Research on genetic algorithm optimization for agricultural machinery operation path planning

[1] Liu, J., Yang, J., Liu, H., Tian, X., & Gao, M. (2017). An improved ant colony algorithm for robot path planning. Soft computing, 21, 5829-5839. Search in Google Scholar

[2] Nazarahari, M., Khanmirza, E., & Doostie, S. (2019). Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm. Expert Systems with Applications, 115, 106-120. Search in Google Scholar

[3] Miao, C., Chen, G., Yan, C., & Wu, Y. (2021). Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Computers & Industrial Engineering, 156, 107230. Search in Google Scholar

[4] Campbell, S., O’Mahony, N., Carvalho, A., Krpalkova, L., Riordan, D., & Walsh, J. (2020, February). Path planning techniques for mobile robots a review. In 2020 6th International Conference on Mechatronics and Robotics Engineering (ICMRE) (pp. 12-16). IEEE. Search in Google Scholar

[5] Sánchez-Ibáñez, J. R., Pérez-del-Pulgar, C. J., & García-Cerezo, A. (2021). Path planning for autonomous mobile robots: A review. Sensors, 21(23), 7898. Search in Google Scholar

[6] Wei, K., & Ren, B. (2018). A method on dynamic path planning for robotic manipulator autonomous obstacle avoidance based on an improved RRT algorithm. Sensors, 18(2), 571. Search in Google Scholar

[7] Bormann, R., Jordan, F., Hampp, J., & Hägele, M. (2018, May). Indoor coverage path planning: Survey, implementation, analysis. In 2018 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1718-1725). IEEE. Search in Google Scholar

[8] Khan, A., Noreen, I., & Habib, Z. (2017). On Complete Coverage Path Planning Algorithms for Nonholonomic Mobile Robots: Survey and Challenges. J. Inf. Sci. Eng., 33(1), 101-121. Search in Google Scholar

[9] Bechar, A., & Vigneault, C. (2017). Agricultural robots for field operations. Part 2: Operations and systems. Biosystems engineering, 153, 110-128. Search in Google Scholar

[10] Rafai, A. N. A., Adzhar, N., & Jaini, N. I. (2022). A review on path planning and obstacle avoidance algorithms for autonomous mobile robots. Journal of Robotics, 2022(1), 2538220. Search in Google Scholar

[11] Cao, X., Zou, X., Jia, C., Chen, M., & Zeng, Z. (2019). RRT-based path planning for an intelligent litchi-picking manipulator. Computers and electronics in agriculture, 156, 105-118. Search in Google Scholar

[12] Utamima, A., Reiners, T., & Ansaripoor, A. H. (2019). Optimisation of agricultural routing planning in field logistics with Evolutionary Hybrid Neighbourhood Search. Biosystems Engineering, 184, 166-180. Search in Google Scholar

[13] Mahmud, M. S. A., Abidin, M. S. Z., Mohamed, Z., Abd Rahman, M. K. I., & Iida, M. (2019). Multi-objective path planner for an agricultural mobile robot in a virtual greenhouse environment. Computers and electronics in agriculture, 157, 488-499. Search in Google Scholar

[14] Edwards, G. T., Hinge, J., Skou-Nielsen, N., Villa-Henriksen, A., Sørensen, C. A. G., & Green, O. (2017). Route planning evaluation of a prototype optimised infield route planner for neutral material flow agricultural operations. Biosystems Engineering, 153, 149-157. Search in Google Scholar

[15] Moysiadis, V., Tsolakis, N., Katikaridis, D., Sørensen, C. G., Pearson, S., & Bochtis, D. (2020). Mobile robotics in agricultural operations: A narrative review on planning aspects. Applied Sciences, 10(10), 3453. Search in Google Scholar

[16] Kiani, F., Seyyedabbasi, A., Nematzadeh, S., Candan, F., Çevik, T., Anka, F. A., ... & Muzirafuti, A. (2022). Adaptive metaheuristic-based methods for autonomous robot path planning: sustainable agricultural applications. Applied Sciences, 12(3), 943. Search in Google Scholar

[17] Plessen, M. M. G., & Bemporad, A. (2017). Reference trajectory planning under constraints and path tracking using linear time-varying model predictive control for agricultural machines. Biosystems Engineering, 153, 28-41. Search in Google Scholar

[18] Rodias, E., Berruto, R., Busato, P., Bochtis, D., Sørensen, C. G., & Zhou, K. (2017). Energy savings from optimised in-field route planning for agricultural machinery. Sustainability, 9(11), 1956. Search in Google Scholar

[19] Santos, L. C., Santos, F. N., Pires, E. S., Valente, A., Costa, P., & Magalhães, S. (2020, April). Path planning for ground robots in agriculture: A short review. In 2020 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) (pp. 61-66). IEEE. Search in Google Scholar

[20] Zhang, L., Li, R., Li, Z., Meng, Y., Liang, J., Fu, L., ... & Li, S. (2021). A quadratic traversal algorithm of shortest weeding path planning for agricultural mobile robots in cornfield. Journal of Robotics, 2021(1), 6633139. Search in Google Scholar

[21] Shen, M., Wang, S., Wang, S., & Su, Y. (2020). Simulation study on coverage path planning of autonomous tasks in hilly farmland based on energy consumption model. Mathematical problems in engineering, 2020(1), 4535734. Search in Google Scholar

[22] Man, Z., Yuhan, J. I., Shichao, L., Ruyue, C. A. O., Hongzhen, X. U., & Zhenqian, Z. H. A. N. G. (2020). Research progress of agricultural machinery navigation technology. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 51(4). Search in Google Scholar

[23] Muthukumaran, S., Ganesan, M., Dhanasekar, J., & Babu Loganathan, G. (2021). Path Planning Optimization for Agricultural Spraying Robots Using Hybrid Dragonfly–Cuckoo Search Algorithm. Alinteri Journal of Agriculture Sciences, 36(1), 2564-7814. Search in Google Scholar

[24] Xiong, Y., Ge, Y., Liang, Y., & Blackmore, S. (2017). Development of a prototype robot and fast path-planning algorithm for static laser weeding. Computers and Electronics in Agriculture, 142, 494-503. Search in Google Scholar

[25] Saghi Hassan,Nezhad Mohammad Reza Sarani,Saghi Reza & Sahneh Sepehr Partovi.(2024).Comparison of Artificial Neural Networks and Genetic Algorithms for Predicting Liquid Sloshing Parameters.Journal of Marine Science and Application(2),292-301. Search in Google Scholar

[26] Seyed Ehsan Hosseini,Omid Karimi & Mohammad Ali AsemanBakhsh.(2024).Experimental investigation and multi-objective optimization of savonius wind turbine based on modified non-dominated sorting genetic algorithm-II.Wind Engineering(3),446-467. Search in Google Scholar

[27] Garthe David,Schmidt Carsten & Denkena Berend.(2024).Lightweight Design in Agricultural Machinery - CFRP Chassisfor Forage Harvesters.ATZheavy duty worldwide(1),32-37 Search in Google Scholar