(Universitas Ahmad Dahlan, Indonesia)(2) * Riky Dwi Puriyanto
(Universitas Ahmad Dahlan, Indonesia)(3) Wahyu Caesarendra
(Universiti Brunei Darusalam, Brunei Darussalam;
Opole University of Technology, Poland)*corresponding author
AbstractMobile robots need path-planning abilities to achieve a collision-free trajectory. Obstacles between the robot and the goal position must be passed without crashing into them. The Artificial Potential Field (APF) algorithm is a method for robot path planning that is usually used to control the robot for avoiding obstacles in front of the robot. The APF algorithm consists of an attractive potential field and a repulsive potential field. The attractive potential fields work based on the predetermined goals that are generated to attract the robot to achieve the goal position. Apart from it, the obstacle generates a repulsive potential field to push the robot away from the obstacle. The robot's localization in producing the robot's position is generated by the differential drive kinematic equations of the mobile robot based on encoder and gyroscope data. In addition, the mapping of the robot's work environment is embedded in the robot's memory. According to the experiment's results, the mobile robot's differential drive can pass through existing obstacles. In this research, four test environments represent different obstacles in each environment. The track length is 1.5 meters. The robot's tolerance to the goal is 0.1 m, so when the robot is in the 1.41 m position, the robot's speed is 0 rpm. The safe distance between the robot and the obstacle is 0.2 m, so the robot will find a route to get away from the obstacle when the robot reaches that safe distance. The speed of the resulting robot decreases as the distance between the robot and the destination gets closer according to the differential drive kinematics equation of the mobile robot.
KeywordsMobile robot; Artificial Potential Field; Kinematics model; Path planning; Dynamic environment
|
DOIhttps://doi.org/10.31763/ijrcs.v3i2.944 |
Article metrics10.31763/ijrcs.v3i2.944 Abstract views : 1401 | PDF views : 538 |
Cite |
Full Text Download
|
References
[1] R. Siegwart and I. R. Nourbakhsh, Introduction to Autonomous Mobile Robots. MIT Press, 2004, https://books.google.co.id/books?id=4of6AQAAQBAJ.
[2] S. Nurmaini and B. Tutuko, “Intelligent robotics navigation system: Problems, methods, and algorithm,” Int. J. Electr. Comput. Eng., vol. 7, no. 6, pp. 3711–3726, 2017, https://doi.org/10.11591/ijece.v7i6.pp3711-3726.
[3] H. Y. Zhang, W. M. Lin, and A. X. Chen, “Path planning for the mobile robot: A review,” Symmetry (Basel)., vol. 10, no. 10, 2018, https://doi.org/10.3390/sym10100450.
[4] A. N. Atiyah, N. Adzhar, and N. I. Jaini, “An overview: On path planning optimization criteria and mobile robot navigation,” J. Phys. Conf. Ser., vol. 1988, no. 1, 2021, https://doi.org/10.1088/1742-6596/1988/1/012036.
[5] A. Koubaa et al., Robot Path Planning and Cooperation: Foundations, Algorithms and Experimentations. Springer Cham, 2018, https://doi.org/10.1007/978-3-319-77042-0.
[6] G. Li, A. Yamashita, H. Asama, and Y. Tamura, “An efficient improved artificial potential field based regression search method for robot path planning,” 2012 IEEE Int. Conf. Mechatronics Autom. ICMA 2012, pp. 1227–1232, 2012, https://doi.org/10.1109/ICMA.2012.6283526.
[7] P. Wang, S. Gao, L. Li, B. Sun, and S. Cheng, “Obstacle avoidance path planning design for autonomous driving vehicles based on an improved artificial potential field algorithm,” Energies, vol. 12, no. 12, 2019, https://doi.org/10.3390/en12122342.
[8] S. Wang, T. Zhao, and W. Li, “Mobile Robot Path Planning Based on Improved Artificial Potential Field Method,” 2018 IEEE Int. Conf. Intell. Robot. Control Eng. IRCE 2018, pp. 257–262, 2018, https://doi.org/10.1109/IRCE.2018.8492951.
