* Corresponding Author
Hide
Show all
Navigasi Robot Mobile Pada Lingkungan Tak Pasti Dengan Pendekatan Behavior Based Control
https://doi.org/10.22146/ijeis.44751 Ilona Usuman(1*), Widodo Prijodiprodjo(2), Prima Asmara Sejati(3)
(1) (Scopus ID : 57191193710); Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(2) Department of Computer Science and Electronics, Universitas Gadjah Mada, Yogyakarta
(3) Departemen Teknik Elektro dan Informatika, Sekolah Vokasi UGM, Yogyakarta
(*) Corresponding Author
Abstract
Robots have been widely used to reach difficult environments or terrain such as disaster areas, wilderness and ruins of buildings. However, to reach these areas, there are many constraints on the limitations of robotic navigation because of the dynamic terrain. Therefore, a behavioral based control algorithm is needed that can make robots adapt flexibly to their environment.
On the scheme of this behavior based control the robot moves based on its tasks. Each task is defined as robotic behavior. Each behavior take input from the sensor and send output to the effector. At each behavior there is a sensor as input for robot that work according to the stages in navigation to overcome uncertain obstacles. The results of the study show that robot can explore, avoid obstacles and reach the final destination.
Keywords
Full Text:
PDFReferences
[1] T. P. Fries, “Autonomous robot navigation in diverse terrain using a fuzzy evolutionary technique,” Proc. IECON 2018 - 44th Annu. Conf. IEEE Ind. Electron. Soc., vol. 1, pp. 5618–5623, 2018.
[2] C. Wang, S. Member, W. Chi, Y. Sun, S. Member, and M. Q. Meng, “Road Map Construction,” pp. 1–12, 2019.
[3] T. Yan and Y. Zhang, “Mobile Robots Location and Mapping Based on Corner Features,” Proc. - 2018 5th Int. Conf. Inf. Sci. Control Eng. ICISCE 2018, pp. 833–838, 2019.
[4] W. Khaksar, Z. Uddin, and J. Torresen, “Self-Adjusting Roadmaps : A Fast Sampling-Based Path Planning Algorithm for Navigation in Unknown Environments,” 2018 IEEE Int. Conf. Robot. Biomimetics, pp. 1094–1101, 2018.
[5] A. Gianibelli, I. Carlucho, M. De Paula, and G. G. Acosta, “An obstacle avoidance system for mobile robotics based on the virtual force field method,” pp. 1–8, 2019.
[6] A. Meylani et al., “Different Types of Fuzzy Logic in Obstacles Avoidance of Mobile Robot,” Proc. 2018 Int. Conf. Electr. Eng. Comput. Sci. ICECOS 2018, vol. 17, pp. 93–100, 2019.
[7] I. Ayari and A. Chatti, “Reactive Control Using Behavior Modelling of a Mobile Robot,” Int. J. Comput. Commun. Control, vol. 2, no. 3, p. 217, 2016.
[8] T. T. Van Nguyen, M. D. Phung, and Q. V. Tran, “Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization,” Int. J. Control Autom., vol. 10, no. 2, pp. 349–364, 2017.
[9] S. Saha, R. Lahiri, A. Konar, and A. K. Nagar, “Gesture Driven Remote Robot Manoeuvre for Unstructured Environment,” Proc. 2018 IEEE Symp. Ser. Comput. Intell. SSCI 2018, pp. 1793–1800, 2019.
[10] M. N. Nicolescu and M. J. Matarić, “A hierarchical architecture for behavior-based robots,” p. 227, 2004.
DOI: https://doi.org/10.22146/ijeis.44751
Article Metrics
Abstract views : 3303
| 
views : 3022
Refbacks
- There are currently no refbacks.
Copyright (c) 2019 IJEIS (Indonesian Journal of Electronics and Instrumentation Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1