THE ACA-BASED PID CONTROLLER FOR ENHANCING A WHEELED-MOBILE ROBOT

Wall-following control of mobile robot is an important topic in the mobile robot researches. The wall-following control problem is characterized by moving the robot along the wall in a desired direction while maintaining a constants distance to the wall. The existing control algorithms become complicated in implementation and not efficient enough. Ant colony algorithm (ACA), in terms of optimizing parameters, has a faster convergence speed and features that are easy to integrate with other methods. This paper adopts ant colony algorithm to optimize PID controller, and then selects ideal control parameters. The simulation results based on MATLAB show that the control system optimized by ant colony algorithm has higher efficiency than the traditional control systems in term of RMSE

Fuzzy Logic-Ant Colony Optimization for Explorer-Follower Robot with Global Optimal Path Planning

Path planning is an essential task for the mobile robot navigation. However, such a task is difficult to solve, due to the optimal path needs to be rerouted in real-time when a new obstacle appears. It produces a sub-optimal path and the robot can be trapped in local minima. To overcome the problem the Ant Colony Optimization (ACO) is combined with Fuzzy Logic Approach to make a globally optimal path. The Fuzzy-ACO algorithm is selected because the fuzzy logic has good performance in imprecision and uncertain environment and the ACO produce simple optimization with an ability to find the globally optimal path. Moreover, many optimization algorithms addressed only at the simulation level. In this research, the real experiment is conducted with the low-cost Explorer-Follower robot. The results show that the proposed algorithm, enables them to successfully identify the shortest path without collision and stack in “local minimaâ€

A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification

Mobil robotların yol planması için metasezgisel hibrit algoritmalar geliştirilmesi ve uygulanması

