Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

F. Umam, Muhammad Fuad, Iswanto Suwarno, A. Ma’arif, W. Caesarendra
{"title":"Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot","authors":"F. Umam, Muhammad Fuad, Iswanto Suwarno, A. Ma’arif, W. Caesarendra","doi":"10.18196/jrc.v4i2.17977","DOIUrl":null,"url":null,"abstract":"This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.","PeriodicalId":443428,"journal":{"name":"Journal of Robotics and Control (JRC)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Robotics and Control (JRC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18196/jrc.v4i2.17977","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于立体视觉导航系统的全向机器人避障
研究了移动机器人导航系统中的避障问题。导航系统被认为非常重要,因为机器人必须能够在不发生碰撞的情况下从初始位置控制到目的地。机器人必须能够避开障碍物并到达目的地。之前的一些研究更多地关注预定的固定障碍。这导致研究结果难以应用于真实环境条件,而在真实条件下,障碍物可能是静止的,也可能是行走环境变化引起的移动。本研究的目的是解决机器人的导航行为,以避免障碍。在处理如上所述的复杂问题时,利用神经模糊算法设计了一个控制系统,使机器人在向目的地移动时能够避开障碍物。本文将ANFIS用于避障控制。使用的学习模式是离线学习。在初始步骤中使用映射输入和输出数据。然后对数据进行训练,使其产生非常小的误差。为了支持机器人的运动,使其在避障时更加灵活和平稳,并能实时识别物体,使用了配备立体视觉传感器的三轮全向机器人。贡献是通过利用基于立体视觉传感器的目标和障碍物检测的ANFIS,推进机器人导航系统避障技术的发展。通过15个不同障碍物设置位置的实验,对所提出的控制方法进行了验证。选择这些场景是为了测试避开移动障碍物的能力,这些障碍物可能来自机器人的前部、右侧或左侧。机器人根据给定的速度向左或向右移动障碍物。经过多次不同障碍物位置的测试,机器人在障碍物距离为173 ~ 150 cm的范围内,平均速度为274 mm/s,成功避开了障碍物。在避障过程中,机器人仍然计算机器人面对目标的方向,直到目标角度为0。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
6.30
自引率
0.00%
发文量
0
期刊最新文献
Efficient Path Planning Algorithm for Mobile Robots Performing Floor Cleaning Like Operations Adaptive Cruise Control of the Autonomous Vehicle Based on Sliding Mode Controller Using Arduino and Ultrasonic Sensor Development of Microclimate Data Recorder on Coffee-Pine Agroforestry Using LoRaWAN and IoT Technology Using Learning Focal Point Algorithm to Classify Emotional Intelligence Enhanced Trajectory Tracking of 3D Overhead Crane Using Adaptive Sliding-Mode Control and Particle Swarm Optimization
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1