NEUROCOGNITIVE ALGORITHMS FOR MANAGING MULTI-AGENT ROBOTICS SYSTEM FOR AGRICULTURAL PURPOSES
Abstract
The main goals of the introduction of robots into agriculture are to increase efficiency and performance,
fulfilling labor -intensive and dangerous tasks and solving the issue of lack of labor. Technological
achievements in the field of detection and management, as well as machine learning allowed autonomous
robots to perform more agricultural tasks. Such tasks vary at all stages of cultivation: from preparation of
land and sowing to monitoring and harvesting. Some agricultural robots are already available, and it is
expected that in the coming years there will be even more, since technologies for processing big data, machine vision and easy capture are becoming more accurate. Currently, the introduction of several interacting
robots in the field is becoming increasingly relevant, since it has good prospects in reducing
production costs and increasing operating efficiency. The purpose of this study is to develop an intellectual
system for managing a mobile robot group based on multi -agent neurocognitive architectures. The task
of the study is to develop neurocognitive algorithms for controlling the multi -agent robotics system of
agricultural purposes. The work describes a multi -agent robotics complex for active plant protection
within the framework of the Smart Field system. The concept of the management system of the group of
mobile robots based on modeling multi -group neurocognitive architectures is presented. To ensure the
work of the multi -agent heterogeneous group of autonomous robots, the use of a neurocognitive control
model with the implementation of individual intellectual agents is proposed on each individual robot and
at the bases of service or servers. At the same time, given the implementation of recursing in architecture
itself, the task of scaling such a management system is noticeably simplified. The use of sensors and effectors
to ensure the exchange of knowledge between robots and decision -making centers allows minimizing
the load on the communication system and ensure a reserve of failure tolerance of the management system.
The results obtained can be used to develop universal control systems and simplification for various
groups of autonomous robots.
References
Agronomy Journal. – 2019. – Vol. 111, No. 4. – P. 1552-1569.
2. Pierce F.J., Nowak P. Aspects of precision agriculture // Advances in agronomy. – 1999. – Vol. 67.
– P. 1-85.
3. Stafford J.V. Implementing precision agriculture in the 21st century // Journal of agricultural engineering
research. – 2000. – Vol. 76, No. 3. – P. 267-275.
4. Marinoudi V. et al. Robotics and labour in agriculture. A context consideration // Biosystems Engineering.
– 2019. – Vol. 184. – P. 111-121.
5. Fountas S. et al. Agricultural robotics for field operations // Sensors. – 2020. – Vol. 20, No. 9.
– P. 2672.
6. Oliveira L.F.P., Moreira A. P., Silva M.F. Advances in agriculture robotics: A state-of-the-art review
and challenges ahead // Robotics. – 2021. – Vol. 10, No. 2. – P. 52.
7. Lytridis C. et al. An overview of cooperative robotics in agriculture // Agronomy. – 2021. – Vol. 11,
No. 9. – P. 1109-1818. – https://doi.org/10.3390/agronomy11091818.
8. Yerebakan M.O., Hu B. Human–Robot Collaboration in Modern Agriculture: A Review of the Current
Research Landscape // Advanced Intelligent Systems. – 2024. – P. 2300823.
9. Bechar A., Edan Y. Human‐robot collaboration for improved target recognition of agricultural robots //
Industrial Robot: An International Journal. – 2003. – Vol. 30, No. 5. – P. 432-436.
10. Tkach I., Bechar A., Edan Y. Switching between collaboration levels in a human–robot target recognition
system // IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).
– 2011. – Vol. 41, No. 6. – P. 955-967.
11. Berenstein R., Edan Y. Human‐robot collaborative site‐specific sprayer // Journal of Field Robotics.
– 2017. – Vol. 34, No. 8. – P. 1519-1530.
12. Adamides G. et al. HRI usability evaluation of interaction modes for a teleoperated agricultural robotic
sprayer // Applied ergonomics. – 2017. – Vol. 62. – P. 237-246.
13. Bergerman M. et al. Robot farmers: Autonomous orchard vehicles help tree fruit production // IEEE
Robotics & Automation Magazine. – 2015. – Vol. 22, No. 1. – P. 54-63.