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Anahtar kelimeler: Meta-sezgisel algoritmalar, Mobil Robotlar, Yol planlama, Matlab, ROS (Robot Operating System) Günümüzde teknolojik gelişmelere paralel olarak, robot teknolojisinin tüm sektörlerde yaygın olarak kullanıldığı görülmektedir. Endüstriyel ve teknik uygulamalarda mobil robotlar güvenilirlik, erişilebilirlik ve maliyet gibi sahip olduğu üstün özelliklerle ön plana çıkmaktadır. Mobil robotlar hareket halinde olduklarından diğer robotlara göre enerji tüketimi daha fazla önem kazanmaktadır. Dolayısıyla mobil robotlar için hedefe ulaşabilecek en kısa yolun belirlenmesi önemli bir çalışma alanı olmuştur. Bu kapsamda, mobil robotlarda yol planlama algoritmalarının geliştirilmesi üzerine çalışmalara odaklanılmış olup konu hala güncelliğini korumaktadır. Bu tez çalışmasının amacı, meta-sezgisel algoritma tabanlı mobil robot yol planlaması için özgün bir hibrit algoritma geliştirmektir. İlk olarak meta-sezgisel tabanlı yol planlama algoritmaları incelenerek daha etkili ve özgün bir hibrit algoritmanın geliştirilmesi için zemin hazırlandı. Kullanılan algoritmaların amacı meta-sezgisel algoritmaların verimliliğini ve yol planlamada yolun minimize edilerek belirlenen zaman, maliyet gibi en önemli performans kriterlerini geliştirmektir. Bu çalışmada öncelikle tek bir tepe kamerası kullanılarak görüntü düzleminden alınan görüntü işlenerek mobil robotların ve engellerin koordinatları alınmaktadır. Daha sonra üzerinde engeller bulunan statik bir ortamda tekli ve çoklu mobil robotların yol planlaması için özgün hibrit bir algoritma geliştirildi ve uygulandı. PSO(Parçacık Sürü Optimizasyonu), FA (Ateş Böceği Algoritması), CS (Guguk Kuşu Algoritması) gibi meta-sezgisel algoritmalardan her biri ayrı ayrı kullanılarak elde edilen sonuçlar benzer çalışmalarla karşılaştırıldı. Daha sonra aynı ortam şartlarında robotlar için yol planlamalar yapmak üzere çalışmaya özgü CS-PSO(Guguk Kuşu AlgoritmasıParçacık Sürü Optimizasyonu), CS-FA(Guguk Kuşu Algoritması-Ateş Böceği Algoritması), CS-PSO-FA (Guguk Kuşu Algoritması-Parçacık Sürü OptimizasyonuAteş Böceği Algoritması) gibi hibrit algoritmalar geliştirildi. Kameradan alınan görüntüyü işleyerek harita oluşturma, algoritmalar ile yol bulma, robot ile haberleşme ve robotun algoritmalar ile belirleyeceği yol bilgisine göre ilerleyebilmesi gibi tüm faaliyetleri kolaylıkla gerçekleştirebilmek amacıyla MATLAB-GUI (Graphical User Interface) tabanlı bir ara yüz tasarlanmıştır. Ayrıca geliştirilen algoritmaları doğrulamak üzere fiziksel ortamda çeşitli uygulamalar gerçekleştirilmekte ve elde edilen sonuçların karşılaştırılması yapılmaktadır. Son olarak geliştirilen CS-PSO-FA hibrit algoritmasıyla elde edilen yolun diğer algoritmalara göre daha kısa olduğu ve böylece daha yüksek bir performansa sahip olduğu kanıtlanmıştır. DEVELOPMENT AND APPLICATION OF META-HEURISTIC HYBRID ALGORITHMS FOR PATH PLANNING OF MOBIL ROBOTSKeywords: Meta-heuristic algorithm, Mobil Robots, Path Planning, Matlab, ROS (Robot Operating System) Today, parallel to technological developments, robot technology seems to be widely used in all sectors. In industrial and technical applications, mobile robots come into prominence with superior features such as reliability, accessibility and cost. Energy consumption is more important for mobile robots than other robots because mobile robots are in motion. Therefore, determining the shortest path to reach the target for mobile robots has become an important field of study. In this context, in mobile robots the development of road planning algorithms has been focused and the issue keep up-to-date.The purpose of this thesis is to develop an authentic hybrid algorithm for meta-heuristic algorithm based mobile robot path planning. Firstly, metaheuristic-based path planning algorithms were examined and the basis for developing a more efficient and authentic hybrid algorithm was prepared. The purpose of the algorithms used is to improve the efficiency of meta-heuristic algorithms and the most important performance criteria such as time, cost, etc., determined by minimizing the path in the path planning. In this study, coordinates of mobile robots and obstacles are taken by processing the image taken from the image plane using a single top camera. In a static environment with obstacles on it, an authentic hybrid algorithm for path planning of single and multiple mobile robots has been developed and implemented. The results were obtained using every meta-heuristic algorithm such as PSO (Particle Swarm Optimization), FA (Firefly Algorithm) and CS (Cuckoo Search Algorithm) separately and these results were compared with similar studies. Then, hybrid algorithms specific to study such as CS-PSO (Cuckoo Search Algorithm - Particle Swarm Optimization), CS-FA (Cuckoo Search Algorithm - Firefly Algorithm) and CS-PSO-FA (Cuckoo Search Algorithm - Particle Swarm Optimization - Firefly Algorithm) were developed to make path planning for robots in the same environment conditions. A MATLAB-GUI (Graphical User Interface) based interface was designed in order to easily perform all activities such as generating maps by processing the images taken from the camera, finding paths with algorithms, communicating with robots, and navigating according to path information that the robot determines with algorithms. In addition, various applications are performed in the physical environment to verify the developed algorithms and the obtained results are compared. In conclusion, it is proved that the path obtained with the developed CS-PSO-FA hybrid algorithm is shorter than the other algorithms and thus has a higher performance

Machine learning assisted optimization with applications to diesel engine optimization with the particle swarm optimization algorithm

A novel approach to incorporating Machine Learning into optimization routines is presented. An approach which combines the benefits of ML, optimization, and meta-model searching is developed and tested on a multi-modal test problem; a modified Rastragin\u27s function. An enhanced Particle Swarm Optimization method was derived from the initial testing. Optimization of a diesel engine was carried out using the modified algorithm demonstrating an improvement of 83% compared with the unmodified PSO algorithm. Additionally, an approach to enhancing the training of ML models by leveraging Virtual Sensing as an alternative to standard multi-layer neural networks is presented. Substantial gains were made in the prediction of Particulate matter, reducing the MMSE by 50% and improving the correlation R^2 from 0.84 to 0.98. Improvements were made in models of PM, NOx, HC, CO, and Fuel Consumption using the method, while training times and convergence reliability were simultaneously improved over the traditional approach