[9] P. Yan, Z. Yan, H. Zheng, and J. Guo, “Real Time Robot Path Planning Method Based on Improved Artificial Potential Field Method,” Proc. 37th Chinese Control Conf., vol. 37, no. July 25-27, 2018, Wuhan, China, pp. 4814–4820, 2018, https://doi.org/10.23919/ChiCC.2018.8482571.
[10] S. M. H. Rostami, A. K. Sangaiah, J. Wang, and X. Liu, “Obstacle avoidance of mobile robots using modified artificial potential field algorithm,” Eurasip J. Wirel. Commun. Netw., vol. 2019, no. 1, 2019, https://doi.org/10.1186/s13638-019-1396-2.
[11] Z. Liu and T. Jiang, “Route planning based on improved artificial potential field method,” 2017 2nd Asia-Pacific Conf. Intell. Robot Syst. ACIRS 2017, pp. 196–199, 2017, https://doi.org/10.1109/ACIRS.2017.7986092.
[12] X. Gu, M. Han, W. Zhang, G. Xue, G. Zhang, and Y. Han, “Intelligent vehicle path planning based on improved artificial potential field Algorithm,” 2019 Int. Conf. High Perform. Big Data Intell. Syst. HPBD IS 2019, no. 2016, pp. 104–109, 2019, https://doi.org/10.1109/HPBDIS.2019.8735451.
[13] W. Li, C. Yang, Y. Jiang, X. Liu, and C. Y. Su, “Motion planning for omnidirectional wheeled mobile robot by potential field method,” J. Adv. Transp., vol. 2017, 2017, https://doi.org/10.1155/2017/4961383.
[14] Iswanto, A. Ma’arif, O. Wahyunggoro, and A. I. Cahyadi, “Artificial potential field algorithm implementation for quadrotor path planning,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 8, pp. 575–585, 2019, https://doi.org/10.14569/IJACSA.2019.0100876.
[15] Z. Yujiang and L. Huilin, “Research on mobile robot path planning based on improved artificial potential field,” Math. Model. Eng., vol. 3, no. 2, pp. 135–144, 2017, https://doi.org/10.21595/mme.2017.19520.
[16] Y. Singh, S. Sharma, R. Sutton and D. Hatton “Path Planning of an Autonomous Surface Vehicle based on Artificial Potential Fields in a Real Time Marine Environment,” 16th International Conference on Computer and IT Applications in the Maritime Industries (COMPIT 2017), pp. 1–10, 2017, http://hdl.handle.net/10026.1/8717.
[17] A. Lazarowska, “A Discrete Artificial Potential Field for Ship Trajectory Planning,” J. Navig., vol. 73, no. 1, pp. 233–251, 2020, https://doi.org/10.1017/S0373463319000468.
[18] P. Sudhakara, V. Ganapathy, B. Priyadharshini, and K. Sundaran, “Obstacle Avoidance and Navigation Planning of a Wheeled Mobile Robot using Amended Artificial Potential Field Method,” Procedia Comput. Sci., vol. 133, pp. 998–1004, 2018, https://doi.org/10.1016/j.procs.2018.07.076.
[19] W. Siming, Z. Tiantian, and L. Weijie, “Mobile Robot Path Planning Based on Improved Artificial Potential Field Method,” 2018 IEEE Int. Conf. Intell. Robot. Control Eng., vol. 2, no. 1, pp. 29–33, 2018, https://doi.org/10.1109/IRCE.2018.8492951.
[20] K. Gao, D. Yan, F. Yang, J. Xie, L. Liu, R. Du, and N. Xiong, “Conditional artificial potential field-based autonomous vehicle safety control with interference of lane changing in mixed traffic scenario,” Sensors (Switzerland), vol. 19, no. 19, 2019, https://doi.org/10.3390/s19194199.
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 Maulana Muhammad Jogo Samodro, Riky Dwi Puriyanto
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia