Keywords and phrases: Arduino Uno, path following, avoiding obstacle, mobile robot.
Received: May 1, 2023; Accepted: June 28, 2023; Published: August 11, 2023
How to cite this article: Muhammad Ahmad Baballe, Abdu Ibrahim Adamu, Abdulkadir Shehu Bari and Amina Ibrahim, Principle operation of a line follower robot, Far East Journal of Electronics and Communications 27 (2023), 1-12. http://dx.doi.org/10.17654/0973700623001
This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License
References:
[1] A. Mora, A. Prados, A. Mendez, R. Barber and S. Garrido, Sensor fusion for social navigation on a mobile robot based on fast marching square and Gaussian mixture model, Sensors 22(22) (2022), 8728. [2] R. M. Sousa, D. B. Aranibar, J. D. Amado, R. E. P. Escarcina and R. M. P. Trindade, A new approach for including social conventions into social robots navigation by using polygonal triangulation and group asymmetric Gaussian functions, Sensors 22(12) (2022), 4602. [3] L. C. Santos, A. S. Aguiar, F. N. Santos, A. Valente and M. Petry, Occupancy grid and topological maps extraction from satellite images for path planning in agricultural robots, Robotics 9(4) (2020), 77. [4] Y. Gao, C. Bai, R. Fu and Q. Quan, A non-potential orthogonal vector field method for more efficient robot navigation and control, Robotics and Autonomous Systems 159 (2023), 104291. [5] M. Santilli, P. Mukherjee, R. K. Williams and A. Gasparri, Multirobot field of view control with adaptive decentralization, IEEE Transactions on Robotics 38(4) (2022), 2131-2150. [6] Y. Zhao, T. Wang and W. Bi, Consensus protocol for multiagent systems with undirected topologies and binary-valued communications, IEEE Trans. Automat. Control 64(1) (2019), 206-221. [7] D. Gadjov and L. Pavel, A passivity-based approach to nash equilibrium seeking over networks, IEEE Trans. Automat. Control 64(3) (2019), 1077-1092. [8] Y. Hua, X. Dong, Q. Li and Z. Ren, Distributed fault-tolerant time-varying formation control for second-order multi-agent systems with actuator failures and directed topologies, IEEE Transactions on Circuits and Systems II: Express Briefs 65(6) (2018), 774-778. [9] A. Carrio, J. Tordesillas, S. Vemprala, S. Saripalli, P. Campoy and J. P. How, Onboard detection and localization of drones using depth maps, IEEE Access 8 (2020), 30480-30490. [10] H. Seo, G. Cho, S.-J. Kim, J.-H. Chun and J. Choi, Multievent histogramming TDC with pre-post weighted histogramming filter for CMOS LiDAR sensors, IEEE Sensors Journal 22(23) (2022), 22785-22798. [11] Z. Tahir, A. H. Qureshi, Y. Ayaz and R. Nawaz, Potentially guided bidirectionalized RRT* for fast optimal path planning in cluttered environments, Robotics and Autonomous Systems 108 (2018), 13-27. [12] K. M. I. Khalilullah, S. Ota, T. Yasuda and M. Jindai, Road area detection method based on DBNN for robot navigation using single camera in outdoor environments, Industrial Robot 45(2) (2018), 275-286. [13] G. Lei, R. Yao, Y. Zhao and Y. Zheng, Detection and modeling of unstructured roads in forest areas based on visual-2D Lidar data fusion, Forests 12(7) (2021), 820. [14] F. Amorós, L. Payá, W. Mayol-Cuevas, L. M. Jiménez and O. Reinoso, Holistic descriptors of omnidirectional color images and their performance in estimation of position and orientation, IEEE Access 8 (2020), 81822-81848. [15] A. W. L. Yao and H. C. Chen, An intelligent color image recognition and mobile control system for robotic arm, International Journal of Robotics and Control Systems 2(1) (2022), 97-104. [16] I. Hassani, I. Ergui and C. Rekik, Turning point and free segments strategies for navigation of wheeled mobile robot, International Journal of Robotics and Control Systems 2(1) (2022), 172-186. [17] G. Farid, et al., Modified A-Star (A*) approach to plan the motion of a quadrotor UAV in three-dimensional obstacle-cluttered environment, Appl. Sci. 12(12) (2022), 5791. [18] T.