scholarly journals Intelligent optimal control of thermal vision-based Person-Following Robot Platform

2014 ◽  
Vol 18 (3) ◽  
pp. 957-966 ◽  
Author(s):  
Ivan Ciric ◽  
Zarko Cojbasic ◽  
Vlastimir Nikolic ◽  
Tomislav Igic ◽  
Branko Tursnek
Keyword(s):  
Mobile Robot ◽  
Skin Color ◽  
Tracking System ◽  
Human Robot Interaction ◽  
Human Detection ◽  
Support Vector ◽  
Level Control ◽  
Person Following ◽  
Thermal Vision ◽  
Robot Platform

In this paper the supervisory control of the Person-Following Robot Platform is presented. The main part of the high level control loop of mobile robot platform is a real-time robust algorithm for human detection and tracking. The main goal was to enable mobile robot platform to recognize the person in indoor environment, and to localize it with accuracy high enough to allow adequate human-robot interaction. The developed computationally intelligent control algorithm enables robust and reliable human tracking by mobile robot platform. The core of the recognition methods proposed is genetic optimization of threshold segmentation and classification of detected regions of interests in every frame acquired by thermal vision camera. The support vector machine classifier determines whether the segmented object is human or not based on features extracted from the processed thermal image independently from current light conditions and in situations where no skin color is visible. Variation in temperature across same objects, air flow with different temperature gradients, person overlap while crossing each other and reflections, put challenges in thermal imaging and will have to be handled intelligently in order to obtain the efficient performance from motion tracking system.

scholarly journals Thermal vision based intelligent system for human detection and tracking in mobile robot control system

2016 ◽  
Vol 20 (suppl. 5) ◽  
pp. 1553-1559 ◽  
Author(s):  
Ivan Ciric ◽  
Zarko Cojbasic ◽  
Danijela Ristic-Durrant ◽  
Vlastimir Nikolic ◽  
Milica Ciric ◽  
...  
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Robot Control ◽  
Thermal Image ◽  
Human Detection ◽  
Intelligent Algorithms ◽  
Detection And Tracking ◽  
Mobile Robot Control ◽  
Thermal Vision ◽  
Robot Platform

This paper presents the results of the authors in thermal vision based mobile robot control. The most important segment of the high level control loop of mobile robot platform is an intelligent real-time algorithm for human detection and tracking. Temperature variations across same objects, air flow with different temperature gradients, reflections, person overlap while crossing each other, and many other non-linearities, uncertainty and noise, put challenges in thermal image processing and therefore the need of computationally intelligent algorithms for obtaining the efficient performance from human motion tracking system. The main goal was to enable mobile robot platform or any technical system to recognize the person in indoor environment, localize it and track it with accuracy high enough to allow adequate human-machine interaction. The developed computationally intelligent algorithms enables robust and reliable human detection and tracking based on neural network classifier and autoregressive neural network for time series prediction. Intelligent algorithm used for thermal image segmentation gives accurate inputs for classification.

scholarly journals An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces

2019 ◽  
Author(s):  
Mohammad Mortazavi T. ◽  
Omid Mahdi Ebadati E.
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Wide Spectrum ◽  
Region Of Interest ◽  
Human Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Skin Detection ◽  
Color Spaces ◽  
Detection And Localization

Human Skin Detection is one of the most applicable methods in human detection, face detection and so many other detections. These processes can be used in a wide spectrum like industry, medicine, security, etc. The objective of this work is to provide an accurate and efficient method to detect human skin in images. This method can detect and classify skin pixels and reduce the size of images. With the use of RGB and YCbCr color spaces, proposed approach can localize a Region Of Interest (ROI) that contains skin pixels. This method consists of three steps. In the first stage, pre-processing an image like normalization, detecting skin range from the dataset, etc. is done. In the second stage, the proposed method detects candidate’s pixels that are in the range of skin color. In the third stage, with the use of a classifier, it decreases unwanted pixels and areas to decrease the accuracy of the region. The results show 97% sensitivity and 85% specificity for support vector machine classifier.

scholarly journals An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces

2019 ◽  
Author(s):  
Mohammad Mortazavi T. ◽  
Omid Mahdi Ebadati E.
Keyword(s):  
Human Skin ◽  
Skin Color ◽  
Wide Spectrum ◽  
Region Of Interest ◽  
Human Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Skin Detection ◽  
Color Spaces ◽  
Detection And Localization

