Cover Image

Mobile Robot Path Planning in a Trajectory with Multiple Obstacles Using Genetic Algorithms

Wahyu Rahmaniar, Amalia Eka Rakhmania

Abstract


Path planning is an essential algorithm to help robots complete their task in the field quickly. However, some path planning algorithms are computationally expensive and cannot adapt to new environments with a distinctly different set of obstacles. This paper presents optimal path planning based on a genetic algorithm (GA) that is proposed to be carried out in a dynamic environment with various obstacles. First, the points of the feasible path are found by performing a local search procedure. Then, the points are optimized to find the shortest path. When the optimal path is calculated, the position of the points on the path is smoothed to avoid obstacles in the environment. Thus, the average fitness values and the GA generation are better than the traditional method. The simulation results show that the proposed algorithm successfully finds the optimal path in an environment with multiple obstacles. Compared to a traditional GA-based method, our proposed algorithm has a smoother route due to path optimization. Therefore, this makes the proposed method advantageous in a dynamic environment.

Keywords


genetic algorithm; mobile robot; optimal path; path planning; shortest path

Full Text:

PDF

References


H. M. Gross, A. Scheidig, S. Müller, B. Schütz, C. Fricke, and S. Meyer, “Living with a mobile companion robot in your own apartment - Final implementation and results of a 20-weeks field study with 20 seniors,” in Proc. of IEEE Int. Conf. Robot. Autom., 2019, pp. 2253–2259, 2019.

C. J. Lai and C. P. Tsai, “Design of introducing service robot into catering services,” in Proc. of ACM Int. Conf. Proceeding Ser., 2018, pp. 62–66.

S. Meghana, T. V. Nikhil, R. Murali, S. Sanjana, R. Vidhya, and K. J. Mohammed, “Design and implementation of surveillance robot for outdoor security,” in Proc. of IEEE Int. Conf. Recent Trends Electron. Inf. Commun. Technol. Proc., 2017, pp. 1679–1682.

M. Takagi, “Japanese society: where humans and robots coexist,” Int. J. Soc. Sci. Humanit., vol. 10, no. 1, pp. 13–16, 2020.

W. Rahmaniar and A. E. Rakhmania, “Online digital image stabilization for an unmanned aerial vehicle (UAV),” J. Robot. Control, vol. 2, no. 4, pp. 234–239, 2021.

S. M. B. P. Samarakoon, M. A. V. J. Muthugala, A. Vu Le, and M. R. Elara, “HTetro-infi: A Reconfigurable floor cleaning robot with infinite morphologies,” IEEE Access, vol. 8, pp. 69816–69828, 2020.

K. Junge, J. Hughes, T. G. Thuruthel, and F. Iida, “Improving robotic cooking using batch bayesian optimization,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 760–765, 2020.

G. Wilson et al., “Robot-enabled support of daily activities in smart home environments,” Cogn. Syst. Res., vol. 54, pp. 258–272, 2019.

M. Niemelä, P. Heikkilä, and H. Lammi, “A social service robot in a shopping mall,” in Proc. of ACM/IEEE International Conference on Human-Robot Interaction, 2017, pp. 227–228.

W. Rahmaniar and A. Wicaksono, “Design and implementation of a mobile robot for carbon monoxide monitoring,” J. Robot. Control, vol. 2, no. 1, 2021.

W. Rahmaniar, W. Wang, and H. Chen, “Real-time detection and recognition of multiple moving objects for aerial surveillance,” Electronics, vol. 8, no. 12, pp. 1373–1390, 2019.

Y. Chen, J. Liang, Y. Wang, Q. Pan, J. Tan, and J. Mao, “Autonomous mobile robot path planning in unknown dynamic environments using neural dynamics,” Soft Comput., vol. 24, no. 18, pp. 13979–13995, 2020.

H. T. Nguyen, H. X. Le, and V. Nam, “Path planning and obstacle avoidance approaches for mobile robot,” Int. J. Comput. Sci. Issues, vol. 13, no. 4, pp. 1–10, 2016.

N. Sariff and N. Buniyamin, “An overview of autonomous mobile robot path planning algorithms,” in Proc. of 4th Student Conf. Res. Dev. "Towards Enhancing Res. Excell. Reg., 2006, pp. 183–188, 2006.

