Optimum Enerji Verimliliğini Hedefleyen Rastgele Ağaçlar ve Yapay Arı Kolonisi Yöntemi ile Otonom Robotlarda Yol Planlama Algoritması

Year 2019, Volume: 7 Issue: 4, 903 - 915, 24.12.2019

Yunis Torun Züleyha Ergül Ahmet Aksöz

https://doi.org/10.29109/gujsc.607996

Cited By: 2

Abstract

Operatörüz
hareket edebilen robotlarda (otonom robotlar) hareket sırasında engellere
çarpmadan, en kısa yol ve en yumuşak yolu seçerek hedef konumuna ulaşması büyük
önem taşımaktadır. Bu çalışımda, yol planlama eylemi sezgisel ve klasik
yöntemlerinin avantajlarını birleştirmek dezavantajlarını minimize etmek için
iki yöntemin melez kullanımı ile gerçekleştirilmiştir. Klasik yöntemlerden
Rastgele ağaçlar yöntemi (Rapidly-exploring Random Tree-RRT) ve sezgisel
yöntemlerden de Yapay Arı Kolonisi yöntemi (Artificial bee colony-ABC) ayrı
ayrı kullanılarak ve daha sonra melez bir yaklaşımla, önceden keşfedilmiş,
başlangıç ve hedef noktası belli haritada optimum yol, MATLAB’ da Robotik
Sistem Araç Kutusu (Robotic System Toolbox) üzerinden benzetimi
gerçekleştirilmiştir. Sunulan melez algoritmada alınan yol hesaplanırken enerji
verimliği ile birlikte yol güvenliği de dikkate alınmıştır. İki tekerli mobil
robotun enerji tüketimini RRT, ABC ve melez RRT-ABC yöntemlerinin kullanılması
ile elde edilen yollarda hesaplanmış ve karşılaştırılmıştır. Yapılan karşılaştırmalar
sonucunda melez algoritmanın daha verimli çalıştığı gözlemlenmiştir.

Keywords

Rastgele Ağaçlar yöntemi, Yapay Arı Kolonisi algoritması, optimum rota planlama, enerji verimliliği, otonom robot

