Multi Robot Exploration through Pruning Frontiers

2012 ◽  
Vol 462 ◽  
pp. 609-616 ◽  
Author(s):  
Anshika Pal ◽  
Ritu Tiwari ◽  
Anupam Shukla
Keyword(s):  
Open Space ◽  
Clustering Algorithm ◽  
Unsupervised Clustering ◽  
Search And Rescue ◽  
Computational Time ◽  
Navigation Strategy ◽  
Key Issues ◽  
The Individual ◽  
Target Locations ◽  
Multi Robot

In this paper, an approach to multi robot exploration is presented. One of the key issues in multi robot exploration is how to assign target locations to the individual robots and how to better distribute the robots over the environment. The proposed technique applies a well-known unsupervised clustering algorithm (k-means) in order to fairly divide the space into as many disjoint regions as available robots. Hungarian Method is used for the assignment of robots to the individual regions with the task to explore the corresponding area. To drive the robots around the environment, a frontier ‘regions on the boundary between open space and unexplored space’ based navigation strategy is used to decide where to move next, according to the data collected so far. Furthermore, we discuss improvements to the frontier based exploration strategy, by pruning the frontier cells that further reduces the computational time. The numbers of candidate locations are evaluated based on three criteria: number of unknown cells, number of known cells and real path travelling cost. Simulations are presented to show the performance of the proposed technique. This method can best be applied in search and rescue operations, partitioning helps to explore different regions of the workspace parallely by different robots instead of concentrating efforts in particular spot, pruning helps to make movement decisions much faster, the result is that the potential victims in a region will not have to wait much longer.

Fast graph clustering in large-scale systems based on spectral coarsening

2021 ◽  
pp. 2150131
Author(s):  
Dasong Sun
Keyword(s):  
Complex Networks ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Graph Clustering ◽  
Superior Performance ◽  
Computational Time ◽  
Single Node ◽  
Multiple Datasets ◽  
Spectral Algorithm ◽  
The Individual

Complex networks depict the individual relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or clusters. However, the existing algorithms are with high complexity, which cost much computational time. In this paper, an efficient graph clustering algorithm based on spectral coarsening is proposed, to deal with the large time complexity of the traditional spectral algorithm. We first find the subset most possibly belonged to the same cluster in the original network, and merge them into a single node. The scale of the network will decrease with the network being coarsened. Then, the spectral clustering algorithm is performed on the coarsened network with the maintained advantages and the improved time efficiency. Finally, the experimental results on the multiple datasets demonstrate that the proposed algorithm, compared with the current state-of-the-art methods, has superior performance.

A Comparative Study of Ant and Genetic Algorithms in Digital Mammography

2018 ◽  
pp. 194-200
Author(s):  
J. Magelin Mary ◽  
Chitra K. ◽  
Y. Arockia Suganthi
Keyword(s):  
Genetic Algorithm ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Processing Technique ◽  
Input Image ◽  
Image Processing Technique ◽  
Computational Time ◽  
Image Mining ◽  
The Individual

Image processing technique in general, involves the application of signal processing on the input image for isolating the individual color plane of an image. It plays an important role in the image analysis and computer version. This paper compares the efficiency of two approaches in the area of finding breast cancer in medical image processing. The fundamental target is to apply an image mining in the area of medical image handling utilizing grouping guideline created by genetic algorithm. The parameter using extracted border, the border pixels are considered as population strings to genetic algorithm and Ant Colony Optimization, to find out the optimum value from the border pixels. We likewise look at cost of ACO and GA also, endeavors to discover which one gives the better solution to identify an affected area in medical image based on computational time.

Practitioner Review: Continuity and Change: The Fair Work Act in Aviation

2011 ◽  
Vol 53 (3) ◽  
pp. 392-401 ◽  
Author(s):  
Sue Bussell ◽  
John Farrow
Keyword(s):  
Industrial Relations ◽  
Safety Net ◽  
Aviation Industry ◽  
Continuity And Change ◽  
National Debate ◽  
New Development ◽  
Key Issues ◽  
The Individual ◽  
The Impact

This article begins by discussing the specific industrial relations challenges of the highly competitive aviation industry. It then reflects on the outcome of the recent intense national debate over industrial relations, exploring the consequences of that debate for practice and policy, and discusses some key issues that remain in play. Although the Fair Work Act 2009 may have come about as a reaction to what many perceive as the ‘excesses’ of Work Choices, the new Act does not so much ‘wind back the clock’ as represent a significant new development in Australia’s long and unique industrial relations history. This article will discuss the impact of the changes, to date, made by the Fair Work Act on one organization, including the expansion of the ‘safety net’, and how the new compromise between the role of the ‘collective’ and the role of the ‘individual’ struck by the Act has the potential to fundamentally change the nature and structure of bargaining. We offer these comments as practitioners who have worked under successive industrial relations regimes since the early 1980s.

Multi-robot search and rescue team

2011 ◽  
Author(s):  
Cai Luo ◽  
Andre Possani Espinosa ◽  
Danu Pranantha ◽  
Alessandro De Gloria
Keyword(s):  
Search And Rescue ◽  
Rescue Team ◽  
Multi Robot

CLUSTERING USING AN IMPROVED HYBRID GENETIC ALGORITHM

2007 ◽  
Vol 16 (06) ◽  
pp. 919-934
Author(s):  
YONGGUO LIU ◽  
XIAORONG PU ◽  
YIDONG SHEN ◽  
ZHANG YI ◽  
XIAOFENG LIAO
Keyword(s):  
Genetic Algorithm ◽  
Clustering Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Sum Of Squares ◽  
Clustering Methods ◽  
Clustering Problem ◽  
Mutation Operation ◽  
Iteration Methods ◽  
Genetic Clustering ◽  
The Individual

In this article, a new genetic clustering algorithm called the Improved Hybrid Genetic Clustering Algorithm (IHGCA) is proposed to deal with the clustering problem under the criterion of minimum sum of squares clustering. In IHGCA, the improvement operation including five local iteration methods is developed to tune the individual and accelerate the convergence speed of the clustering algorithm, and the partition-absorption mutation operation is designed to reassign objects among different clusters. By experimental simulations, its superiority over some known genetic clustering methods is demonstrated.

