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

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

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

Past, present and future of path-planning algorithms for mobile robot navigation in dynamic environments

Mobile robots have been making a significant contribution to the advancement of many sectors including automation of mining, space, surveillance, military, health, agriculture and many more. Safe and efficient navigation is a fundamental requirement of mobile robots, thus, the demand for advanced algorithms rapidly increased. Mobile robot navigation encompasses the following four requirements: perception, localization, path-planning and motion control. Among those, path-planning is a vital part of a fast, secure operation. During the last couple of decades, many path-planning algorithms were developed. Despite most of the mobile robot applications being in dynamic environments, the number of algorithms capable of navigating robots in dynamic environments is limited. This paper presents a qualitative comparative study of the up-to-date mobile robot path-planning methods capable of navigating robots in dynamic environments. The paper discusses both classical and heuristic methods including artificial potential field, genetic algorithm, fuzzy logic, neural networks, artificial bee colony, particle swarm optimization, bacterial foraging optimization, ant-colony and Agoraphilic algorithm. The general advantages and disadvantages of each method are discussed. Furthermore, the commonly used state-of-the-art methods are critically analyzed based on six performance criteria: algorithm's ability to navigate in dynamically cluttered areas, moving goal hunting ability, object tracking ability, object path prediction ability, incorporating the obstacle velocity in the decision, validation by simulation and experimentation. This investigation benefits researchers in choosing suitable path-planning methods for different applications as well as identifying gaps in this field. © 2020 IEEE

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

Robot Path Planning with IGA-MMAS and MMAS-IGA

Path Planning of mobile robots is one of the essential tasks in robotic research and studies with intelligent technologies. It helps in determining the path from a source to the destination. It has extended its roots from classic approaches to further improvements over time, such as evolutionary approaches. Ant Colony Optimization (ACO) and Genetic algorithm are well known evolutionary approaches in effective path planning. This research work focuses on the Max-Min Ant System (MMAS) derived from the ACO evolutionary approach of Ant System (AS) and Improved Genetic Algorithm (IGA) which is efficient over the classical Genetic Algorithm. In-order to study robot path planning two methods are combined in this research work combining MMAS and IGA as two-hybrid methods MMAS-IGA and IGA-MMAS . The results of the two-hybrid methods will be deriving the near optimal solution, demonstrated in the experimental study of this work. Grid maps are used for simulating the robot path planning environment which is modeled using the grid method. Genetic operators of IGA are combined with MMAS for the enhancement of the overall result of the methods IGA-MMAS and MMAS-IGA. The effectiveness of these two methods will be determined in the simulation modeled using MATLAB environment. The experimental results of these methods are done in a static environment and the results of MMAS-IGA and IGA-MMAS are compared to the path planning method GA-ACO

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 Real-time Global Optimal Path Planning for mobile robot in Dynamic Environment Based on Artificial Immune Approach

This paper illustrates a method to finding a globaloptimal path in a dynamic environment of known obstacles foran Mobile Robot (MR) to following a moving target. Firstly, theenvironment is defined by using a practical and standard graphtheory. Then, a suboptimal path is obtained by using DijkstraAlgorithm (DA) that is a standard graph searching method. Theadvantages of using DA are; elimination the uncertainness ofheuristic algorithms and increasing the speed, precision andperformance of them. Finally, Continuous Clonal SelectionAlgorithm (CCSA) that is combined with Negative SelectionAlgorithm (NSA) is used to improve the suboptimal path andderive global optimal path. To show the effectiveness of themethod it is compared with some other methods in this area

An Analysis Review: Optimal Trajectory for 6-DOF-based Intelligent Controller in Biomedical Application

With technological advancements and the development of robots have begun to be utilized in numerous sectors, including industrial, agricultural, and medical. Optimizing the path planning of robot manipulators is a fundamental aspect of robot research with promising future prospects. The precise robot manipulator tracks can enhance the efficacy of a variety of robot duties, such as workshop operations, crop harvesting, and medical procedures, among others. Trajectory planning for robot manipulators is one of the fundamental robot technologies, and manipulator trajectory accuracy can be enhanced by the design of their controllers. However, the majority of controllers devised up to this point were incapable of effectively resolving the nonlinearity and uncertainty issues of high-degree freedom manipulators in order to overcome these issues and enhance the track performance of high-degree freedom manipulators. Developing practical path-planning algorithms to efficiently complete robot functions in autonomous robotics is critical. In addition, designing a collision-free path in conjunction with the physical limitations of the robot is a very challenging challenge due to the complex environment surrounding the dynamics and kinetics of robots with different degrees of freedom (DoF) and/or multiple arms. The advantages and disadvantages of current robot motion planning methods, incompleteness, scalability, safety, stability, smoothness, accuracy, optimization, and efficiency are examined in this paper