References

• [1] Klancar, G., Zdesar, A., Blazic, S., & Skrjanc, I. Wheeled mobile robotics: from fundamentals towards autonomous systems. Butterworth-Heinemann, 2017.
• [2] Montiel, O., Orozco-Rosas, U., & Sepúlveda, R., Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles. Expert Systems with Applications, 42(12), 5177-5191, 2015.
• [3] Mac, T. T., Copot, C., Tran, D. T., & De Keyser, R., Heuristic approaches in robot path planning: A survey. Robotics and Autonomous Systems, 86, 13-28, 2016.
• [4] Rosell, J., & Iniguez, P., Path planning using harmonic functions and probabilistic cell decomposition. In Proceedings of the 2005 IEEE international conference on robotics and automation. IEEE. pp. 1803-1808. Apr., 2005.
• [5] Šeda, M., Roadmap methods vs. cell decomposition in robot motion planning. In Proceedings of the 6th WSEAS International Conference on Signal Processing, Robotics and Automation. World Scientific and Engineering Academy and Society (WSEAS). pp. 127-132. Feb., 2017.
• [6] Cosío, F. A., & Castañeda, M. P., Autonomous robot navigation using adaptive potential fields. Mathematical and computer modelling, 40(9), 1141-1156, 2004.
• [7] Yin, L., Yin, Y., & Lin, C. J., A new potential field method for mobile robot path planning in the dynamic environments. Asian Journal of Control, 11(2), 214-225, 2009.
• [8] Zhang, Q., Yue, S. G., Yin, Q. J., & Zha, Y. B., Dynamic obstacle-avoiding path planning for robots based on modified potential field method. In International Conference on Intelligent Computing. Springer Berlin Heidelberg, pp. 332-342, Jul., 2013.
• [9] Singh, N. N., Chatterjee, A., Chatterjee, A., & Rakshit, A., A two-layered subgoal based mobile robot navigation algorithm with vision system and IR sensors. Measurement, 44(4), 620-641,2011.
• [10] Liu, H., Wan, W., & Zha, H. A dynamic subgoal path planner for unpredictable environments. In Robotics and Automation (ICRA), 2010 IEEE International Conference on. IEEE. pp. 994-1001. May., 2010.
• [11] Candido, S., Kim, Y. T., & Hutchinson, S., An improved hierarchical motion planner for humanoid robots. In Humanoids 2008-8th IEEE-RAS International Conference on Humanoid Robots. IEEE. pp. 654-661. Dec.,2008.
• [12] Lee, J., Kwon, O., Zhang, L., & Yoon, S. E., A selective retraction-based RRT planner for various environments. IEEE Transactions on Robotics, 30(4), 1002-1011, 2014.
• [13] Hidalgo-Paniagua, A., Vega-Rodríguez, M. A., & Ferruz, J., Applying the MOVNS (multi-objective variable neighborhood search) algorithm to solve the path planning problem in mobile robotics. Expert Systems with Applications, 58, 20-35. 2016.
• [14] Dezfoulian, S. H., Wu, D., & Ahmad, I. S., A generalized neural network approach to mobile robot navigation and obstacle avoidance. In Intelligent Autonomous Systems 12 .Springer Berlin Heidelberg, pp. 25-42,2013.
• [15] Singh, M. K., & Parhi, D. R., Path optimisation of a mobile robot using an artificial neural network controller. International Journal of Systems Science, 42(1), 107-120, 2011.
• [16] Al-Sagban, M., & Dhaouadi, R., Neural-based navigation of a differential-drive mobile robot. In Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on IEEE. pp. 353-358, Dec., 2012.
• [17] Chang, H., & Jin, T., Command Fusion Based Fuzzy Controller Design for Moving Obstacle Avoidance of Mobile Robot. In Future Information Communication Technology and Applications. Springer Netherlands, pp. 905-913,2013.
• [18] Abdessemed, F., Faisal, M., Emmadeddine, M., Hedjar, R., Al-Mutib, K., Alsulaiman, M., & Mathkour, H., A hierarchical fuzzy control design for indoor mobile robot. International Journal of Advanced Robotic Systems, 11, 2014.
• [19] Morales, N., Toledo, J., & Acosta, L., Path planning using a Multiclass Support Vector Machine. Applied Soft Computing, 43, 498-50, 2016.
• [20] Roberge, V., Tarbouchi, M., & Labonté, G., Comparison of parallel genetic algorithm and particle swarm optimization for real-time UAV path planning. IEEE Transactions on Industrial Informatics, 9(1), 132-141, 2013.
• [21] Alajlan, M., Koubâa, A., Châari, I., Bennaceur, H., & Ammar, A., Global path planning for mobile robots in large-scale grid environments using genetic algorithms. In Individual and Collective Behaviors in Robotics (ICBR), 2013 International Conference on IEEE, pp. 1-8, Dec.,2013.
• [22] Oleiwi, B. K., Al-Jarrah, R., Roth, H., & Kazem, B. I., Multi Objective Optimization of Trajectory Planning of Non-holonomic Mobile Robot in Dynamic Environment Using Enhanced GA by Fuzzy Motion Control and A. In International Conference on Neural Networks and Artificial Intelligence. Springer International Publishing, pp. 34-49, Jun., 2014.
• [23] Karami, A. H., & Hasanzadeh, M., An adaptive genetic algorithm for robot motion planning in 2D complex environments. Computers & Electrical Engineering, 43, 317-329, 2015.
• [24] Wang, G., Guo, L., Duan, H., Liu, L., & Wang, H., A bat algorithm with mutation for UCAV path planning. The Scientific World Journal, 2012.
• [25] Das, P. K., Behera, H. S., & Panigrahi, B. K., A hybridization of an improved particle swarm optimization and gravitational search algorithm for multi-robot path planning. Swarm and Evolutionary Computation, 28, 14-28, 2016.
• [26] Zhang, Y., Gong, D. W., & Zhang, J. H., Robot path planning in uncertain environment using multi-objective particle swarm optimization. Neurocomputing, 103, 172-185, 2013.
• [27] Chen, X., Kong, Y., Fang, X., & Wu, Q., A fast two-stage ACO algorithm for robotic path planning. Neural Computing and Applications, 22(2), 313-319, 2013.
• [28] Karaboga, D., & Basturk, B., A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. Journal of global optimization, 39(3), 459-471, 2007.
• [29] Liu, S., & Sun, D.,Minimizing Energy Consumption of Wheeled Mobile Robots via Optimal Motion Planning. IEEE/ASME Transactions on Mechatronics, 19(2), 401–411. doi:10.1109/tmech.2013.2241777,2014.
• [30] Martin, S. R., Wright, S. E., & Sheppard, J. W., Offline and online evolutionary bi-directional RRT algorithms for efficient re-planning in dynamic environments. In Automation Science and Engineering, 2007. CASE 2007. IEEE International Conference on .IEEE, pp. 1131-1136, Sept.,2007.
• [31] Zhao, D., & Yi, J., Robot planning with artificial potential field guided ant colony optimization algorithm. In International Conference on Natural Computation .Springer, Berlin, Heidelberg, pp. 222-231,Sept.,2006.
• [32] Santiago, R. M. C., De Ocampo, A. L., Ubando, A. T., Bandala, A. A., & Dadios, E. P. ,Path planning for mobile robots using genetic algorithm and probabilistic roadmap. In Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2017 IEEE 9th International Conference on .IEEE, pp. 1-5,Dec.,2017.
• [33] Chen, Y., Su, F., & Shen, L. C., Improved ant colony algorithm base on PRM for UAV route planning. Journal of System Simulation, 21(6), 1658-1666, 2009.
• [34] Masehian, E., & Sedighizadeh, D., Multi-objective PSO-and NPSO-based algorithms for robot path planning. Advances in electrical and computer engineering, 10(4), 69-76, 2010.
• [35] LaValle, S. M., Rapidly-exploring random trees: A new tool for path planning. TR 98-11, Computer Science Dept., Iowa State Univ. , Oct., 1998.
• [36] Kuffner, James J., and Steven M. LaValle. "RRT-connect: An efficient approach to single-query path planning." Robotics and Automation, 2000. Proceedings. ICRA'00. IEEE International Conference on. Vol. 2. IEEE, 2000.
• [37] Ardiyanto, J. Miura, Real-time navigation using randomized kinodynamic planning with arrival time field, Robot. Auton. Syst. 60 (2012) 1579–1591.
• [38] Karaboğa, D.,Yapay Zeka Optimizasyon Algoritmalari, 2014.
• [39] Karaboga, D., & Basturk, B., Artificial bee colony (ABC) optimization algorithm for solving constrained optimization problems. In International fuzzy systems association world congress (pp. 789-798). Springer, Berlin, Heidelberg, Jun., 2007.
• [40] UZLU E . Türkiye için gri kurt optimizasyon algoritması ile yapay sinir ağlarını kullanarak enerji tüketiminin tahmini. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji. 2019; 7(2): 262-245.
• [41] MathWorks internet sayfası.https://www.mathworks.com/matlabcentral/fileexchange/55177-robot-path-planning?focused=5915988&tab=function (2016).