-Y. Lin, K.-R. Wu, Y.-S. Chen and Y.-S. Shen, Collision-free motion algorithms for sensors automated deployment to enable a smart environmental sensing-net, IEEE Transactions on Automation Science and Engineering 19(4) (2022), 3853-3870. [19] K. Li, Q. Hu and J. Liu, Path planning of mobile robot based on improved multiobjective genetic algorithm, Wireless Communications and Mobile Computing 2021 (2021), 1-12. [20] K. Hao, J. Zhao, B. Wang, Y. Liu and C. Wang, The application of an adaptive genetic algorithm based on collision detection in path planning of mobile robots, Computational Intelligence and Neuroscience 2021 (2021), 1-20. [21] I. Noreen, A. Khan, K. Asghar and Z. Habib, A path-planning performance comparison of RRT*-AB with MEA* in a 2-dimensional environment, Symmetry 11(7) (2019), 945-960. [22] J. Wang, W. Chi, C. Li, C. Wang and M. Q.-H. Meng, Neural RRT*: learning-based optimal path planning, IEEE Transactions on Automation Science and Engineering 17(4) (2020), 1748-1758. [23] Y. Bai, G. Li and N. Li, Motion planning and tracking control of autonomous vehicle based on improved A* algorithm, Journal of Advanced Transportation 2022 (2022), 1-14. [24] H. Min, X. Xiong, P. Wang and Y. Yu, Autonomous driving path planning algorithm based on improved algorithm in unstructured environment, Proceedings of the Institution of Mechanical Engineers - Part D: Journal of Automobile Engineering 235(2-3) (2021), 513-526. [25] B. Fu et al., An improved A* algorithm for the industrial robot path planning with high success rate and short length, Robotics and Autonomous Systems 106 (2018), 26-37. [26] N. B. A. Latip, R. Omar and S. K. Debnath, Optimal path planning using equilateral spaces-oriented visibility graph method, Intl. J. Electr. Comput. Eng. 7(6) (2017), 3046-3051. [27] L. Janson, E. Schmerling, A. Clark and M. Pavone, Fast marching tree: a fast marching sampling-based method for optimal motion planning in many dimensions, The International Journal of Robotics Research 34(7) (2015), 883-921. [28] O. Montiel, U. Orozco-Rosas and R. Seplveda, Path planning for mobile robots using bacterial potential field for avoiding static and dynamic obstacles, Expert Syst. Appl. 42(12) (2015), 5177-5191. [29] A. H. Karami and M. Hasanzadeh, An adaptive genetic algorithm for robot motion planning in 2D complex environments, Computers and Electrical Engineering 43 (2015), 317-329. [30] F. Kamil, T. S. Hong, W. Khaksar, M. Y. Moghrabiah, N. Zulkifli and S. A. Ahmad, New robot navigation algorithm for arbitrary unknown dynamic environments based on future prediction and priority behavior, Expert Syst. Appl. 86 (2017), 274-291. [31] M. Alajlan, I. Chaari, A. Koubaa, H. Bennaceur, A. Ammar and H. Youssef, Global robot path planning using GA for large grid maps: modelling performance and experimentation, International Journal of Robotics and Automation 31(6) (2016), 484-495. [32] Y. Zhao, X. Liu, G. Wang, S. Wu and S. Han, Dynamic resource reservation based collision and deadlock prevention for multi AGVs, IEEE Access 8 (2020), 82120-82130. [33] L. Yue and H. Fan, Dynamic scheduling and path planning of automated guided vehicles in automatic container terminal, IEEE/CAA Journal of Automatica Sinica 9(11) (2022), 2005-2019. [34] H. Xiao, X. Wu, D. Qin and J. Zhai, A collision and deadlock prevention method with traffic sequence optimization strategy for UGN-based AGVS, IEEE Access 8 (2020), 209452-209470. [35] M. Faisal, R. Hedjar, M. Al-Sulaiman and K. Al-Mutib, Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment, International Journal of Advanced Robotic Systems 10(1) (2013). https://doi.org/10.5772/54427. [36] A. Shitsukane, W. Cheruiyot, C. Otieno and M. Mvurya, Fuzzy logic sensor fusion for obstacle avoidance mobile robot, 2018 IST-Africa Week Conference (IST-Africa), 2018, pp. 1-8. [37] S.-Y. Chiang, Vision-based obstacle avoidance system with fuzzy logic for humanoid robots, The Knowledge Engineering Review 32(E9) (2017). DOI: https://doi.org/10.1017/S0269888916000084. [38] S. Ayub, N. Singh, M. Z. Hussain, M. Ashraf, D. K. Singh and A. Haldorai, Hybrid approach to implement multi-robotic navigation system using neural network, fuzzy logic, and bio-inspired optimization methodologies, Computational Intelligence (2022), 1-15. https://doi.org/10.1111/coin.12547. [39] K. Farah and M. Y. Mohammed, Multilayer decision-based fuzzy logic model to navigate mobile robot in unknown dynamic environments, Fuzzy Information and Engineering 14(1) (2021), 51-73. [40] H. Muhammad et al., Robust mobile robot navigation in cluttered environments based on hybrid adaptive neuro-fuzzy inference and sensor fusion, Journal of King Saud University - Computer and Information Sciences 34(10) (2022), 9060-9070. [41] F. Á. Szili, J. Botzheim and B. Nagy, Bacterial evolutionary algorithm-trained interpolative fuzzy system for mobile robot navigation, Electronics 11(11) (2022), 1734. [42] S. Zubair, S. ABU, R. Ruzairi, A. Andi and H. Mohd, Non-verbal human-robot interaction using neural network for the application of service robot, IIUM Engineering Journal 24(1) (2023), 301-318. [43] U. A. Syed, F. Kunwar and M. Iqbal, Guided autowave pulse coupled neural network (GAPCNN) based real time path planning and an obstacle avoidance scheme for mobile robots, Robotics and Autonomous Systems 62(4) (2014), 474-486. [44] K.-H. Chi and M.-F. R. Lee, Obstacle avoidance in mobile robot using neural network, 2011 International Conference on Consumer Electronics, Communications and Networks (CECNet), 2011, pp. 5082-5085. [45] A. Medina-Santiago, J. L. Camas-Anzueto, J. A. Vazquez-Feijoo, H. R. Hernández-de León and R. Mota-Grajales, Neural control system in obstacle avoidance in mobile robots using ultrasonic sensors, Journal of Applied Research and Technology 12(1) (2014), 104-110. [46] A. N. A. Rafai, N. Adzhar and N. I. Jaini, A review on path planning and obstacle avoidance algorithms for autonomous mobile robots, Journal of Robotics 2022 (2022), 1-14. [47] F. Qu, W. Yu, K. Xiao, C. Liu and W. Liu, Trajectory generation and optimization using the mutual learning and adaptive ant colony algorithm in uneven environments, Appl. Sci. 12(9) (2022), 4629. Doi: 10.3390/app12094629. [48] A. Muhammad, M. A. H. Ali and I. H. Shanono, Path planning methods for mobile robots: a systematic and bibliometric review, ELEKTRIKA- Journal of Electrical Engineering 19(3) (2020), 14-34. [49] D. Zhu, C. Tian, B. Sun and C. Luo, Complete coverage path planning of autonomous underwater vehicle based on GBNN algorithm, Journal of Intelligent and Robotic Systems 94 (2019), 237-249. [50] C. Sun, W. He and J. Hong, Neural network control of a flexible robotic manipulator using the lumped spring-mass model, IEEE Transactions on Systems, Man, and Cybernetics: Systems 47(8) (2017), 1863-1874. [51] A. Kumar, P. B. Kumar and D. R. Parhi, Intelligent navigation of humanoids in cluttered environments using regression analysis and genetic algorithm, Arabian Journal for Science and Engineering 43 (2018), 7655-7678. [52] A. K. Abbas, Y. A. Mashhadany, M. J. Hameed