculation consuming. To overcome the limits on power, memory and calculation, channel pruning is proposed to compress the model in channel wise and soon become a common approach to have big models compressed. Generally, pruning is a three-stage pipeline containing training, pruning and finetuning. In this work, we come up with a new pruning approach that needs no finetuning. the major idea is extracting channel saliences by squeeze and excitation block and pushing the salience to either 0 or 1 by a sin-based function. then take the salience as criteria for pruning. As the criteria of our approach is activation rather than trainable parameter, finetuning is not necessary in our pruning strategy which make the pruning process more stable and time saving. Experiment on flowers demonstrates our new designed pruning method is effective on reducing the model scale while maintaining the overall accuracy.

关键词： Deep neural networks

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Efficient Objects Identification for a KidSize Humanoid Soccer Robot: Using Feedforward Neural Networks with Image Segmentation 16

Efficient Objects Identification for a KidSize Humanoid Socc...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Jeronimo, Gilmar Correia Botelho, Wagner Tanaka Pimentel, Edson Pinheiro Maia dos Santos, thaynara Cristina Fed Univ ABC UFABC Ctr Engn Modelling & Appl Social Sci CECS Santo Andre SP Brazil Fed Univ ABC UFABC Ctr Math Computat & Cognit CMCC Santo Andre SP Brazil

ISBN: (纸本)9781728177090

One of the biggest problems faced in robot soccer tournaments is related to object identification in the soccer field. the ball, field marks, crossbars of the goal, the opponent players or teammates are considered objects and should be identified by the robot soccer player in the category of KidSize RoboCup Humanoid League. the main target of this paper is to present a robotic architecture composed of the Software and Hardware Modules responsible for identifying these objects. the Software Module was composed of a DataBase (DB) with images of the objects to be identified and a Feedforward Neural Network (FNN) trained by the Neural Network Toolbox (NNT) of the MATrix LABoratory (MATLAB). In order to integrate the identification process with the Hardware Module, it was necessary to develop the NeuralNet library. this library was implemented in JAVA, making it compatible with multiple platforms. the purpose of this library was to transfer the FNN already trained by the NNT to the Raspberry Pi 3B. the Raspberry Pi 3B was responsible for processing the images captured by the vision System of the robotic player. Also, all the objects in the field were identified through the trained FNN and the Open Source Computer vision (OpenCV) library. the results showed an efficiency of about 82% in ball identification, 92% in field marks, 81% in crossbars of the goal, and 93% in opponent players or teammates.

关键词： Object recognition Neural networks vision for robots

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Enhancing Adaptive Event-Triggered Protocols for Multi-Agent Consensus with External Disturbances 16

Enhancing Adaptive Event-Triggered Protocols for Multi-Agent...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Li, Xianwei Tang, Yang Zhu, Bing Li, Shaoyuan Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Beihang Univ Res Div 7 Beijing 100191 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China

ISBN: (纸本)9781728177090

this paper studies the design of adaptive event-triggered protocols for consensus of linear multi-agent systems (MASs) with external disturbances. Different from most of the existing results that deal with undirected graphs, this paper addresses directed graphs, specifically graphs that are assumed to be strongly connected. Inspired by a recent development [1], this paper devises novel adaptive event-triggered protocols for linear MASs with all agents subject to external disturbances. Two specific designs of composite triggering conditions are analysed and discussed. Compared with the disturbance-free case, additional constraints need to be introduced to deal with external disturbances and moreover the time-dependent terms are allowed to take any finite functions, rather than a class of L-1 functions.

