Mobile robot vavigation using a vision based approach

PhD ThesisThis study addresses the issue of vision based mobile robot navigation in a partially cluttered indoor environment using a mapless navigation strategy. The work focuses on two key problems, namely vision based obstacle avoidance and vision based reactive navigation strategy. The estimation of optical flow plays a key role in vision based obstacle avoidance problems, however the current view is that this technique is too sensitive to noise and distortion under real conditions. Accordingly, practical applications in real time robotics remain scarce. This dissertation presents a novel methodology for vision based obstacle avoidance, using a hybrid architecture. This integrates an appearance-based obstacle detection method into an optical flow architecture based upon a behavioural control strategy that includes a new arbitration module. This enhances the overall performance of conventional optical flow based navigation systems, enabling a robot to successfully move around without experiencing collisions. Behaviour based approaches have become the dominant methodologies for designing control strategies for robot navigation. Two different behaviour based navigation architectures have been proposed for the second problem, using monocular vision as the primary sensor and equipped with a 2-D range finder. Both utilize an accelerated version of the Scale Invariant Feature Transform (SIFT) algorithm. The first architecture employs a qualitative-based control algorithm to steer the robot towards a goal whilst avoiding obstacles, whereas the second employs an intelligent control framework. This allows the components of soft computing to be integrated into the proposed SIFT-based navigation architecture, conserving the same set of behaviours and system structure of the previously defined architecture. The intelligent framework incorporates a novel distance estimation technique using the scale parameters obtained from the SIFT algorithm. The technique employs scale parameters and a corresponding zooming factor as inputs to train a neural network which results in the determination of physical distance. Furthermore a fuzzy controller is designed and integrated into this framework so as to estimate linear velocity, and a neural network based solution is adopted to estimate the steering direction of the robot. As a result, this intelligent iv approach allows the robot to successfully complete its task in a smooth and robust manner without experiencing collision. MS Robotics Studio software was used to simulate the systems, and a modified Pioneer 3-DX mobile robot was used for real-time implementation. Several realistic scenarios were developed and comprehensive experiments conducted to evaluate the performance of the proposed navigation systems. KEY WORDS: Mobile robot navigation using vision, Mapless navigation, Mobile robot architecture, Distance estimation, Vision for obstacle avoidance, Scale Invariant Feature Transforms, Intelligent framework

A new algorithm for optimal solution of fixed charge transportation problem

summary:Fixed charge transportation problem (FCTP) is a supply chain problem. In this problem, in addition to the cost per unit for each transported product, a fixed cost is also required. The aim is to carry out the transportation process at the lowest possible cost. As with all supply chain problems, this problem may have one, two, or three stages. An algorithm that can find the optimal solution for the problem in polynomial time is not known, even if it is a single-stage problem. For this reason, new algorithms have been proposed in recent years to provide an approximate solution for the problem. The vast majority of these algorithms are meta-heuristic algorithms. In this study, we propose a new heuristic algorithm to find an optimal solution for the 1-stage FCTP. We compare the results of our algorithm with the results of other existing algorithms

Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer

Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.publishedVersionPeer reviewe