and S. Algburi, Review of intelligent control systems with robotics, Indonesian Journal of Electrical Engineering and Informatics (IJEEI) 10(4) (2022), 734-753. [53] M. Naeem, S. T. H. Rizvi and A. Coronato, A gentle introduction to reinforcement learning and its application in different fields, IEEE Access 8 (2020), 209320-209344. [54] M. Naeem, A. Coronato, Z. Ullah, S. Bashir and G. Paragliola, Optimal user scheduling in multi antenna system using multi agent reinforcement learning, Sensors 22(21) (2022), 8278. [55] W. Kumwilaisak, S. Phikulngoen, J. Piriyataravet, N. Thatphithakkul and C. Hansakunbuntheung, Adaptive call center workforce management with deep neural network and reinforcement learning, IEEE Access 10 (2022), 35712-35724. [56] A. Alwarafy, M. Abdallah, B. S. Çiftler, A. Al-Fuqaha and M. Hamdi, The frontiers of deep reinforcement learning for resource management in future wireless HetNets: techniques, challenges, and research directions, IEEE Open Journal of the Communications Society 3 (2022), 322-365. [57] B. Jang, M. Kim, G. Harerimana and J. W. Kim, Q-learning algorithms: a comprehensive classification and applications, IEEE Access 7 (2019), 133653-133667. [58] N. Sutisna, A. M. R. Ilmy, I. Syafalni, R. Mulyawan and T. Adiono, FARANE-Q: fast parallel and pipeline q-learning accelerator for configurable reinforcement learning SoC, IEEE Access 11 (2023), 144-161. [59] N. Sutisna, Z. N. Arifuzzaki, I. Syafalni, R. Mulyawan and T. Adiono, Architecture design of q-learning accelerator for intelligent traffic control system, 2022 International Symposium on Electronics and Smart Devices (ISESD), 2022, pp. 1-6. [60] A. Chandrakar and P. Paliwal, An intelligent mechanism for utility and active customers in demand response using single and double Q learning approach, Smart Energy and Advancement in Power Technologies 926 (2023), 397-413. [61] J. Raajan, P. V. Srihari, J. P. Satya, B. Bhikkaji and R. Pasumarthy, Real time path planning of robot using deep reinforcement learning, IFAC-PapersOnLine 53(2) (2020), 15602-15607. [62] R. Cimurs, J. H. Lee and I. H. Suh, Goal-oriented obstacle avoidance with deep reinforcement learning in continuous action space, Electronics 9(3) (2020), 411. [63] L. Huang, H. Qu, M. Fu and W. Deng, Reinforcement learning for mobile robot obstacle avoidance under dynamic environments, PRICAI 2018: Trends in Artificial Intelligence, Vol. 11012, 2018. [64] A. D. Pambudi, T. Agustinah and R. Effendi, Reinforcement point and fuzzy input design of fuzzy Q-learning for mobile robot navigation system, 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 2019, pp. 186-191. [65] S. Wen, X. Hu, Z. Li, H. K. Lam, F. Sun and B. Fang, NAO robot obstacle avoidance based on fuzzy Q-learning, Industrial Robot 47(6) (2020), 801-811. [66] L. D. Hanh and V. D. Cong, Path following and avoiding obstacle for mobile robot under dynamic environments using reinforcement learning, Journal of Robotics and Control (JRC) 4(2) (2023), 157-164. Doi: 10.18196/jrc.v4i2.17368. [67] M. Çavaş and M. B. Ahmad, A review on spider robotic system, International Journal of New Computer Architectures and their Applications (IJNCAA) 9(1) (2019), 19-24. [68] M. B. Ahmad and A. S. Muhammad, A general review on advancement in the robotic system, Artificial and Computational Intelligence (2020), 1-7. http://acors.org/ijacoi/VOL1_ISSUE2_04.pdf. [69] M. A. Baballe, M. I. Bello, A. Hussaini and U. S. Musa, Pipeline inspection robot monitoring system, Journal of Advancement in Robotics 9(2) (2022), 27-35. Doi: 10.37591/JoARB. [70] https://circuitdigest.com/microcontroller-projects/arduino-uno-line-follower-robot.
|