关键词： Multi agent systems

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Sparse Causal Residual Neural Network for Linear and Nonlinear Concurrent Causal Inference and Root Cause Diagnosis 16

Sparse Causal Residual Neural Network for Linear and Nonline...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Chen, Jiawei Zhao, Chunhui Sun, Youxian Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

ISBN: (纸本)9781728177090

Reliable and effective fault diagnosis methods are necessary for complex industrial processes that consists of various units. After a process fault is detected, it remains a challenging task to locate the root cause unit and determine the propagation path of the fault. In this paper, a novel method, termed Sparse Causal Residual Neural Network (SCRNN), is proposed and applied for modern industrial root cause diagnosis. the advantage of SCRNN lies in that it can not only recognize linear and nonlinear causal relationships in parallel, but also automatically determine the causality lags and deduce the time delay of causal transmission. Besides, due to the specially designed sparse constraint and optimization algorithm, the SCRNN model can realize the function of key dependent variable selection, avoiding the high computational complexity and complicated procedure brought by pairwise comparison. the feasibility of the proposed method is illustrated through the benchmark TE process.

关键词： Fault detection

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Self-initialized Visual Servoing for Accurate End-effector Positioning 6

Self-initialized Visual Servoing for Accurate End-effector P...

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6th international conference on control, automation and robotics (ICCAR)

作者： Neuberger, Bernhard Patten, Timothy Park, Kim Vincze, Markus TU Wien Automat & Control Inst Vis Robot Lab Vienna Austria

ISBN: (纸本)9781728161396

vision-based control of an end-effector is important for industrial and domestic robotic applications. Direct positioning based on object recognition or pose estimation output is challenging due to inaccuracies of the estimations and system calibration. Visual servoing overcomes these inaccuracies, however, present techniques require initialization of a target region in the input image. this work presents an integrated system to servo towards a target with automatic initialization. In our two-step approach, the first phase positions the end-effector relative to an object using the output of a pose estimation network. the initial coarse alignment is then refined in a second phase using image-based visual servoing (IBVS). the target region in the image for IBVS is initialized by the detection of the pose estimator. the system monitors the tracking quality of the IBVS to automatically switch from the first to the second phase. Experiments with a 7 degree of freedom robotic arm show that our approach results in successful end-effector positioning from arbitrary initial positions.

关键词： visual servoing vision-based control robot vision robotic system

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Robot Locomotion control Using Central Pattern Generator with Non-linear Bio-mimetic Neurons

Robot Locomotion Control Using Central Pattern Generator wit...

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international conference on Autonomous Robots and Agents, ICARA

作者： Vijay Shankaran Vivekanand Shahin Hashemkhani Shanmuga Venkatachalam Rajkumar Kubendran Electrical and Computer Engg. University of Pittsburgh Pittsburgh USA ECE and Bioengineering University of Pittsburgh Pittsburgh USA

Central pattern generators (CPG) generate rhythmic gait patterns that can be tuned to exhibit various locomotion behaviors like walking, trotting, etc. CPGs inspired by biology have been implemented previously in robotics to generate periodic motion patterns. this paper aims to take the inspiration even further to present a novel methodology to control movement of a four-legged robot using a non-linear bio-mimetic neuron model. In contrast to using regular leaky integrate and fire (LIF) neurons to create coupled neural networks, our design uses non-linear neurons constituting a mixed-feedback (positive and negative) control system operating at multiple timescales (fast, slow and ultraslow ranging from sub-ms to seconds), to generate a variety of spike patterns that control the robotic limbs and hence its gait. the use of spikes as motor control signals allows for low memory usage and low latency operation of the robot. Unlike LIF neurons, the bio-mimetic neurons are also jitter tolerant making the CPG network more resilient and robust to perturbations in the input stimulus. As a proof of concept, we implemented our model on the Petoi Bittle bot, a quadruped pet dog robot and were able to reliably observe different modes of locomotion-walk, trot and jump. Four bio-mimetic neurons forming a CPG network to control the four limbs were implemented on Arduino microcontroller and compared to a similar CPG built using four LIF neurons. the differential equations for both neurons were solved real-time on Arduino and profiled for memory usage, latency and jitter tolerance. the CPG using bio-mimetic non-linear neurons used marginally higher memory (378 bytes, 18% higher than LIF neurons), incurred insignificant latency of 3.54ms compared to motor activation delay of 200ms, while providing upto 5-10x higher jitter tolerance.

关键词： Legged locomotion Biological system modeling Neurons Memory management Jitter Real-time systems Generators

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