Global and local robot navigation combination for mobile robot obstacle avoidance

Yıl 2023, Cilt: 2 Sayı: 2, 335 - 343, 27.12.2023

Zead Yosif Basil Mahmood Saad Zaghlol Saeed

Öz

Nowadays, robots can be seen in different areas of life. Mobile robots can perform some tasks that are too risky for a human to perform. An important issue in the mobile robot was addressed, which is driving the robot until reaches its destination. A combination of global and local mobile robot navigation has been proposed to address the challenge of dynamic obstacle avoidance. A-star is utilized to discover an initial way between the begin and goal points. The ANFIS model is called when the obstacle is near to the mobile robot to anticipate the collision.
There are three inputs and two outputs in the adaptive neurofuzzy inference system. Angle, distance, and the relative speed between the mobile robot and any obstacles are the inputs. The outputs are recommendations for a mobile robot's steering angle and speed. According to the simulation findings, the model can avoid both static and moving obstacles in a static known environment. The proposed system achieves avoiding multiple obstacles. In comparison with other papers, the proposed model shows the enhancement in path length, speed, and time required for mobile robot traveling.

Anahtar Kelimeler

Mobile robot navigation, local path planning, global path planning, Adaptive Neuro-Fuzzy Inference System, dynamic obstacle avoidance

Kaynakça

• Armaghani, D.J., Asteris, P.G., 2021. A comparative study of ANN and ANFIS models for the prediction of cement-based mortar materials compressive strength. Neural Comput & Applic 33, 4501–4532. doi: https://doi.org/10.1007/s00521-020-05244-4 .
• Chatterjee, A., Rakshit, A., Singh, N.N., 2013. Mobile Robot Navigation, in: Vision Based Autonomous Robot Navigation, Studies in Computational Intelligence. Springer Berlin Heidelberg, Berlin, Heidelberg, pp. 1–20. doi: https://doi.org/10.1007/978-3-642- 33965-3_1
• Chen, Y.-H., Chang, C.-D., 2018. An intelligent ANFIS controller design for a mobile robot, in: 2018 IEEE International Conference on Applied System Invention (ICASI). Presented at the 2018 IEEE International Conference on Applied System Innovation (ICASI), IEEE, Chiba, pp. 445–448. doi: https://doi.org/10.1109/ICASI.2018.8394280
• Duchoň, F., Babinec, A., Kajan, M., Beňo, P., Florek, M., Fico, T., Jurišica, L., 2014. Path Planning with Modified a Star Algorithm for a Mobile Robot. Procedia Engineering 96, 59–69. doi: https://doi.org/10.1016/j.proeng.2014.12.098
• Gasparetto, A., Boscariol, P., Lanzutti, A., Vidoni, R., 2015. Path planning and trajectory planning algorithms: A general overview. Motion and operation planning of robotic systems 3–27.
• Gharajeh, M.S., Jond, H.B., 2020. Hybrid Global Positioning System-Adaptive NeuroFuzzy Inference System based autonomous mobile robot navigation. Robotics and Autonomous Systems 134, 103669. doi: https://doi.org/10.1016/j.robot.2020.103669
• Guruji, A.K., Agarwal, H., Parsediya, D.K., 2016. Time-efficient A* Algorithm for Robot Path Planning. Procedia Technology, 3rd International Conference on Innovations in Automation and Mechatronics Engineering 2016, ICIAME 2016 05-06 February, 2016 23, 144–149. doi: https://doi.org/10.1016/j.protcy.2016.03.010
• Pandey, A., Kumar, S., Pandey, K.K., Parhi, D.R., 2016. Mobile robot navigation in unknown static environments using ANFIS controller. Perspectives in Science 8, 421– 423. doi: https://doi.org/10.1016/j.pisc.2016.04.094
• Pandey, A., Panwar, V.S., Hasan, M.E., Parhi, D.R., 2020. V-REP-based navigation of automated wheeled robot between obstacles using PSO-tuned feedforward neural network. Journal of Computational Design and Engineering 7, 427–434. doi: https://doi.org/10.1093/jcde/qwaa035 Yosif, Mahmood, Saeed Journal of Optimization & Decision Making 2(2), 335-243, 2023 343
• Samadi Gharajeh, M., Jond, H.B., 2022. An intelligent approach for autonomous mobile robots path planning based on adaptive neuro-fuzzy inference system. Ain Shams Engineering Journal 13, 101491. doi: https://doi.org/10.1016/j.asej.2021.05.005
• Shafiullah, M., Abido, M.A., Al-Mohammed, A.H., 2022. Artificial intelligence techniques, in: Power System Fault Diagnosis. Elsevier, pp. 69–100. doi: https://doi.org/10.1016/B978-0-323-88429-7.00007-2
• Shahad M.Majeed, I.A.A., 2021. Path Planning with Static and Dynamic Obstacles Avoidance Using Image Processing. International Transaction Journal of Engineering Management, 12A8A: 17. doi: https://doi.org/10.14456/ITJEMAST.2021.148
• Singh, M.K., Parhi, D.R., Pothal, J.K., 2009. ANFIS Approach for Navigation of Mobile Robots, in: 2009 International Conference on Advances in Recent Technologies in Communication and Computing. Presented at the 2009 International Conference on Advances in Recent Technologies in Communication and Computing, IEEE, Kottayam, Kerala, India, pp. 727–731. doi: https://doi.org/10.1109/ARTCom.2009.119
• Singh, N.H., Thongam, K., 2019. Neural network-based approaches for mobile robot navigation in static and moving obstacles environments. Intel Serv Robotics 12, 55–67. doi: https://doi.org/10.1007/s11370-018-0260-2
• Yosif, Z., Mahmood, B., Al-khayyt, S., 2021. Assessment and Review of the Reactive Mobile Robot Navigation. Al-Rafidain Engineering Journal (AREJ) 26, 340–355. doi: https://doi.org/10.33899/rengj.2021.129484.1082
• Yosif, Z.M., Mahmood, B.S., Saeed, S.Z., 2022. Artificial Techniques Based on Neural Network and Fuzzy Logic Combination Approach for Avoiding Dynamic Obstacles. JESA 55, 339–348. doi: https://doi.org/10.18280/jesa.550306