A Hybrid Method for Industrial Robot Navigation

Journal of Optimization in Industrial Engineering

مقاله 11، دوره 14، شماره 1 - شماره پیاپی 30، شهریور 2021، صفحه 133-148   

نوع مقاله: Original Manuscript

شناسه دیجیتال (DOI): 10.22094/joie.2020.1863337.1629

نویسنده

Somayeh Raiesdana^*

Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

چکیده

Robot navigation in dynamic unknown environments is a challenging issue in the field of autonomous mobile robot control. This paper presents a hybrid robust method for navigating an industrial robot in an environment that contains dynamic obstacles. The objectives are to find the shortest path, to minimize the energy consumption of robot, to make the smoothness of the generated paths and to tackle dynamic obstacles. Robots employed in industrial environments demand considerable autonomy and require high level of accuracy and manoeuvrability at the same time. Besides, no collision is tolerable along the way. A single-objective optimization method based on path criteria fails to satisfy all of the requirements. This paper proposes a hybrid algorithm including the whale optimization algorithm (WOA) for path planning, a learnable function approximation network for making smoothness of the generated paths and a fuzzy logic controller to avoid obstacle collision. In this algorithm, WOA optimizes the best path to be taken from the start to goal position. Once a sequence of points is candidate and segments of path are merged, a radial basis function is trained to provide a smooth movement path in the dynamic environment while trying to maximize the safety margin. To further improve the safety of navigation, a fuzzy-based obstacle avoidance algorithm is executed when the robot is placed in the vicinity of an obstacle. Fuzzy decisions are made based on values of distance information. The proposed hybrid method for path planning and obstacle avoidance issues was implemented and evaluated in dynamic environments including specific shaped obstacles. A GUI-based simulation platform was designed in Matlab environment for testing the proposed algorithm. Implementation results indicate that the proposed algorithm has yielded in smooth non-marginal goal-directed navigation with acceptable performance metrics. Meanwhile, collisions to dynamic obstacles were adaptively and non-rigidly avoided. Such a model-free hybrid algorithm for path planning and obstacle avoidance can improve autonomy in industrial operation and decrease computational complexity.

کلیدواژه‌ها

Mobile robot navigation؛ path planning؛ obstacle avoidance؛ whale optimization algorithm؛ RBF network؛ fuzzy inference system

مراجع

AbuBaker, A. (2012). A novel mobile robot navigation system using neuro-fuzzyrule-based optimization technique. Research Journal of Applied Sciences, Engineering and Technology, 4:2577-2583.

Barbehenn, M. (1998). A note on the complexity of Dijkstra’s algorithm for graphs with weighted vertices. IEEE Transactions on Commuters, 47(2): 263.

 Borenstein, J., Koren, Y. (1991). The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans Robot Autom, 7(3): 278–288.

Castellano, G., Attolico, G., Distante A. (1997). Automatic generation of fuzzy rules for reactive robot controllers. Robot Autonomous System, 22(2):133-149.

Ceballos, N.D.M., Valencia, J.A., and Ospina, N.L. (2010). Quantitative performance metrics for mobile robots navigation. Mobile Robots Navigation 15: 486:500.

Dao, T.K., Pan, T.S. and Pan, J.S. (2016). A multi-objective optimal mobile robot path planning based on whale optimization algorithm.  IEEE 13th International Conference on Signal Processing.ICSP2016: 337- 342.

Deng, X., Milios, E. (1996). Landmark selection strategies for path execution. Robotics and Autonomous Systems, 17: 171-185.

Dezfoulian, S.H. (2011). A generalized neural network approach to mobile robot navigation and obstacle avoidance. MSc. Thesis, University of Windsor.

Dugarjav, B., Kim, H., Lee, S-G (2015). Online cell decomposition with a laser range finding for coverage path in an unknown workspace. International Journal of Mechanical and Production Engineering, 3(5): 18-24.

Fox, D., Burgard, W., Thrun, S. (1997). The dynamic window approach to collision avoidance. IEEE Robotics & Automation Magazine, 4: 23–33.

Fujii, T., Arai, Y., Asama, H., Endo, I. (1998), Multilayered reinforcement learning for complicated collision avoidance problems.  IEEE International Conference on Robotics and Automation, 2186–2191.

Ganapathy, V., Yun, S. C., and Ng, J. (2009).  Fuzzy and neural controllers for acute obstacle avoidance in mobile robot navigation.  IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 1236-1241.

Ge, S.S., Cui, Y. J. (2000). New potential functions for mobile robot path planning. IEEE Trans. Robot. Autom. 16(5): 615–620.

Groen, F.C.A. (2000). A virtual target approach for resolving the limit cycle problem in navigation of a fuzzy behavior-based mobile robot. Robotics and Autonomous System,  30(4): 315–324.

