Role of Internet of Things and Machine Learning in Finding the Optimal Path for an Autonomous Mobile Robot

2021 ◽  
pp. 383-391
Author(s):  
Dadi Ramesh ◽  
Mohmmad Sallauddin ◽  
Syed Nawaz Pasha ◽  
G. Sunil
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Robot ◽  
Optimal Path ◽  
Autonomous Mobile Robot

scholarly journals PSO based path planner of an autonomous mobile robot

2012 ◽  
Vol 2 (2) ◽  
Author(s):  
B. Deepak ◽  
Dayal Parhi
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Fitness Function ◽  
Optimal Path ◽  
Mobile Robot Navigation ◽  
Autonomous Mobile Robot ◽  
Swarm Optimization ◽  
Novel Approach ◽  
Fitness Value ◽  
Path Planner

AbstractA novel approach based on particle swarm optimization has been presented in this paper for solving mobile robot navigation task. The proposed technique tries to optimize the path generated by an intelligent mobile robot from its source position to destination position in its work space. For solving this problem, a new fitness function has been modelled, which satisfies the obstacle avoidance and optimal path traversal conditions. From the obtained fitness values of each particle in the swarm, the robot moves towards the particle which is having optimal fitness value. Simulation results are provided to validate the feasibility of the developed methodology in various unknown environments.

A New Cockroach Swarm Optimization for Motion Planning of Mobile Robot

2012 ◽  
Vol 263-266 ◽  
pp. 834-838
Author(s):  
Wanhui Liu ◽  
Le Cheng
Keyword(s):  
Mobile Robot ◽  
Motion Planning ◽  
Euclidean Distance ◽  
Optimal Path ◽  
Autonomous Mobile Robot ◽  
Weight Factor ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
Heuristic Information

In this paper, an improved cockroach swarm optimization, called cockroach swarm optimization with expansion gird (CSO-EG), is presented and applied to motion planning of autonomous mobile robot. In CSO-EG, the expansion gird method is used to model workspace. By computing the weight factor, the Euclidean distance from each candidate to the destination cell and the pheromone strength of each candidate cell are use as the heuristic information together. For increasing the variety of path, a random choosing cell strategy is introduced. The simulation experiments demonstrate that the CSO-EG algorithm can quickly get the optimal or near-optimal path in a workspace populated with obstacles.

Optimal Path Planning of an Autonomous Mobile Robot Using Genetic Algorithm

2012 ◽  
Vol 488-489 ◽  
pp. 1747-1751
Author(s):  
V. Vasu ◽  
K. Jyothi Kumar
Keyword(s):  
Mobile Robot ◽  
Hardware Implementation ◽  
Robot Navigation ◽  
Fitness Function ◽  
Optimal Path ◽  
Autonomous Mobile Robot ◽  
Software Simulation ◽  
Optimal Path Planning ◽  
The Past ◽  
Extract Information

An autonomous Mobile Robot (AMR) is a machine able to extract information from its environment and move in a meaningful and purposeful manner. Robot Navigation and Obstacle avoidance are the most important problems in mobile robots. In the past, a number of soft computing algorithms have been designed by many researchers for robot navigation problems but very few are actually implementable because they haven’t considered robot size as parameter. This paper presents software simulation and hardware implementation of navigation of a mobile robot avoiding obstacles and selecting optimal path in a static environment using evolution based Genetic algorithms with robot size as a parameter in fitness function.

scholarly journals Path Planning of an Autonomous Mobile Robot using Enhanced Bacterial Foraging Optimization Algorithm

2017 ◽  
Vol 12 (4) ◽  
pp. 26-35 ◽  
Author(s):  
Nizar Hadi Abbas ◽  
Farah Mahdi Ali
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Target Point ◽  
Autonomous Mobile Robot ◽  
Bacterial Foraging Optimization ◽  
Bacterial Foraging ◽  
Starting Point ◽  
Bacterial Foraging Optimization Algorithm

This paper describes the problem of online autonomous mobile robot path planning, which is consisted of finding optimal paths or trajectories for an autonomous mobile robot from a starting point to a destination across a flat map of a terrain, represented by a 2-D workspace. An enhanced algorithm for solving the problem of path planning using Bacterial Foraging Optimization algorithm is presented. This nature-inspired metaheuristic algorithm, which imitates the foraging behavior of E-coli bacteria, was used to find the optimal path from a starting point to a target point. The proposed algorithm was demonstrated by simulations in both static and dynamic different environments. A comparative study was evaluated between the developed algorithm and other two state-of-the-art algorithms. This study showed that the proposed method is effective and produces trajectories with satisfactory results.

scholarly journals Implementación de un robot móvil para optimizar el flujo documentario en el área de logística usando tecnologías de robótica autónoma basada en la plataforma de software libre ROS

2018 ◽  
Vol 27 (2) ◽  
pp. 93
Author(s):  
Iván A. Calle Flores ◽  
Rider V. Paredes Maraza ◽  
Cristopher Bazan Yaranga ◽  
Aldo A. Guardia Guizado
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Optimal Path ◽  
Initial Position ◽  
Autonomous Mobile Robot ◽  
Navigation Path ◽  
Palabras Clave ◽  
The World ◽  
A Company ◽  
Target Locations

El presente proyecto consistió en la implementación de un robot móvil autónomo capaz de facilitar el flujo de documentos entre las diferentes áreas de una empresa, universidad, etc. Este robot es capaz de navegar de manera completamente autónoma en ambientes reales tal como los ambientes del CTIC, FIM, FIEE, etc. Tan solo especificando el punto inicial, el mapa del ambiente de navegación, y el punto deseado, este robot es capaz de generar el camino óptimo para llegar a dicha meta, y luego seguir este camino con la capacidad de evitar obstáculos si estos se presentan. Dadas estas características, este robot se puede usar en aplicaciones logísticas en donde el robot debe llevar paquetes, cargas, etc., a algún punto especificado por el usuario. En el proyecto se tienen dos modelos, el primer robot llamado R2D2‐R1 puede llevar cargas de hasta 3kg, y el segundo robot llamado R2D2‐R2 puede llevar cargas de hasta 25kg. Cabe señalar que los algoritmos implementados en este proyecto representan el estado del arte del campo de la robótica autónoma, y su desempeño se ha comprobado en las diversas pruebas de navegación realizadas en ambientes de la UNI. Este proyecto contribuye a cumplir con la misión de la UNI en los temas de innovación y gestión tecnológica para contribuir al bienestar de la sociedad y desarrollo del país. Palabras clave.- Robot móvil, navegación autónoma, planificación de trayectorias, evitamiento de obstáculos, aplicaciones logísticas. ABSTRACT The present project is about the implementation of an autonomous mobile robot designed for logistic tasks in different areas of a company, university, etc. This robot is able to navigate autonomously in real environments, you just need to specify the initial position, the grip map of the world and the target locations, and the robot will generate automatically the optimal path to reach the target positions, and then will follow this path while avoiding obstacles such as persons, trash bins, etc. These characteristics allow that our robot can be used in logistic tasks where the robot needs to carry loads from one place to another. In this project we developed two robot models, the first one called R2d2‐R1 can carry loads of up to 3kg, and the second one called can carry loads of up to 25Kg. The algorithms implemented in this project represent the state‐of‐the‐car methods and its performance has been proved in the several experiments carried out with these two robots. Keywords.- Mobile robot, autonomous navigation, path planning, obstacle avoidance, logistic applications.

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