A Comparative Study of Optimization Algorithms for Global Path Planning of Mobile Robots

Yıl 2021, , 417 - 428, 15.04.2021

Mustafa Yusuf Yıldırım , Rüştü Akay

https://doi.org/10.16984/saufenbilder.800067

Cited By: 3

Öz

It is an essential issue for mobile robots to reach the target points with optimum cost which can be minimum duration or minimum fuel, depending on the problem. In this paper, it was aimed to develop a software for the optimal path planning of mobile robots in user-defined two-dimensional environments with static obstacles and to analyze the performance of some optimization algorithms for this problem using this software. The developed software is designed to create obstacles of different shapes and sizes in the work area and to find the shortest path for the robot using the selected optimization algorithm. Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Genetic Algorithm (GA) were implemented in the software. These algorithms have been tested for optimum path planning in four models with different problem sizes and different difficulty levels. When the results are evaluated, it is observed that the ABC algorithm gives better results than other algorithms in terms of the shortest distance. With this study, the use of optimization algorithms in real-time path planning of land mobile robots or unmanned aerial vehicles can be simulated.

Anahtar Kelimeler

mobile robot, path planning, cubic spline interpolation, optimization algorithms, simulation

Kaynakça

• [1] Y. Wang, F. Cai, and Y. Wang, “Dynamic Path Planning for Mobile Robot Based on Particle Swarm Optimization,” AIP Conference Proceedings 1864, 020024, pp. 1–4, 2017.
• [2] M. E. Dere, “Optimum path planning for mobile robots,” MS Thesis, Konya Technical University, 2019.
• [3] E. Bogar, “A Hybrid Optimization Method for Single and Multi-Objective Robot Path Planning Problem,” MS Thesis, Pamukkale University, 2016.
• [4] N. Buniyamin, N. Sariff, W. A. J. Wan Ngah and Z. Mohamad, “Robot Global Path Planning Overview and A Variation of Ant Colony System Algorithm,” International Journal of Mathematics and Computers in Simulation, vol. 1, no. 5, pp. 9–16, 2011.
• [5] M. Alajlan, A. Koubaa, I. Chaari, H. Bennaceur and A. Ammar, “Global Path Planning for Mobile Robots in Large-Scale Grid Environments using Genetic Algorithms,” International Conference on Individual and Collective Behaviors in Robotics (ICBR), pp. 1–8, 2013.
• [6] F. H. Ajeil, I. K. Ibraheem, M. A. Sahib and A. J. Humaidi, “Multi-Objective Path Planning of an Autonomous Mobile Robot using Hybrid PSO-MFB Optimization Algorithm,” Applied Soft Computing, vol. 89, 106076, pp. 1–13, 2020.
• [7] B. Wang, S. Li, J. Guo and Q. Chen, “Car-Like Mobile Robot Path Planning in Rough Terrain using Multi-Objective Particle Swarm Optimization Algorithm,” Neurocomputing, vol. 282, pp. 42–51, 2018.
• [8] L. Zhang, Y. Zhang and Y. Li, “Path Planning for Indoor Mobile Robot Based on Deep Learning,” Optics, vol. 219, 165096, pp. 1–17, 2020.
• [9] H. S. Dewang, P. K. Mohanty and S. Kundu, “A Robust Path Planning for Mobile Robot using Smart Particle Swarm Optimization,” Procedia Computer Science, vol. 133, pp. 290–297, 2018.
• [10] E. S. Low, P. Ong and K. C. Cheah, “Solving The Optimal Path Planning of a Mobile Robot using Improved Q-Learning,” Robotics and Autonomous Systems, vol. 115, pp. 143–161, 2019.
• [11] B. K. Patle, D. R. K. Parhi, A. Jagadeesh and S. K. Kashyap, “Matrix-Binary Codes Based Genetic Algorithm for Path Planning of Mobile Robot,” Computers & Electrical Engineering, vol. 67, pp. 708–728, 2018.