Intelligent Navigational Strategies For Multiple Wheeled Mobile Robots Using Artificial Hybrid Methodologies

At present time, the application of mobile robot is commonly seen in every fields of science and engineering. The application is not only limited to industries but also in thehousehold, medical, defense, transportation, space and much more. They can perform all kind of tasks which human being cannot do efficiently and accurately such as working in hazardous and highly risk condition, space research etc. Hence, the autonomous navigation of mobile robot is the highly discussed topic of today in an uncertain environment. The present work concentrates on the implementation of the Artificial Intelligence approaches for the mobile robot navigation in an uncertain environment. The obstacle avoidance and optimal path planning is the key issue in autonomous navigation, which is solved in the present work by using artificial intelligent approaches. The methods use for the navigational accuracy and efficiency are Firefly Algorithm (FA), Probability- Fuzzy Logic (PFL), Matrix based Genetic Algorithm (MGA) and Hybrid controller (FAPFL,FA-MGA, FA-PFL-MGA).The proposed work provides an effective navigation of single and multiple mobile robots in both static and dynamic environment. The simulational analysis is carried over the Matlab software and then it is implemented on amobile robot for real-time navigation analysis. During the analysis of the proposed controller, it has been noticed that the Firefly Algorithm performs well as compared to fuzzy and genetic algorithm controller. It also plays an important role inbuilding the successful Hybrid approaches such as FA-PFL, FA-MGA, FA-PFL-MGA. The proposed hybrid methodology perform well over the individual controller especially for pathoptimality and navigational time. The developed controller also proves to be efficient when they are compared with other navigational controller such as Neural Network, Ant Colony Algorithm, Particle Swarm Optimization, Neuro-Fuzzy etc

Evolving machine learning and deep learning models using evolutionary algorithms

Despite the great success in data mining, machine learning and deep learning models are yet subject to material obstacles when tackling real-life challenges, such as feature selection, initialization sensitivity, as well as hyperparameter optimization. The prevalence of these obstacles has severely constrained conventional machine learning and deep learning methods from fulfilling their potentials. In this research, three evolving machine learning and one evolving deep learning models are proposed to eliminate above bottlenecks, i.e. improving model initialization, enhancing feature representation, as well as optimizing model configuration, respectively, through hybridization between the advanced evolutionary algorithms and the conventional ML and DL methods. Specifically, two Firefly Algorithm based evolutionary clustering models are proposed to optimize cluster centroids in K-means and overcome initialization sensitivity as well as local stagnation. Secondly, a Particle Swarm Optimization based evolving feature selection model is developed for automatic identification of the most effective feature subset and reduction of feature dimensionality for tackling classification problems. Lastly, a Grey Wolf Optimizer based evolving Convolutional Neural Network-Long Short-Term Memory method is devised for automatic generation of the optimal topological and learning configurations for Convolutional Neural Network-Long Short-Term Memory networks to undertake multivariate time series prediction problems. Moreover, a variety of tailored search strategies are proposed to eliminate the intrinsic limitations embedded in the search mechanisms of the three employed evolutionary algorithms, i.e. the dictation of the global best signal in Particle Swarm Optimization, the constraint of the diagonal movement in Firefly Algorithm, as well as the acute contraction of search territory in Grey Wolf Optimizer, respectively. The remedy strategies include the diversification of guiding signals, the adaptive nonlinear search parameters, the hybrid position updating mechanisms, as well as the enhancement of population leaders. As such, the enhanced Particle Swarm Optimization, Firefly Algorithm, and Grey Wolf Optimizer variants are more likely to attain global optimality on complex search landscapes embedded in data mining problems, owing to the elevated search diversity as well as the achievement of advanced trade-offs between exploration and exploitation