Human Skin Detection is one of the most applicable methods in human detection, face detection and so many other detections. These processes can be used in a wide spectrum like industry, medicine, security, etc. The objective of this work is to provide an accurate and efficient method to detect human skin in images. This method can detect and classify skin pixels and reduce the size of images. With the use of RGB and YCbCr color spaces, proposed approach can localize a Region Of Interest (ROI) that contains skin pixels. This method consists of three steps. In the first stage, pre-processing an image like normalization, detecting skin range from the dataset, etc. is done. In the second stage, the proposed method detects candidate’s pixels that are in the range of skin color. In the third stage, with the use of a classifier, it decreases unwanted pixels and areas to decrease the accuracy of the region. The results show 97% sensitivity and 85% specificity for support vector machine classifier.

scholarly journals Approach to hand posture recognition based on hand shape features for human–robot interaction

2021 ◽  
Author(s):  
Jing Qi ◽  
Kun Xu ◽  
Xilun Ding
Keyword(s):  
Gaussian Mixture ◽  
Human Robot Interaction ◽  
Polar Coordinates ◽  
Support Vector ◽  
Hand Posture ◽  
Data Set ◽  
Hand Shape ◽  
Hand Posture Recognition ◽  
Hand Segmentation ◽  
Posture Recognition

AbstractHand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.

Autonomous mobile robot platform with multi-variant task-specific end-effector and voice activation

2016 ◽  
Author(s):  
Jonathan Tapia ◽  
Eric Wineman ◽  
Patrick Benavidez ◽  
Aldo Jaimes ◽  
Ethan Cobb ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Autonomous Mobile Robot ◽  
End Effector ◽  
Robot Platform

scholarly journals Practical Model for Energy Consumption Analysis of Omnidirectional Mobile Robot

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1800
Author(s):  
Linfei Hou ◽  
Fengyu Zhou ◽  
Kiwan Kim ◽  
Liang Zhang
Keyword(s):  
Energy Consumption ◽  
Power Consumption ◽  
Mobile Robot ◽  
Load Capacity ◽  
Working Environment ◽  
Energy Consumption Analysis ◽  
Mecanum Wheel ◽  
Save Energy ◽  
Energy Consumption Modeling ◽  
Robot Platform

The four-wheeled Mecanum robot is widely used in various industries due to its maneuverability and strong load capacity, which is suitable for performing precise transportation tasks in a narrow environment. While the Mecanum wheel robot has mobility, it also consumes more energy than ordinary robots. The power consumed by the Mecanum wheel mobile robot varies enormously depending on their operating regimes and environments. Therefore, only knowing the working environment of the robot and the accurate power consumption model can we accurately predict the power consumption of the robot. In order to increase the applicable scenarios of energy consumption modeling for Mecanum wheel robots and improve the accuracy of energy consumption modeling, this paper focuses on various factors that affect the energy consumption of the Mecanum wheel robot, such as motor temperature, terrain, the center of gravity position, etc. The model is derived from the kinematic and kinetic model combined with electrical engineering and energy flow principles. The model has been simulated in MATLAB and experimentally validated with the four-wheeled Mecanum robot platform in our lab. Experimental results show that the accuracy of the model reached 95%. The results of energy consumption modeling can help robots save energy by helping them to perform rational path planning and task planning.

scholarly journals Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 771
Author(s):  
Toshiya Arakawa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Markov Models ◽  
Tracking System ◽  
Video Tracking ◽  
Training Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

scholarly journals Projection Surfaces Detection and Image Correction for Mobile Robots in HRI

2017 ◽  
Vol 2017 ◽  
pp. 1-16
Author(s):  
Enrique Fernández-Rodicio ◽  
Víctor González-Pacheco ◽  
José Carlos Castillo ◽  
Álvaro Castro-González ◽  
María Malfaz ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Vertical Plane ◽  
Vertical Surface ◽  
Human Robot Interaction ◽  
Image Correction ◽  
Robot Interaction ◽  
Kinect Camera ◽  
Fitting Algorithm ◽  
The Right ◽  
Large Groups

Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.

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