• [12] P. K. Das and P. K. Jena, “Multi-Robot Path Planning using Improved Particle Swarm Optimization Algorithm through Novel Evolutionary Operators,” Applied Soft Computing, vol. 92, 106312, pp. 1–24, 2020.
• [13] R. A. Saeed, D. R. Recupero and P. Remagnino, “A Boundary Node Method for Path Planning of Mobile Robots,” Robotics and Autonomous Systems, vol. 123, 103320, pp. 1–21, 2020.
• [14] M. Nazarahari, E. Khanmirza and S. Doostie, “Multi-Objective Multi-Robot Path Planning in Continuous Environment using an Enhanced Genetic Algorithm,” Expert Systems with Applications, vol. 115, pp. 106–120, 2019.
• [15] M. Saraswathi, G. B. Murali and B. B. V. L. Deepak, “Optimal Path Planning of Mobile Robot using Hybrid Cuckoo Search-Bat Algorithm,” Procedia Computer Science, vol. 133, pp. 510–517, 2018.
• [16] U. O. Rosas, O. Montiel and R. Sepúlveda, “Mobile Robot Path Planning using Membrane Evolutionary Artificial Potential Field,” Applied Soft Computing, vol. 77, pp. 236–251, 2019.
• [17] F. Bayat, S. S. Najafinia and M. Aliyari, “Mobile Robots Path Planning: Electrostatic Potential Field Approach,” Expert Systems with Applications, vol. 100, pp. 68–78, 2018.
• [18] C. Qu, W. Gai, M. Zhong and J. Zhang, “A Novel Reinforcement Learning Based Grey Wolf Optimizer Algorithm for Unmanned Aerial Vehicles (Uavs) Path Planning,” Applied Soft Computing, vol. 89, 106099, pp. 1–12, 2020.
• [19] B. K. Patle, A. Pandey, A. Jagadeesh and D. R. Parhi, “Path Planning in Uncertain Environment by using Firefly Algorithm,” Defence Technology, vol. 14, no. 6, pp. 691–701, 2018.
• [20] P. C. Song, J. S. Pan and S. C. Chu, “A Parallel Compact Cuckoo Search Algorithm for Three-Dimensional Path Planning,” Applied Soft Computing, vol. 94, 106443, pp. 1–16, 2020.
• [21] M. Elhoseny, A. Tharwat and A. E. Hassanien, “Bezier Curve Based Path Planning in A Dynamic Field using Modified Genetic Algorithm,” Journal of Computational Science, vol. 25, pp. 339–350, 2018.
• [22] B. K. Patle, D. R. K. Parhi, A. Jagadeesh and S. K. Kashyap, “Application of Probability to Enhance the Performance of Fuzzy Based Mobile Robot Navigation,” Applied Soft Computing, vol. 75, pp. 265–283, 2019.
• [23] U. Goel, S. Varshney, A. Jain, S. Maheshwari and A. Shukla, “Three Dimensional Path Planning for UAVs in Dynamic Environment using Glow-Worm Swarm Optimization,” Procedia Computer Science, vol. 133, pp. 230–239, 2018.
• [24] H. Li and A. V. Savkin, “An Algorithm for Safe Navigation of Mobile Robots by a Sensor Network in Dynamic Cluttered Industrial Environments,” Robotics and Computer-Integrated Manufacturing, vol. 54, pp. 65–82, 2018.
• [25] D. Karaboga, “Artificial Intelligence Optimization Algorithms,” Nobel Publishing, 2017.
• [26] A. Ayari and S. Bouamama, “A New Multiple Robot Path Planning Algorithm: Dynamic Distributed Particle Swarm Optimization,” Robotics and Biomimetics, vol. 4, no. 8, pp. 1–15, 2017.
• [27] A. Altay, O. Ozkan and G. Kayakutlu, “Prediction of Aircraft Failure Times using Artificial Neural Networks and Genetic Algorithms,” Journal of Aircraft, vol. 51, no. 1, pp. 47–53, 2014.
• [28] M. K. Heris, Particle Swarm Optimization in MATLAB (URL: https://yarpiz.com/50/ypea102-particle-swarm-optimization), 2015.
• [29] M. K. Heris, Artificial Bee Colony in MATLAB (URL: https://yarpiz.com/297/ypea114-artificial-bee-colony), 2015.
• [30] E. Chołodowicz and D. Figurowski, “Mobile Robot Path Planning with Obstacle Avoidance using Particle Swarm Optimization,” Pomiary Automatyka Robotyka, vol. 21, no. 3, pp. 59–68, 2017.
• [31] Y. He, W. J. Ma and J. P. Zhang, “The Parameters Selection of PSO Algorithm Influencing on Performance of Fault Diagnosis,” MATEC Web of Conferences 63-02019, pp. 1–5, 2016.