scholarly journals A novel navigation system for an autonomous mobile robot in an uncertain environment

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Meng-Yuan Chen ◽  
Yong-Jian Wu ◽  
Hongmei He
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Clustering Algorithm ◽  
Modular Design ◽  
Optimal Path ◽  
Surrounding Environment ◽  
Computing Complexity ◽  
The Arts ◽  
Window Approach

Abstract In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

An Integrated Algorithm for Autonomous Navigation of a Mobile Robot in an Unknown Environment

2008 ◽  
Vol 12 (4) ◽  
pp. 328-335 ◽  
Author(s):  
Lee Gim Hee ◽  
◽  
Marcelo H. Ang Jr. ◽  
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Hybrid Method ◽  
Autonomous Navigation ◽  
Prior Information ◽  
Optimal Path ◽  
Unknown Environment ◽  
Global Path Planning ◽  
Local Navigation ◽  
Navigation Algorithms

Global path planning algorithms are good in planning an optimal path in a known environment, but would fail in an unknown environment and when reacting to dynamic and unforeseen obstacles. Conversely, local navigation algorithms perform well in reacting to dynamic and unforeseen obstacles but are susceptible to local minima failures. A hybrid integration of both the global path planning and local navigation algorithms would allow a mobile robot to find an optimal path and react to any dynamic and unforeseen obstacles during an operation. However, the hybrid method requires the robot to possess full or partial prior information of the environment for path planning and would fail in a totally unknown environment. The integrated algorithm proposed and implemented in this paper incorporates an autonomous exploration technique into the hybrid method. The algorithm gives a mobile robot the ability to plan an optimal path and does online collision avoidance in a totally unknown environment.

scholarly journals SISTEM NAVIGASI MOBILE ROBOT DALAM RUANGAN BERBASIS AUTONOMOUS NAVIGATION

2019 ◽  
Vol 4 (2) ◽  
pp. 78 ◽  
Author(s):  
Dwiky Erlangga ◽  
Endang D ◽  
Rosalia H S ◽  
Sunarto Sunarto ◽  
Kuat Rahardjo T.S ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Local Planning ◽  
Localization And Mapping ◽  
Monte Carlo Localization ◽  
Computer Based ◽  
Slam Algorithm ◽  
Main Components ◽  
Window Approach ◽  
Global Planning

<p><em>Autonomous navigation is absolutely necessary in mobile-robotic, which consists of four main components, namely: perception, localization, path-planning, and motion-control. Mobile robots create maps of space so that they can carry out commands to move from one place to another using the autonomous-navigation method. Map making using the Simultaneous-Localization-and-Mapping (SLAM) algorithm that processes data from the RGB-D camera sensor and bumper converted to laser-scan and point-cloud is used to obtain perception. While the wheel-encoder and gyroscope are used to obtain odometry data which is used to construct travel maps with the SLAM algorithm, gmapping and performing autonomous navigation. The system consists of three sub-systems, namely: sensors as inputs, single-board computers for processes, and actuators as movers. Autonomous-navigation is regulated through the navigation-stack using the Adaptive-Monte-Carlo-Localization (AMCL) algorithm for localization and global-planning, while the Dynamic-Window-Approach (DWA) algorithm with Robot-Operating-System-(ROS) for local -planning. The results of the test show the system can provide depth-data that is converted to laser-scan, bumper data, and odometry data to single-board-computer-based ROS so that mobile-controlled teleoperating robots from workstations can build 2-dimensional grid maps with total accuracy error rate of 0.987%. By using maps, data from sensors, and odometry the mobile-robot can perform autonomous-navigation consistently and be able to do path-replanning, avoid static obstacles and continue to do localization to reach the destination point.</em></p>

scholarly journals Mobile Robot Designed with Autonomous Navigation System

2017 ◽  
Vol 910 ◽  
pp. 012036
Author(s):  
Feng An ◽  
Qiang Chen ◽  
Yanfang Zha ◽  
Wenyin Tao
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Autonomous Navigation System

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.

scholarly journals An Integrated Approach to Goal Selection in Mobile Robot Exploration

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1400 ◽  
Author(s):  
Miroslav Kulich ◽  
Jiří Kubalík ◽  
Libor Přeučil
Keyword(s):  
Mobile Robot ◽  
Evolutionary Algorithm ◽  
Autonomous Navigation ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Optimal Path ◽  
Integrated Approach ◽  
Limited Range ◽  
Simulated Environment ◽  
Feasible Solutions

This paper deals with the problem of autonomous navigation of a mobile robot in an unknown2D environment to fully explore the environment as efficiently as possible. We assume a terrestrial mobilerobot equipped with a ranging sensor with a limited range and 360º field of view. The key part of theexploration process is formulated as the d-Watchman Route Problem which consists of two coupledtasks—candidate goals generation and finding an optimal path through a subset of goals—which aresolved in each exploration step. The latter has been defined as a constrained variant of the GeneralizedTraveling Salesman Problem and solved using an evolutionary algorithm. An evolutionary algorithmthat uses an indirect representation and the nearest neighbor based constructive procedure was proposedto solve this problem. Individuals evolved in this evolutionary algorithm do not directly code thesolutions to the problem. Instead, they represent sequences of instructions to construct a feasible solution.The problems with efficiently generating feasible solutions typically arising when applying traditionalevolutionary algorithms to constrained optimization problems are eliminated this way. The proposedexploration framework was evaluated in a simulated environment on three maps and the time needed toexplore the whole environment was compared to state-of-the-art exploration methods. Experimentalresults show that our method outperforms the compared ones in environments with a low density ofobstacles by up to 12.5%, while it is slightly worse in office-like environments by 4.5% at maximum.The framework has also been deployed on a real robot to demonstrate the applicability of the proposedsolution with real hardware.

Sign in / Sign up

Export Citation Format

Share Document