Design of Servo Control System for Mobile Robot Based on UAV Global Vision
Pages 125 - 130
Abstract
When ground reconnaissance mobile robots perform tasks alone, they can only obtain ground view perception, which leads to the inability to globally optimize, resulting in local rather than global optimal solutions. Inspired by this, a servo control system for mobile robots based on UAV global vision is designed in this paper. Firstly, acquire global visual images and process them, selecting target points via mouse under global vision. Secondly, plan an obstacle avoidance path to reach the target point using artificial potential field method, and smooth the path using B-spline curves. Then, establish the kinematic model of the mobile robot and use the LQR algorithm to enable the robot to track the path. Finally, experimental tests were conducted via the Webots simulation platform to verify the feasibility of the system. Experimental results show that the system can ensure high accuracy and smooth path tracking, and has strong cooperative control, effectively addressing the single visual problem of ground mobile robots.
References
[1]
O Amidi. 2002. Integrated Air/Ground Vehicle System for Semi-Autonomous Off-Road Navigation. In AUVSI Symposium, July 9-11, 2002.
[2]
Ivana Budinská and Štefan Havlík. 2016. Task allocation within a heterogeneous multi-robot system. In 2016 Cybernetics & Informatics (K&I). IEEE, 1–5.
[3]
Luiz Chaimowicz, Ben Grocholsky, James F Keller, Vijay Kumar, and Camillo J Taylor. 2004. Experiments in multirobot air-ground coordination. In IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA’04. 2004, Vol. 4. IEEE, 4053–4058.
[4]
Wendi Ding, Xinyi Chen, Wenxin Zhu, and Qinyuan Ren. 2021. Vision-Based Formation Control for a Heterogeneous Multi-Robot System. In 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 1791–1796.
[5]
Jinxiang Feng, Bo Yang, Xiaofei Pei, and Pengwei Zhou. 2021. Research on path planning and control of driverless logistics train. In 2021 5th CAA International Conference on Vehicular Control and Intelligence (CVCI). IEEE, 1–6.
[6]
Mario Garzón, Joao Valente, David Zapata, and Antonio Barrientos. 2013. An aerial–ground robotic system for navigation and obstacle mapping in large outdoor areas. Sensors 13, 1 (2013), 1247–1267.
[7]
Yang Li, Bin Tian, Yuanjun Yang, and Changde Li. 2022. Path planning of robot based on artificial potential field method. In 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC), Vol. 6. IEEE, 91–94.
[8]
Cai Luo, Andre Possani Espinosa, Alessandro De Gloria, and Roberto Sgherri. 2011. Air-ground multi-agent robot team coordination. In 2011 IEEE International Conference on Robotics and Automation. IEEE, 6588–6591.
[9]
Zongchen Qin, Shuqin Xiang, Shaoxin Sun, Yuliang Cai, and Song Wang. 2023. Formation Control and Obstacle Avoidance for Multi-Robot Systems. In 2022 First International Conference on Cyber-Energy Systems and Intelligent Energy (ICCSIE). IEEE, 1–6.
[10]
Marcos Rodríguez, Abdulla Al-Kaff, Ángel Madridano, David Martín, and Arturo de la Escalera. 2020. Wilderness search and rescue with heterogeneous multi-robot systems. In 2020 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 110–116.
[11]
Anthony Stentz. 1994. Optimal and efficient path planning for partially-known environments. In Proceedings of the 1994 IEEE international conference on robotics and automation. IEEE, 3310–3317.
[12]
Anthony Stentz 1995. The focussed d⌃* algorithm for real-time replanning. In IJCAI, Vol. 95. 1652–1659.
[13]
Mathilde Theunissen, Hugo Pousseur, P Castillo, and A Correa Victorino. 2023. Cooperative architecture using air and ground vehicles for the search and recognition of targets. In 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 1355–1360.
[14]
Zicong Wu, Weizhou Su, and Junhui Li. 2019. Multi-robot path planning based on improved artificial potential field and B-spline curve optimization. In 2019 Chinese Control Conference (CCC). IEEE, 4691–4696.
[15]
Chihiro Yokoyama, Munehiro Takimoto, and Yasushi Kambayashi. 2013. Cooperative control of multi-robots using mobile agents in a three-dimensional environment. In 2013 IEEE International Conference on Systems, Man, and Cybernetics. IEEE, 1115–1120.
[16]
Wanbing Zhao, Hao Liu, Yan Wan, and Zongli Lin. 2022. Data-driven formation control for multiple heterogeneous vehicles in air–ground coordination. IEEE Transactions on Control of Network Systems 9, 4 (2022), 1851–1862.
Index Terms
- Design of Servo Control System for Mobile Robot Based on UAV Global Vision
Recommendations
Vision Aided Path Planning for Mobile Robot
ICCCE '14: Proceedings of the 2014 International Conference on Computer and Communication EngineeringPath planning is very important for autonomous mobile robots to navigate from the beginning to the ending position. Vision aided path planning for mobile robot system is discussed in this paper. The paper reveals the accounts from a historical overview ...
Obstacle avoidance of a mobile robot using vision system and ultrasonic sensor
ICIC'07: Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applicationsThis paper proposes obstacle collision avoidance algorithm for a mobile robot. To navigate without colliding with obstacles the mobile robot uses both the vision system approach and edge detection approach using ultrasonic sensor. The vision system ...
Neural network-based approaches for mobile robot navigation in static and moving obstacles environments
Mobile robots can travel by acquiring the information using sensor-actuator control techniques from surrounding and perform several tasks. Due to the ability of traversing, mobile robots are used in different application for different places. In the ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
June 2024
186 pages
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 21 August 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Youth Innovation Science and Technology Support Plan of Colleges in Shandong Province under Grant
- Pilot projects for the integration of science, education, and industry
Conference
RobCE 2024
RobCE 2024: 2024 4th International Conference on Robotics and Control Engineering
June 27 - 29, 2024
Edinburgh, United Kingdom
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 16Total Downloads
- Downloads (Last 12 months)16
- Downloads (Last 6 weeks)2
Reflects downloads up to 02 Mar 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format