L. Sun, X. Liu, and M. Leng, “An effective algorithm of shortest path planning in a static environment,” Int. Fed. Inf. Process., vol. 207, pp. 257–262, 2006.

J. Bae and W. Chung, “Efficient path planning for multiple transportation robots under various loading conditions,” Int. J. Adv. Robot. Syst., vol. 16, no. 2, pp. 1–9, 2019.

D. Connell and H. M. La, “Dynamic path planning and replanning for mobile robots using RRT,” in Proc. of IEEE Int. Conf. Syst. Man, Cybern, 2017, pp. 1429–1434, 2017.

K. Wei and B. Ren, “A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm,” Sensors, vol. 18, no. 2, 2018.

G. Nagib and W. Gharieb, “Path planning for a mobile robot using genetic algorithms,” in Proc. of Int. Conf. Electr. Electron. Comput. Eng., 2004, pp. 185–189, 2004.

A. Ghorbani, S. Shiry, and A. Nodehi, “Using genetic algorithm for a mobile robot path planning,” in Proc. of Int. Conf. Futur. Comput. Commun., 2009, pp. 164–166.

J. Ma, Y. Liu, S. Zang, and L. Wang, “Robot path planning based on genetic algorithm fused with continuous bezier optimization,” Comput. Intell. Neurosci., vol. 2020, pp. 1–10, 2020.

A. Tuncer and M. Yildirim, “Dynamic path planning of mobile robots with improved genetic algorithm,” Comput. Electr. Eng., vol. 38, no. 6, pp. 1564–1572, 2012.

J. Xin, J. Zhong, F. Yang, Y. Cui, and J. Sheng, “An improved genetic algorithm for path-planning of unmanned surface vehicle,” Sensors, vol. 19, no. 11, pp. 1–23, 2019.

A. V. Le et al., “Complete path planning for a tetris-inspired self-reconfigurable robot by the genetic algorithm of the traveling salesman problem,” Electron., vol. 7, no. 12, pp. 1–21, 2018.

C. Messom, “Genetic algorithms for auto-tuning mobile robot motion control,” Research Lett. in the Inf. and Math. Sci., vol. 3, pp. 129-134, 2002.

C. Lamini, S. Benhlima, and A. Elbekri, “Genetic algorithm based approach for autonomous mobile robot path planning,” in Procedia Computer Science, 2018, vol. 127, pp. 180–189.

F. A. Afsar, M. Arif, and M. Hussain, “Genetic algorithm based path planning and optimization for autonomous mobile robots with morphological preprocessing,” in Proc. of IEEE International Multitopic Conference, 2006, pp. 182–187.

Y. Xue and J. Q. Sun, “Solving the path planning problem in mobile robotics with the multi-objective evolutionary algorithm,” Appl. Sci., vol. 8, no. 9, 2018.

T. Tometzki and S. Engell, “Systematic initialization techniques for hybrid evolutionary algorithms for solving two-stage stochastic mixed-integer programs,” IEEE Trans. Evol. Comput., vol. 15, no. 2, pp. 196–214, Apr. 2011.

K. Rakesh and K. Mahesh, “Exploring genetic algorithm for shortest path optimization in data networks.” Global J. Comp. Sci. Tech., vol. 10, no. 11, pp. 8-12, 2010.

M. Muthiah and A. Saad, “Multi robot path planning and path coordination using genetic algorithms,” in Proc. of SouthEast Conf. ACMSE, 2017, pp. 112–119.

S. Das and S. Sarvottamananda, “Computing the minkowski sum of convex polytopes in $Re^d$.” arXiv:1811.05812, 2018.

Y. Xue, “Mobile robot path planning with a non-dominated sorting genetic algorithm,” Appl. Sci., vol. 8, no. 11, 2018.




DOI: https://doi.org/10.18196/jrc.v3i1.11024

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Wahyu Rahmaniar, Amalia Eka Rakhmania

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

Journal of Robotics and Control (JRC)

P-ISSN: 2715-5056 || E-ISSN: 2715-5072
Organized by Peneliti Teknologi Teknik Indonesia
Published by Universitas Muhammadiyah Yogyakarta in collaboration with Peneliti Teknologi Teknik Indonesia, Indonesia and the Department of Electrical Engineering
Website: http://journal.umy.ac.id/index.php/jrc
Email: jrcofumy@gmail.com

Kuliah Teknik Elektro Terbaik