462 Redefining Positive Airway Pressure Adherence Phenotypes Utilizing Deep Neural Networks and Unsupervised Clustering

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A182-A182
Author(s):  
Yoav Nygate ◽  
Sam Rusk ◽  
Chris Fernandez ◽  
Nick Glattard ◽  
Nathaniel Watson ◽  
...  
Keyword(s):  
Airway Pressure ◽  
Clustering Algorithm ◽  
Positive Airway Pressure ◽  
Treatment Success ◽  
Unsupervised Clustering ◽  
Additional Patient ◽  
Apnea Hypopnea Index ◽  
Obstructive Sleep ◽  
Usage Patterns ◽  
Usage Data

Abstract Introduction Improving positive airway pressure (PAP) adherence is crucial to obstructive sleep apnea (OSA) treatment success. We have previously shown the potential of utilizing Deep Neural Network (DNN) models to accurately predict future PAP usage, based on predefined compliance phenotypes, to enable early patient outreach and interventions. These phenotypes were limited, based solely on usage patterns. We propose an unsupervised learning methodology for redefining these adherence phenotypes in order to assist with the creation of more precise and personalized patient categorization. Methods We trained a DNN model to predict PAP compliance based on daily usage patterns, where compliance was defined as the requirement for 4 hours of PAP usage a night on over 70% of the recorded nights. The DNN model was trained on N=14,000 patients with 455 days of daily PAP usage data. The latent dimension of the trained DNN model was used as a feature vector containing rich usage pattern information content associated with overall PAP compliance. Along with the 455 days of daily PAP usage data, our dataset included additional patient demographics such as age, sex, apnea-hypopnea index, and BMI. These parameters, along with the extracted usage patterns, were applied together as inputs to an unsupervised clustering algorithm. The clusters that emerged from the algorithm were then used as indicators for new PAP compliance phenotypes. Results Two main clusters emerged: highly compliant and highly non-compliant. Furthermore, in the transition between the two main clusters, a sparse cluster of struggling patients emerged. This method allows for the continuous monitoring of patients as they transition from one cluster to the other. Conclusion In this research, we have shown that by utilizing historical PAP usage patterns along with additional patient information we can identify PAP specific adherence phenotypes. Clinically, this allows focus of PAP adherence program resources to be targeted early on to patients susceptible to treatment non-adherence. Furthermore, the transition between the two main phenotypes can also indicate when personalized intervention is necessary to maximize treatment success and outcomes. Lastly, providers can transition patients in the highly non-compliant group more quickly to alternative therapies. Support (if any):

scholarly journals Handwritten English Character Recognition and translate English to Devnagari Words

2019 ◽  
pp. 142-151
Author(s):  
Shivali Parkhedkar ◽  
Shaveri Vairagade ◽  
Vishakha Sakharkar ◽  
Bharti Khurpe ◽  
Arpita Pikalmunde ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Character Recognition ◽  
Computational Time ◽  
Feature Vectors ◽  
Form Processing ◽  
Knn Classifier ◽  
Gabor Feature ◽  
Handwritten Document ◽  
The Individual

In our proposed work we will accept the challenges of recognizing the words and we will work to win the challenge. The handwritten document is scanned using a scanner. The image of the scanned document is processed victimization the program. Each character in the word is isolated. Then the individual isolated character is subjected to “Feature Extraction” by the Gabor Feature. Extracted features are passed through KNN classifier. Finally we get the Recognized word. Character recognition is a process by which computer recognizes handwritten characters and turns them into a format which a user can understand. Computer primarily based pattern recognition may be a method that involves many sub process. In today’s surroundings character recognition has gained ton of concentration with in the field of pattern recognition. Handwritten character recognition is beneficial in cheque process in banks, form processing systems and many more. Character recognition is one in all the favored and difficult space in analysis. In future, character recognition creates paperless environment. The novelty of this approach is to achieve better accuracy, reduced computational time for recognition of handwritten characters. The proposed method extracts the geometric features of the character contour. These features are based on the basic line types that forms the character skeleton. The system offers a feature vector as its output. The feature vectors so generated from a training set, were then used to train a pattern recognition engine based on Neural Networks so that the system can be benchmarked. The algorithm proposed concentrates on the same. It extracts totally different line varieties that forms a specific character. It conjointly also concentrates on the point options of constant. The feature extraction technique explained was tested using a Neural Network which was trained with the feature vectors obtained from the proposed method.

scholarly journals Design and Development of IoT enabled Multi Robot System for Search and Rescue Mission

2018 ◽  
Vol 10 (2) ◽  
pp. 51 ◽  
Author(s):  
Rajesh Singh ◽  
Rohit Samkaria ◽  
Anita Gehlot ◽  
Sushabhan Choudhary
Keyword(s):  
Search And Rescue ◽  
Design And Development ◽  
Robot System ◽  
Rescue Mission ◽  
Multi Robot
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