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

Planificación de trayectorias usando metaheurísticas

In this work, a comparison between two metaheuristic methods to solve the path planning problem is presented. These methods are 1) Artificial ant colony and 2) Artificial bee colony. The following metrics are used to evaluate these implementations: 1) Path length and 2) Execution time. The comparison was tested using ten maps obtained from the University of Prague Department of Intelligent Cybernetics and the Mobil Robotics Group. Several runs were carried out to find the best algorithm parameters and get the best algorithm for the route planning task. The best algorithm was the artificial bee colony. These evaluations were visualized using the VPython package; here, a differential mobile robot was simulated to follow the trajectory calculated by the best algorithm. This simulation made it possible to observe that the robot makes the correct trajectory from the starting point to the objective point in each evaluated map.En este trabajo se presenta una comparación entre dos métodos metaheurísticos para resolver problemas de planificación de rutas. Estos métodos son: 1) Colonia de hormigas artificiales y 2) Colonia de abejas artificiales. Para evaluar estas implementaciones, se utilizan las siguientes métricas: 1) Longitud de ruta y 2) Tiempo de ejecución. El comparativo se probó utilizando diez mapas obtenidos del Departamento de Cibernética Inteligente y Mobil Robotics Group de la Universidad de Praga. Se realizaron varias ejecuciones con el objetivo de encontrar los mejores parámetros de los algoritmos y obtener el mejor algoritmo para la tarea de planificación de ruta. El mejor algoritmo fue la colonia de abejas artificiales. Estas evaluaciones se visualizaron utilizando el paquete VPython, aquí se simuló un robot móvil diferencial para seguir la trayectoria calculada por el mejor algoritmo. A partir de esta simulación fue posible observar que el robot realiza la trayectoria correcta desde el punto de inicio hasta el punto objetivo en cada uno de los mapas evaluados

Survey on Path Planning of Mobile Robot with Multi Algorithms

Sensible practical environment for path and continuous motion preparation problems usually involves various operational areas coupled with indoor usage comprising of multiple apartments, corridors, a few doors and several static and active obstacles in between. The disintegration of this system into limited areas or regions indicates an effect on the fun preparation of appropriate pathways in a complex setting. Many algorithms are designed to solve problems with narrow passages and with optimal solution for more than one field. Independent mobile robot gadget would have felt the stability of its abilities, the steadfastness and the question of resilience with the project and the implementation of an innovative as well as an efficient plan with the best approach. Navigation algorithms reaching a certain sophistication in the field of autonomous mobile robot, which ensures that most work now focuses on more specialized activities such as efficient route planning and navigation across complex environments. Adaptive way to prepare and maneuver needs to establish learning thresholds, legislation to identify areas and to specify planned requirements of the library. The aim of this survey is studying many algorithms to view the advantage and disadvantage for each method then can use optimal method depended on this study

Intelligent Robotics Navigation System: Problems, Methods, and Algorithm

This paper set out to supplement new studies with a brief and comprehensible review of the advanced development in the area of the navigation system, starting from a single robot, multi-robot, and swarm robots from a particular perspective by taking insights from these biological systems. The inspiration is taken from nature by observing the human and the social animal that is believed to be very beneficial for this purpose. The intelligent navigation system is developed based on an individual characteristic or a social animal biological structure. The discussion of this paper will focus on how simple agent’s structure utilizes flexible and potential outcomes in order to navigate in a productive and unorganized surrounding. The combination of the navigation system and biologically inspired approach has attracted considerable attention, which makes it an important research area in the intelligent robotic system. Overall, this paper explores the implementation, which is resulted from the simulation performed by the embodiment of robots operating in real environments

A Comprehensive Overview of Classical and Modern Route Planning Algorithms for Self-Driving Mobile Robots

Mobile robots are increasingly being applied in a variety of sectors, including agricultural, firefighting, and search and rescue operations. Robotics and autonomous technology research and development have played a major role in making this possible. Before a robot can reliably and effectively navigate a space without human aid, there are still several challenges to be addressed. When planning a path to its destination, the robot should be able to gather information from its surroundings and take the appropriate actions to avoid colliding with obstacles along the way. The following review analyses and compares 200 articles from two databases, Scopus and IEEE Xplore, and selects 60 articles as references from those articles. This evaluation focuses mostly on the accuracy of the different path-planning algorithms. Common collision-free path planning methodologies are examined in this paper, including classical or traditional and modern intelligence techniques, as well as both global and local approaches, in static and dynamic environments. Classical or traditional methods, such as Roadmaps (Visibility Graph and Voronoi Diagram), Potential Fields, and Cell Decomposition, and modern methodologies such as heuristic-based (Dijkstra Method, A* Algorithms, and D* Algorithms), metaheuristics algorithms (such as PSO, Bat Algorithm, ACO, and Genetic Algorithm), and neural systems such as fuzzy neural networks or fuzzy logic (FL) and Artificial Neural Networks (ANN) are described in this report. In this study, we outline the ideas, benefits, and downsides of modeling and path-searching technologies for a mobile robot

Counterexample Guided Inductive Optimization Applied to Mobile Robots Path Planning (Extended Version)

We describe and evaluate a novel optimization-based off-line path planning algorithm for mobile robots based on the Counterexample-Guided Inductive Optimization (CEGIO) technique. CEGIO iteratively employs counterexamples generated from Boolean Satisfiability (SAT) and Satisfiability Modulo Theories (SMT) solvers, in order to guide the optimization process and to ensure global optimization. This paper marks the first application of CEGIO for planning mobile robot path. In particular, CEGIO has been successfully applied to obtain optimal two-dimensional paths for autonomous mobile robots using off-the-shelf SAT and SMT solvers.Comment: 7 pages, 14rd Latin American Robotics Symposium (LARS'2017

Modified Q-Learning Algorithm for Mobile Robot Path Planning Variation using Motivation Model

Path planning is an essential algorithm in autonomous mobile robots, including agricultural robots, to find the shortest path and to avoid collisions with obstacles. Q-Learning algorithm is one of the reinforcement learning methods used for path planning. However, for multi-robot system, this algorithm tends to produce the same path for each robot. This research modifies the Q-Learning algorithm in order to produce path variations by utilizing the motivation model, i.e. achievement motivation, in which different motivation parameters will result in different optimum paths. The Motivated Q-Learning (MQL) algorithm proposed in this study was simulated in an area with three scenarios, i.e. without obstacles, uniform obstacles, and random obstacles. The results showed that, in the determined scenario, the MQL can produce 2 to 4 variations of optimum path without any potential of collisions (Jaccard similarity = 0%), in contrast to the Q-Learning algorithm that can only produce one optimum path variation. This result indicates that MQL can solve multi-robots path planning problems, especially when the number of robots is large, by reducing the possibility of collisions as well as decreasing the problem of queues. However, the average computational time of the MQL is slightly longer than that of the Q-Learning