14. Dusadeerungsikul P O., Nof S.Y. A collaborative control protocol for agricultural robot routing with
online adaptation // Computers & Industrial Engineering. – 2019. – Vol. 135. – P. 456-466.
15. Seyyedhasani H. et al. Collaboration of human pickers and crop-transporting robots during harvesting–
Part I: Model and simulator development // Computers and electronics in agriculture. – 2020.
– Vol. 172. – P. 105324.
16. Seyyedhasani H. et al. Collaboration of human pickers and crop-transporting robots during harvesting–
Part II: Simulator evaluation and robot-scheduling case-study // Computers and electronics in agriculture.
– 2020. – Vol. 172. – P. 105323.
17. Tiotsop L.F., Servetti A., Masala E. An integer linear programming model for efficient scheduling of
UGV tasks in precision agriculture under human supervision // Computers & Operations Research.
– 2020. – Vol. 114. – P. 104826.
18. Doering D. et al. Design and optimization of a heterogeneous platform for multiple UAV use in precision
agriculture applications // IFAC Proceedings Volumes. – 2014. – Vol. 47, No. 3. – P. 12272-12277.
19. del Cerro J. et al. Unmanned aerial vehicles in agriculture: A survey // Agronomy. – 2021. – Vol. 11,
No. 2. – P. 203-215.
20. Albani D. et al. Field coverage for weed mapping: toward experiments with a UAV swarm // Bioinspired
Information and Communication Technologies: 11th EAI International Conference, BICT
2019, Pittsburgh, PA, USA, March 13–14, 2019, Proceedings 11. – Springer International Publishing,
2019. – P. 132-146.
21. Ju C., Son H.I. A distributed swarm control for an agricultural multiple unmanned aerial vehicle system
// Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control
Engineering. – 2019. – Vol. 233, No. 10. – P. 1298-1308.
22. Ju C., Son H.I. Multiple UAV systems for agricultural applications: Control, implementation, and
evaluation // Electronics. – 2018. – Vol. 7, No. 9. – P. 162.
23. Vu Q. et al. Trends in development of UAV-UGV cooperation approaches in precision agriculture //
Interactive Collaborative Robotics: Third International Conference, ICR 2018, Leipzig, Germany,
September 18–22, 2018, Proceedings 3. – Springer International Publishing, 2018. – P. 213-221.
24. Ni J. et al. An improved real-time path planning method based on dragonfly algorithm for heterogeneous
multi-robot system // IEEE Access. – 2020. – Vol. 8. – P. 140558-140568.
25. Ju C., Son H.I. Hybrid systems based modeling and control of heterogeneous agricultural robots for
field operations // 2019 ASABE Annual International Meeting. – American Society of Agricultural
and Biological Engineers, 2019. – P. 3-15.
26. Potena C. et al. AgriColMap: Aerial-ground collaborative 3D mapping for precision farming // IEEE
Robotics and Automation Letters. – 2019. – Vol. 4, No. 2. – P. 1085-1092.
27. Wang Z., McDonald S.T. Convex relaxation for optimal rendezvous of unmanned aerial and ground
vehicles // Aerospace Science and Technology. – 2020. – Vol. 99. – P. 105756.
28. Peterson J. et al. Experiments in unmanned aerial vehicle/unmanned ground vehicle radiation search //
Journal of Field Robotics. – 2019. – Vol. 36, No. 4. – P. 818-845.
29. Нагоев З.В., Шуганов В.М., Заммоев А.У., Бжихатлов К.Ч., Иванов З.З. Разработка интеллекту-
альной интегрированной системы «Умное поле» // Известия ЮФУ. Технические науки. – 2022.
– № 1. – С. 81-91. – DOI: https://doi.org/10.18522/2311-3103-2022-1-81-91.
30. Bzhikhatlov K., Pshenokova I. Intelligent Spraying System of Autonomous Mobile Agricultural Robot
// International Conference on Agriculture Digitalization and Organic Production. – Singapore :
Springer Nature Singapore, 2023. – P. 269-278. – https://doi.org/10.1007/978-981-99-4165-0_25.
31. Нагоев З.В. Интеллектика, или Мышление в живых и искусственных системах. – Нальчик: Изд-
во КБНЦ РАН, 2013. – 211 c.
