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Year 2021, Volume: 16 Issue: 2, 205 - 214, 15.09.2021

Abstract

References

  • [1] Ma, J., Liu, Y., Zang, S., & Wang, L. (2020). Robot path planning based on genetic algorithm fused with continuous bezier optimization. Computational intelligence and neuroscience, 2020.
  • [2] Pellegrinelli, S., Borgia, S., Pedrocchi, N., Villagrossi, E., Bianchi, G., & Tosatti, L. M. (2015). Minimization of the energy consumption in motion planning for single-robot tasks. Procedia Cirp, 29, 354-359.
  • [3] Gul, F., Rahiman, W., & Alhady, S. S. N. (2019). A comprehensive study for robot navigation techniques. Cogent Engineering, 6(1), 1632046.
  • [4] Zakaria, M. A., Zamzuri, H., Mamat, R., & Mazlan, S. A. (2013). A path tracking algorithm using future prediction control with spike detection for an autonomous vehicle robot. International Journal of Advanced Robotic Systems, 10(8), 309.
  • [5] Zhang, H. Y., Lin, W. M., & Chen, A. X. (2018). Path planning for the mobile robot: A review. Symmetry, 10(10), 450.
  • [6] Gürgüze, G. & Türkoğlu, İ. (2019). Algorithms Used in Robot Systems. Turkish Journal of Nature and Science, 8(1), 17-31.
  • [7] Ibraheem, I. K., & Ajeil, F. H. (2018). Multi-objective path planning of an autonomous mobile robot in static and dynamic environments using a hybrid PSO-MFB optimisation algorithm. arXiv preprint arXiv:1805.00224.
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  • [9] Li, W., & Wang, G. Y. (2010, October). Application of improved PSO in mobile robotic path planning. In 2010 International Conference on Intelligent Computing and Integrated Systems (pp. 45-48). IEEE.
  • [10] Davoodi, M., Panahi, F., Mohades, A., & Hashemi, S. N. (2015). Clear and smooth path planning. Applied Soft Computing, 32, 568-579.
  • [11] Das, P. K., Behera, H. S., & Panigrahi, B. K. (2016). A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation, 28, 14-28.
  • [12] Purcaru, C., Precup, R. E., Iercan, D., Fedorovici, L. O., Petriu, E. M., & Voisan, E. I. (2013, July). Multi-robot GSA-and PSO-based optimal path planning in static environments. In 9th International Workshop on Robot Motion and Control (pp. 197-202). IEEE.
  • [13] Roberge, V., Tarbouchi, M., & Labonté, G. (2012). Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on industrial informatics, 9(1), 132-141.
  • [14] Shivgan, R., & Dong, Z. (2020, May). Energy-Efficient Drone Coverage Path Planning using Genetic Algorithm. In 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR) (pp. 1-6). IEEE.
  • [15] Tao, W., Yan, S., Pan, F., & Li, G. (2020, September). AUV Path Planning Based on Improved Genetic Algorithm. In 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE) (pp. 195-199). IEEE.
  • [16] Abhishek, B., Ranjit, S., Shankar, T., Eappen, G., Sivasankar, P., & Rajesh, A. (2020). Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs. SN Applied Sciences, 2(11), 1-16.
  • [17] Yan, Z., Li, J., Wu, Y., & Zhang, G. (2019). A real-time path planning algorithm for AUV in unknown underwater environment based on combining PSO and waypoint guidance. Sensors, 19(1), 20.
  • [18] Shakiba, R., Najafipour, M., & Salehi, M. E. (2013, April). An improved PSO-based path planning algorithm for humanoid soccer playing robots. In 2013 3rd Joint Conference of AI & Robotics and 5th RoboCup Iran Open International Symposium (pp. 1-6). IEEE.
  • [19] Zhang, H., Zhang, Y., & Yang, T. (2020). A survey of energy-efficient motion planning for wheeled mobile robots. Industrial Robot: the international journal of robotics research and application.
  • [20] Liu, F., He, H., Li, Z., Guan, Z. H., & Wang, H. O. (2020, July). Improved potential field method path planning based on genetic algorithm. In 2020 39th Chinese Control Conference (CCC) (pp. 3725-3729). IEEE.
  • [21] Châari, I., Koubaa, A., Bennaceur, H., Trigui, S., & Al-Shalfan, K. (2012, June). Smart PATH: A hybrid ACO-GA algorithm for robot path planning. In 2012 IEEE congress on evolutionary computation (pp. 1-8). IEEE.
  • [22] Masehian, E., & Sedighizadeh, D. (2010). Multi-objective robot motion planning using a particle swarm optimization model. Journal of Zhejiang University SCIENCE C, 11(8), 607-619.
  • [23] Hafez, A. T., & Kamel, M. A. (2019). Cooperative task assignment and trajectory planning of unmanned systems via HFLC and PSO. Unmanned Systems, 7(02), 65-81.
  • [24] Olabode, A. O., Abdulkareem, B. Q., & Ajao, T. A. Comparative analysis of some selected metaheuristic algorithms for solving intelligent path planning problem of mobile agents.
  • [25] Gürgüze, G., & Türkoğlu, İ. (2019) Position Control of Differential Mobile Robot with Known Dynamic Model with Pure Pursuit Algorithm. International Congress on Human Computer Interaction, Optimization and Robotic Applications (HORA)
  • [26] Hirpo, B. D., & Zhongmin, W. (2017). Design and Control for Differential Drive Mobile Robot. International Journal of Engineering Research & Technology (IJERT), 6(10).

Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning

Year 2021, Volume: 16 Issue: 2, 205 - 214, 15.09.2021

Abstract

In the movement of autonomous mobile robots in static or dynamic environments, one of the important issues sought for a solution is to reach the target with the shortest and safest path without collision. For this purpose, there are many algorithms based on probabilistic, potential field and artificial intelligence. The solutions brought by these algorithms differ according to the dynamics of the environment. However, as is known, the real world environment is complex. As the environment gets more complex, more environment knowledge is required for the performance of the algorithms. Complex mobile robotic systems equipped with sensors are required to obtain environmental information. This causes more energy consumption, processing load and the formation of heavy structures. In order to solve these problems, there are algorithms that perform path planning without the need for all environment information. Two of these are the Genetic Algorithm (GA) and the Particle Swarm Optimization (PSO) algorithm. In this study, the performance of both algorithms was compared according to the object density in the environment. Objects in the environment were detected according to the image information. Distance, time, curvature, and processing speed analyzes were performed in Matlab / Simulink environment according to different density environment scenarios.

References

  • [1] Ma, J., Liu, Y., Zang, S., & Wang, L. (2020). Robot path planning based on genetic algorithm fused with continuous bezier optimization. Computational intelligence and neuroscience, 2020.
  • [2] Pellegrinelli, S., Borgia, S., Pedrocchi, N., Villagrossi, E., Bianchi, G., & Tosatti, L. M. (2015). Minimization of the energy consumption in motion planning for single-robot tasks. Procedia Cirp, 29, 354-359.
  • [3] Gul, F., Rahiman, W., & Alhady, S. S. N. (2019). A comprehensive study for robot navigation techniques. Cogent Engineering, 6(1), 1632046.
  • [4] Zakaria, M. A., Zamzuri, H., Mamat, R., & Mazlan, S. A. (2013). A path tracking algorithm using future prediction control with spike detection for an autonomous vehicle robot. International Journal of Advanced Robotic Systems, 10(8), 309.
  • [5] Zhang, H. Y., Lin, W. M., & Chen, A. X. (2018). Path planning for the mobile robot: A review. Symmetry, 10(10), 450.
  • [6] Gürgüze, G. & Türkoğlu, İ. (2019). Algorithms Used in Robot Systems. Turkish Journal of Nature and Science, 8(1), 17-31.
  • [7] Ibraheem, I. K., & Ajeil, F. H. (2018). Multi-objective path planning of an autonomous mobile robot in static and dynamic environments using a hybrid PSO-MFB optimisation algorithm. arXiv preprint arXiv:1805.00224.
  • [8] Hassani, I., Maalej, I., & Rekik, C. (2018). Robot path planning with avoiding obstacles in known environment using free segments and turning points algorithm. Mathematical Problems in Engineering, 2018.
  • [9] Li, W., & Wang, G. Y. (2010, October). Application of improved PSO in mobile robotic path planning. In 2010 International Conference on Intelligent Computing and Integrated Systems (pp. 45-48). IEEE.
  • [10] Davoodi, M., Panahi, F., Mohades, A., & Hashemi, S. N. (2015). Clear and smooth path planning. Applied Soft Computing, 32, 568-579.
  • [11] Das, P. K., Behera, H. S., & Panigrahi, B. K. (2016). A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation, 28, 14-28.
  • [12] Purcaru, C., Precup, R. E., Iercan, D., Fedorovici, L. O., Petriu, E. M., & Voisan, E. I. (2013, July). Multi-robot GSA-and PSO-based optimal path planning in static environments. In 9th International Workshop on Robot Motion and Control (pp. 197-202). IEEE.
  • [13] Roberge, V., Tarbouchi, M., & Labonté, G. (2012). Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on industrial informatics, 9(1), 132-141.
  • [14] Shivgan, R., & Dong, Z. (2020, May). Energy-Efficient Drone Coverage Path Planning using Genetic Algorithm. In 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR) (pp. 1-6). IEEE.
