Heuristic localization and mapping for active sensing with humanoid robot NAO

The purpose of this thesis is to utilize vision system for autonomous navigation. The platform which has been used was an NAO humanoid robot. More specifically, NAO cameras and its makers have been used to solve the two most fundamental problems of autonomous mobile robots which are localization and mapping the environment. NAO markers have been printed and positioned on virtual walls to construct an experimental environment to investigate proposed localization and mapping methods. In algorithm side, basically NAO uses two known markers to localize itself and averages over all location predicted using each pair of known markers. At the same time NAO calculates the location of any unknown markers and add it to the Map. Moreover, A simple go-to-goal path planning algorithm has been implemented to provide a continuous localization and mapping for longer walks of NAO. The result of this work shows that NAO can navigate in an experimental environment using only its marker and camera and reach a predefined target location successfully. Also, It has been shown that NAO can locate itself with acceptable accuracy and make a feature-based map of markers at each location. This thesis provides a starting point for experimenting with different algorithms in path planning as well as possibility to investigate active sensing methods. Furthermore, the possibility of combining other features with NAO marker can be investigated to provide even more accurate result

RMDL: Random Multimodel Deep Learning for Classification

The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have achieved state-of-the-art results across many domains. RMDL solves the problem of finding the best deep learning structure and architecture while simultaneously improving robustness and accuracy through ensembles of deep learning architectures. RDML can accept as input a variety data to include text, video, images, and symbolic. This paper describes RMDL and shows test results for image and text data including MNIST, CIFAR-10, WOS, Reuters, IMDB, and 20newsgroup. These test results show that RDML produces consistently better performance than standard methods over a broad range of data types and classification problems.Comment: Best Paper award ACM ICISD

Heuristic localization and mapping for active sensing with humanoid robot NAO

The purpose of this thesis is to utilize vision system for autonomous navigation. The platform which has been used was an NAO humanoid robot. More specifically, NAO cameras and its makers have been used to solve the two most fundamental problems of autonomous mobile robots which are localization and mapping the environment. NAO markers have been printed and positioned on virtual walls to construct an experimental environment to investigate proposed localization and mapping methods. In algorithm side, basically NAO uses two known markers to localize itself and averages over all location predicted using each pair of known markers. At the same time NAO calculates the location of any unknown markers and add it to the Map. Moreover, A simple go-to-goal path planning algorithm has been implemented to provide a continuous localization and mapping for longer walks of NAO. The result of this work shows that NAO can navigate in an experimental environment using only its marker and camera and reach a predefined target location successfully. Also, It has been shown that NAO can locate itself with acceptable accuracy and make a feature-based map of markers at each location. This thesis provides a starting point for experimenting with different algorithms in path planning as well as possibility to investigate active sensing methods. Furthermore, the possibility of combining other features with NAO marker can be investigated to provide even more accurate result