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Design of an Artificial Intelligence Based Autonomous Navigation System Using Swarm Intelligence Techniques for Agricultural Applications | ||
| AUT Journal of Mechanical Engineering | ||
| مقاله 8، دوره 10، شماره 1، فروردین 2026، صفحه 123-136 اصل مقاله (1.36 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/ajme.2025.24228.6186 | ||
| نویسندگان | ||
| Kapil Jajulwar* 1؛ Sugesh Ghodmare2؛ Poonam Jattewar3 | ||
| 1Department of Electronics & Telecommunication Engineering, G H Raisoni College of Engineering Nagpur 440016, India * kapil.jajulwar@raisoni.net | ||
| 2Department of Civil Engineering, G H Raisoni College of Engineering Nagpur 440016, India | ||
| 3Department of Electronics & Telecommunication Engineering , KDK College of Engineering Nagpur 440024, India | ||
| چکیده | ||
| Agriculture in developing regions still depends on manual labor and low-precision tools, leading to reduced productivity, inconsistent output quality, and inefficient use of resources. To overcome these challenges, this paper presents the design and implementation of an Artificial Intelligence based Autonomous Navigation System empowered by Swarm Intelligence for next-generation smart farming. The system integrates Particle Swarm Optimization and Ant Colony Optimization with Simultaneous Localization and Mapping to enable cooperative navigation, decentralized decision-making, and adaptive path planning for multiple agricultural robots operating within the same field environment. The combined Artificial Intelligence and Swarm Intelligence framework supports dynamic obstacle avoidance, efficient field-coverage planning, and improved robustness under uncertain terrain, varying crop densities, and rapidly changing environmental conditions. The uniqueness of this work lies in its unified model that merges AI-driven reasoning with swarm-based coordination, improving navigation accuracy, task reliability, and energy efficiency in unstructured agricultural fields. System validation was conducted through simulation and semi-field experiments on a 25 × 25 meter test plot. Results showed a significant enhancement in performance, including a thirty-five percent improvement in navigation accuracy, a twenty-two percent increase in obstacle-avoidance success, and a fifteen percent reduction in energy consumption compared to conventional single-robot navigation methods. These outcomes demonstrate that integrating Artificial Intelligence with Swarm Intelligence provides a scalable and sustainable solution for precision agriculture, promoting autonomous, intelligent, and resource-efficient farming suitable for developing regions. | ||
| کلیدواژهها | ||
| Precision Farming؛ Multi-Robot Coordination؛ Path Optimization؛ Obstacle Avoidance؛ Sustainable Field Automation | ||
| مراجع | ||
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[1] Y.-C.C. Shih-An Li, Bo-Xian Wu & Hsuan-Ming Feng 3D lidar SLAM-based systems in object detection and navigation applications Journal of the Chinese Institute of Engineers 46(8) (2023) 912–925.
[2] Z.W. Wenji Li, Chaotao Guan, Chuangbin Chen, Boxi Wang, et al., Formation control of swarm robotics: A survey from biological inspirations to design automation methods Robotics and Autonomous Systems 196(1) (2025).
[3] S.K. Mohamed H. Hassan, Mahmoud A. El-Dabah, Mohammad A. Abido, et al., Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi-machine systems, IET Generation, Transmission and Distribution 19(1) (2025) 1–17.
[4] N.P.M.M.K.J.M.M.M. Patil, Design of Multipurpose Agro System Using Swarm Intelligence, 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA), (2021).
[5] N.K. S. Tanwar, A. Kumari Swarm Intelligence-Based Navigation of Autonomous Agricultural Robots: A Review, Computers and Electronics in Agriculture, 170(105229) (2020).
[6] T.L.C. Chia‐Ju Wu, Tsong‐Li Lee & Li‐Chun Lai, Navigation of a mobile robot in outdoor environments, Journal of the Chinese Institute of Engineers 28(6) (2005) 915–924.
[7] W.S. Barbosa, P.-R. Dept. of Electrical Engineering, Rio de Janeiro, RJ, Brazil, A.I.S.O.G.B.P.B.A.C.L.K.T.F.M.M.B.R. Vellasco, Design and Development of an Autonomous Mobile Robot for Inspection of Soy and Cotton Crops, 12th International Conference on Developments in eSystems Engineering (DeSE), 17(6) (2009).
[8] S.L. Nandan Ravi, Nikita Balappa, Narasannavar, A. S. Pratxusha, and Sampath Kumar, Swarm Robotics for Agriculture, International Research Journal of Engineering and Technology, 7(2) (2020).
[9] Y.A.M. Makaraci, Swarm Robotics for Autonomous Aerial Robots: Features, Algorithms, Control Techniques, and Challenges, IEEE Access, 11 (2023) 19523–19544.
[10] K.P.S. K. R. Rana, N. Kumar, Swarm Intelligence in Agriculture: Techniques, Applications, and Challenges., Computers and Electronics in Agriculture, 162 (2019) 466–472.
[11] M.A.A. Marc-Andrė Blais, Reinforcement learning for swarm robotics: An overview of applications, algorithms and simulators, Engineering Applications of Artificial Intelligence, 126(Part A) (2023).
[12] V.K. R. Gupta, A. Sing, Reinforcement-Learning-Enhanced Swarm Robotics for Smart Farming, IEEE Access, 10 (2022).
[13] F.L. Ruqing Zhao, Xin Lu, Shubin Lyu, Multi-objective cooperative transportation for reconfigurable robot using isomorphic mapping multi-agent reinforcement learning, Robotics and Autonomous Systems, 101 (2024).
[14] I.A. Medhat A. Tawfeek, Madallah Alruwaili, Fatma M. Talaat A Fuzzy Multi-Objective Framework for Energy Optimization and Reliable Routing in Wireless Sensor Networks via Particle Swarm Optimization, Computers, Materials & Continua 83(2) (2025) 1653–1671.
[15] V.P.T. Lucas William Page, Duy Luan Nguyen, Real-time cooperative target tracking in cluttered environments using multiple drone swarms with adaptive fuzzy emotional learning, Neurocomputing, 161(Part A) (2025).
[16] Z.S. Abdennabi Morchid, Almoataz Y. Abdelaziz, Pierluigi Siano, Hassan Qjidaa, Fuzzy logic-based IoT system for optimizing irrigation with cloud computing: Enhancing water sustainability in smart agriculture, Scientific Reports, 15 (2025) 1–22.
[17] B.C. kunling Zhang, Factor-biased agricultural productivity improvement and industrialization: evidence from the Belt and Road Countries, 17(3) (2025) 507–525.
[18] B.W. Shouying Liu, The decline in agricultural share and agricultural industrialization—some stylized facts and theoretical explanations, China Agricultural Economic Review, 14(3) (2022) 370–394.
[19] L.Z. Yiqing Lu, Qiankun Zhao, LiDAR-Visual SLAM with Integrated Semantic and Texture Information for Enhanced Ecological Monitoring Vehicle Localization, Computers, Materials & Continua, 82(1) (2025).
[20] M.A.K.K. Adib Bin Rashid, AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications, Hybrid Advances, 7(7) (2024).
[21] L.M. Abdelkrim Abanay, Dirar Benkhedra, Khalid El Amraoui, et al., A transformation model for vision-based navigation of agricultural robots, Cognitive Robotics, 5(1) (2025). | ||
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