  • [15] Tao, W., Yan, S., Pan, F., & Li, G. (2020, September). AUV Path Planning Based on Improved Genetic Algorithm. In 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE) (pp. 195-199). IEEE.
  • [16] Abhishek, B., Ranjit, S., Shankar, T., Eappen, G., Sivasankar, P., & Rajesh, A. (2020). Hybrid PSO-HSA and PSO-GA algorithm for 3D path planning in autonomous UAVs. SN Applied Sciences, 2(11), 1-16.
  • [17] Yan, Z., Li, J., Wu, Y., & Zhang, G. (2019). A real-time path planning algorithm for AUV in unknown underwater environment based on combining PSO and waypoint guidance. Sensors, 19(1), 20.
  • [18] Shakiba, R., Najafipour, M., & Salehi, M. E. (2013, April). An improved PSO-based path planning algorithm for humanoid soccer playing robots. In 2013 3rd Joint Conference of AI & Robotics and 5th RoboCup Iran Open International Symposium (pp. 1-6). IEEE.
  • [19] Zhang, H., Zhang, Y., & Yang, T. (2020). A survey of energy-efficient motion planning for wheeled mobile robots. Industrial Robot: the international journal of robotics research and application.
  • [20] Liu, F., He, H., Li, Z., Guan, Z. H., & Wang, H. O. (2020, July). Improved potential field method path planning based on genetic algorithm. In 2020 39th Chinese Control Conference (CCC) (pp. 3725-3729). IEEE.
  • [21] Châari, I., Koubaa, A., Bennaceur, H., Trigui, S., & Al-Shalfan, K. (2012, June). Smart PATH: A hybrid ACO-GA algorithm for robot path planning. In 2012 IEEE congress on evolutionary computation (pp. 1-8). IEEE.
  • [22] Masehian, E., & Sedighizadeh, D. (2010). Multi-objective robot motion planning using a particle swarm optimization model. Journal of Zhejiang University SCIENCE C, 11(8), 607-619.
  • [23] Hafez, A. T., & Kamel, M. A. (2019). Cooperative task assignment and trajectory planning of unmanned systems via HFLC and PSO. Unmanned Systems, 7(02), 65-81.
  • [24] Olabode, A. O., Abdulkareem, B. Q., & Ajao, T. A. Comparative analysis of some selected metaheuristic algorithms for solving intelligent path planning problem of mobile agents.
  • [25] Gürgüze, G., & Türkoğlu, İ. (2019) Position Control of Differential Mobile Robot with Known Dynamic Model with Pure Pursuit Algorithm. International Congress on Human Computer Interaction, Optimization and Robotic Applications (HORA)
  • [26] Hirpo, B. D., & Zhongmin, W. (2017). Design and Control for Differential Drive Mobile Robot. International Journal of Engineering Research & Technology (IJERT), 6(10).
There are 26 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section TJST
Authors

Gürkan Gürgöze 0000-0002-2831-498X

İbrahim Türkoğlu 0000-0003-4938-4167

Publication Date September 15, 2021
Submission Date February 17, 2021
Published in Issue Year 2021 Volume: 16 Issue: 2

Cite

APA Gürgöze, G., & Türkoğlu, İ. (2021). Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning. Turkish Journal of Science and Technology, 16(2), 205-214.
AMA Gürgöze G, Türkoğlu İ. Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning. TJST. September 2021;16(2):205-214.
Chicago Gürgöze, Gürkan, and İbrahim Türkoğlu. “Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning”. Turkish Journal of Science and Technology 16, no. 2 (September 2021): 205-14.
EndNote Gürgöze G, Türkoğlu İ (September 1, 2021) Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning. Turkish Journal of Science and Technology 16 2 205–214.
IEEE G. Gürgöze and İ. Türkoğlu, “Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning”, TJST, vol. 16, no. 2, pp. 205–214, 2021.
ISNAD Gürgöze, Gürkan - Türkoğlu, İbrahim. “Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning”. Turkish Journal of Science and Technology 16/2 (September 2021), 205-214.
JAMA Gürgöze G, Türkoğlu İ. Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning. TJST. 2021;16:205–214.
MLA Gürgöze, Gürkan and İbrahim Türkoğlu. “Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning”. Turkish Journal of Science and Technology, vol. 16, no. 2, 2021, pp. 205-14.
Vancouver Gürgöze G, Türkoğlu İ. Analysis Of The Performance According To Object Density In Static Environments of GA and PSO Algorithms Used In Mobile Robot Path Planning. TJST. 2021;16(2):205-14.