Huji, D.,  Croft,  E.A.,  Zak,  G.,  Fenton R.G.,  Mills, J.K.,   Benhabi, B. (1998), The robotic interception of moving objects inindustrial settings: strategy developmentand experiment.  IEEE/ASME Transactions On Mechatronics, 3: 225 – 239.

Joshi, M. M., Zaveri, M. (2010). Neuro-fuzzy based autonomous mobile robot navigation system. 11th International Conference on Control Automation Robotics & Vision (ICARCV), pp. 384-389.

Kumar, A., Kumar, Priyadarshi Biplab, K., Parhi Dayal, R. (2018). Intelligent navigation of humanoids in cluttered environments using regression analysis and genetic algorithm. Arabian J Sci Eng. 1-24.

Kumar, M. P., Rishna K. P., Dayal Manfis, R. P. (2014). Approach for Path Planning and Obstacle Avoidance for Mobile Robot Navigation. Advances in Intelligent Systems and Computing, 248(1): 361-370.

Li, T. and Latombe, J. (1997). Online manipulation planning for two robot arms in a dynamic environment. The International Journal of Robotics Research, 16 (2):144-167.

Masehian, E. and Sedighizadeh, D. (2013). An improved particle swarm optimization method for motion planning of multiple robots springer. Tracts in Advanced Robotics, 83: 175-188.

Mirjalili, S., Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95: 51–67.

Mohanty, P. K., Parhi, D. R. (2013). Path planning strategy for mobile robot navigation using MANFIS controller.  Advances in Intelligent Systems and Computing, 247: 353-361.

Mohanty, P. K. and Parhi, D. R. (2015). A new hybrid intelligent path planner for mobile robot navigation based on adaptive neuro-fuzzy inference system. Australian Journal of Mechanical Engineering, 13(3): 195–207.

Ng, J., Bräunl, T. (2007). Performance comparison of bug navigation algorithms. Journal of Intelligent and Robotic Systems, 50: 73-84.

Oscar, M., Ulises, O-R., Roberto, S. (2015). Path planning for mobile robots using Bacterial Potential Field for avoiding static and dynamic obstacles. Expert Syst Appl: 42(12):5177-91.

Patle, B.K., Babu, G., Pandey, A., Parhi, D.R.K., Jagadeesh, A. (2019). A review: On path planning strategies for navigation of mobile robot. Defense Technology 15:582-606.

Patle, BK, Parhi, DRK, Jagadeesh, A, Kashyap, S. K. (2016). Probabilistic fuzzy controller based robotics path decision theory. World Journal of Engineering; 13(2):181e92.

Raiesdana, S., HashemiGoplayegani, S.M. (2013). Study on chaos anti-control for hippocampal models of epilepsy. Neurocomputing, 111: 54-69

Ravankar, A., Ravankar, A., Kobayashi, Y., Hoshino, Y. and Peng, C.  (2018). Path smoothing techniques in robot navigation: state-of-the-art, Current and Future Challenges. Sensors 18: 3170-3200.

Silva, C., Crisostomo, M., Ribeiro, B. (2000), MONODA: a neural modular architecture for obstacle avoidance without knowledge of the environment. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 6:334-339.

 

Singh, M.K., Parhi, D.R. (2011). Path optimization of a mobile robot using an artificial neural network controller. International Journal of Systems Science, 42:107-120.

Singh, M. K., Parhi, D. R., Pothal, J. K. (2009). ANFIS approach for navigation of mobile robots. International Conference on Advances in Recent Technologies in Communication and Computing, 727-731.

Tsui, W., Masmoudi, M. S., Karray, F. (2008). Soft-computing-based embedded design of an intelligent wall/lane-following vehicle. IEEE/ASME Trans. Mechatronics, 13(1): 125-135.

Vukosavjev, S., Kukolj, D., Papp, I., Markoski, B. (2011). Mobile robot control using combined neural-fuzzy and neural network. 12th IEEE International Symposium on Computational Intelligence and Informatics, 351-356.

Wooden, D. T. (2006). Graph-based path planning for mobile robots, PhD. Thesis, School of Electrical and Computer Engineering Georgia Institute of Technology.

Zafar, M. N., Mohanta, J. C. (2018). Methodology for path planning and optimization of mobile robots: a review. procedia computer science 133:141–152.

Zavlangas, P. G., Tzafestas, S. G. (2000). Industrial robot navigation and obstacle avoidance employing fuzzy logic. Journal of Intelligent and Robotic Systems, 27: 85–97.

Zavlangas, P.G., Tzafestas, S.G. (2003). Motion control for mobile robot obstacle avoidance and navigation: a fuzzy logic-based approach. Systems Analysis Modelling Simulation, 43: 1625–1637.