Moving Object Following Control of Six-legged Robot with Four Joint Legs Based on Backstepping Method
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Amruta Vinod Gulalkari | - |
dc.contributor.author | Pandu Sandi Pratama | - |
dc.contributor.author | 김진욱 | - |
dc.contributor.author | 김학경 | - |
dc.contributor.author | 전봉환 | - |
dc.contributor.author | 김상봉 | - |
dc.date.accessioned | 2021-12-08T14:41:02Z | - |
dc.date.available | 2021-12-08T14:41:02Z | - |
dc.date.issued | 20150402 | - |
dc.identifier.uri | https://www.kriso.re.kr/sciwatch/handle/2021.sw.kriso/4312 | - |
dc.description.abstract | This paper proposes a moving object tracking and following algorithm for the six-legged robot (6LR) with four joint legs installed with a Kinect camera sensor based on Kalman filter and backstepping control method. To do this task, the following steps are executed. Firstly, a real 6LR is developed with several interconnected devices such as servomotors, Micro Control Unit(MCU) and IMU sensor. A Kinect camera sensor is installed on the six-legged robot platform for image processing. A candidate blue colored object is selected as an object for detection, tracking and following. Secondly, kinematic modeling modeling of the 6LR is presented. Thirdly, a candidate blue colored object is detected using color-based object detection algorithm. The localcoordinates of the detected object are obtained to provide its position. Then. an object motion modeling is presented to describe the motion of the candidate object. A Kalman filter algorithm based on the presented object motion modeling is implemented to track the position of the detected moving object. Fourthly, a backstepping controller using Lyapunov stability is designed to achieve the object following task. Finally, simulations and experiments are conducted to verify the effectiveness and the performance of the proposed controller for following the candidate moving object. The results show that the proposed controller makes the 6LR to track the candidate object well withowing steps are executed. Firstly, a real 6LR is developed with several interconnected devices such as servomotors, Micro Control Unit(MCU) and IMU sensor. A Kinect camera sensor is installed on the six-legged robot platform for image processing. A candidate blue colored object is selected as an object for detection, tracking and following. Secondly, kinematic modeling modeling of the 6LR is presented. Thirdly, a candidate blue colored object is detected using color-based object detection algorithm. The localcoordinates of the detected object are obtained to provide its position. Then. an object motion modeling is presented to describe the motion of the candidate object. A Kalman filter algorithm based on the presented object motion modeling is implemented to track the position of the detected moving object. Fourthly, a backstepping controller using Lyapunov stability is designed to achieve the object following task. Finally, simulations and experiments are conducted to verify the effectiveness and the performance of the proposed controller for following the candidate moving object. The results show that the proposed controller makes the 6LR to track the candidate object well with | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.title | Moving Object Following Control of Six-legged Robot with Four Joint Legs Based on Backstepping Method | - |
dc.title.alternative | Moving Object Following Control of Six-legged Robot with Four Joint Legs Based on Backstepping Method | - |
dc.type | Conference | - |
dc.citation.title | 수중로봇기술연구회 | - |
dc.citation.volume | 1 | - |
dc.citation.number | 1 | - |
dc.citation.startPage | 47 | - |
dc.citation.endPage | 53 | - |
dc.citation.conferenceName | 수중로봇기술연구회 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(34103) 대전광역시 유성구 유성대로1312번길 32042-866-3114
COPYRIGHT 2021 BY KOREA RESEARCH INSTITUTE OF SHIPS & OCEAN ENGINEERING. ALL RIGHTS RESERVED.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.