{"title":"Intelligent Path Planning Approach for Autonomous Mobile Robot","authors":"Ibrahim M. Al-Adwan","doi":"10.20965/jrm.2021.p1423","DOIUrl":null,"url":null,"abstract":"This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.","PeriodicalId":51661,"journal":{"name":"Journal of Robotics and Mechatronics","volume":"33 1","pages":"1423-1428"},"PeriodicalIF":0.9000,"publicationDate":"2021-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Robotics and Mechatronics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20965/jrm.2021.p1423","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ROBOTICS","Score":null,"Total":0}
引用次数: 1
Abstract
This paper presents a new path planning algorithm for an autonomous mobile robot. It is desired that the robot reaches its goal in a known or partially known environment (e.g., a warehouse or an urban environment) and avoids collisions with walls and other obstacles. To this end, a new, efficient, simple, and flexible path finder strategy for the robot is proposed in this paper. With the proposed strategy, the optimal path from the robot’s current position to the goal position is guaranteed. The environment is represented as a grid-based map, which is then divided into a predefined number of subfields to reduce the number of required computations. This leads to a reduction in the load on the controller and allows a real-time response. To evaluate the flexibility and efficiency of the proposed strategy, several tests were simulated with environments of different sizes and obstacle distributions. The experimental results demonstrate the reliability and efficiency of the proposed algorithm.
期刊介绍:
First published in 1989, the Journal of Robotics and Mechatronics (JRM) has the longest publication history in the world in this field, publishing a total of over 2,000 works exclusively on robotics and mechatronics from the first number. The Journal publishes academic papers, development reports, reviews, letters, notes, and discussions. The JRM is a peer-reviewed journal in fields such as robotics, mechatronics, automation, and system integration. Its editorial board includes wellestablished researchers and engineers in the field from the world over. The scope of the journal includes any and all topics on robotics and mechatronics. As a key technology in robotics and mechatronics, it includes actuator design, motion control, sensor design, sensor fusion, sensor networks, robot vision, audition, mechanism design, robot kinematics and dynamics, mobile robot, path planning, navigation, SLAM, robot hand, manipulator, nano/micro robot, humanoid, service and home robots, universal design, middleware, human-robot interaction, human interface, networked robotics, telerobotics, ubiquitous robot, learning, and intelligence. The scope also includes applications of robotics and automation, and system integrations in the fields of manufacturing, construction, underwater, space, agriculture, sustainability, energy conservation, ecology, rescue, hazardous environments, safety and security, dependability, medical, and welfare.