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Tsiakas, K.; Papadimitriou, A.; Pechlivani, E.M.; Giakoumis, D.; Frangakis, N.; Gasteratos, A.; Tzovaras, D. Autonomous Navigation Framework for Holonomic Mobile Robots. Encyclopedia. Available online: https://encyclopedia.pub/entry/51439 (accessed on 16 November 2024).
Tsiakas K, Papadimitriou A, Pechlivani EM, Giakoumis D, Frangakis N, Gasteratos A, et al. Autonomous Navigation Framework for Holonomic Mobile Robots. Encyclopedia. Available at: https://encyclopedia.pub/entry/51439. Accessed November 16, 2024.
Tsiakas, Kosmas, Alexios Papadimitriou, Eleftheria Maria Pechlivani, Dimitrios Giakoumis, Nikolaos Frangakis, Antonios Gasteratos, Dimitrios Tzovaras. "Autonomous Navigation Framework for Holonomic Mobile Robots" Encyclopedia, https://encyclopedia.pub/entry/51439 (accessed November 16, 2024).
Tsiakas, K., Papadimitriou, A., Pechlivani, E.M., Giakoumis, D., Frangakis, N., Gasteratos, A., & Tzovaras, D. (2023, November 10). Autonomous Navigation Framework for Holonomic Mobile Robots. In Encyclopedia. https://encyclopedia.pub/entry/51439
Tsiakas, Kosmas, et al. "Autonomous Navigation Framework for Holonomic Mobile Robots." Encyclopedia. Web. 10 November, 2023.
Autonomous Navigation Framework for Holonomic Mobile Robots
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This entry is adapted from the peer-reviewed paper 10.3390/robotics12060146

Due to the accelerated growth of the world’s population, food security and sustainable agricultural practices have become essential. The incorporation of Artificial Intelligence (AI)-enabled robotic systems in cultivation, especially in greenhouse environments, represents a promising solution, where the utilization of the confined infrastructure improves the efficacy and accuracy of numerous agricultural duties.

autonomous navigation agricultural robots mobile robots

1. Introduction

With the rapid expansion of the global population, the imperative to address the issues of food security and sustainable farming techniques has gained significant urgency. To meet the growing demand for food production, the agricultural sector has been driven to the excessive use of pesticides and herbicides [1], resulting in a significant impact on the environment and the surrounding ecosystem [2]. Consequently, it is imperative to employ alternative approaches to pest control by embracing innovative technologies and procedures that enhance effectiveness while minimizing negative ecological impacts. In the present context, the integration of robotics into the agricultural industry has emerged as a highly promising and transformational option, leading to significant changes in traditional farming methods. Additionally, current threats to agriculture, such as climate change and invasive pests, need to be urgently solved and AI-enabled robotic systems can significantly contribute to that, even though there are still numerous challenges and limitations to be solved [3].
The utilization of greenhouses, which include regulated settings, has historically been crucial in the optimization of agricultural development and the efficient management of resources. They provide a controlled environment that protects crops from the unpredictable fluctuations of the external climate and facilitates optimal conditions for crop growth, leading to extended growing seasons, increased yields, and reduced potential hazards associated with extreme climate-related occurrences, such as storms, frost, and excessive heat [4]. Moreover, greenhouses contribute to the establishment of a more secure and conducive working environment for agricultural workers [5]. Through the use of strategies such as limiting exposure to outdoor elements and employing controlled pest management techniques, the potential health hazards linked to chronic chemical exposure are mitigated. Nevertheless, the effective administration of greenhouses has its own array of difficulties. The conventional approaches frequently prove inadequate in attaining accurate management of variables such as temperature, humidity, and irrigation, resulting in unsatisfactory crop production and inefficient use of resources [6]. The implementation of autonomous navigation for robotic platforms in greenhouses promises a paradigm shift in tackling these difficulties, heralding a novel era of intelligent and environmentally-friendly agriculture.
In recent times, the agricultural sector has experienced a significant transformation due to the integration of robotics technology. This advancement has facilitated the automation of labor-intensive and repetitive activities, such as spraying, resulting in reduced labor expenses and enhanced operational effectiveness [7]. According to Fountas et al. [8], wheeled mobile robots possess notable attributes such as swift locomotion, extensive operational independence, and substantial load-carrying capacity. Additionally, these robots demonstrate the capability to navigate through challenging topographies and the implementation of omnidirectional kinematics enhances the versatility of wheeled robots, enabling them to effectively perform multiple tasks, including spot spraying.
Greenhouse environments are well adapted for automation using mobile robots since they are structured and are not frequently modified. Typically, plant benches are arranged in long, parallel aisles. These benches are interspersed with corridors, which typically contain a pair of long pipelines for controlling the temperature. Intriguingly, these pipes also serve as rails for diverse varieties of plant treatment equipment and serve as an ideal guide for the robot’s course, assuming it can utilize them effectively [9]. In addition, the greenhouse’s headland, which is the area outside of the corridors, is typically covered with a durable flat concrete floor.
By leveraging the existing infrastructure within greenhouses, mobile robots can navigate along these rails, enhancing their efficiency and precision during various tasks. This utilization of the greenhouse layout not only provides a well-defined path for the robot but also ensures that it can operate seamlessly alongside the existing infrastructure. The successful autonomous navigation of a mobile platform relies heavily on factors such as localization and mapping accuracy, path planning, and motion control. In the case of greenhouses, their semi-indoor nature poses limitations on the effectiveness of conventional approaches used in outdoor scenarios, such as those based on the Global Navigation Satellite System (GNSS) [10], due to restricted satellite reception. Additionally, standard indoor approaches [11] are sub-optimal due to the rapidly changing environment caused by plant growth.
For the spraying task, different approaches have been introduced by the research community. Unmanned aerial vehicles (UAVs) have proven to be able to cover large distance in minimal time, especially in open fields. Despite their wide adoption, UAV sprayers do not tackle the problem of excessive pesticide use, since the spraying is performed from high altitude, spreading the pesticides above the field [12]. Another approach is the use of legged robots [13], which are highly flexible and well suited for applications with rough terrain or steep slopes and can handle precise spot spraying. However, their payload is limited and they usually move at slow speeds when operating in rough terrains. Wheeled robots are characterized by fast moving speeds and high payload, while they are able to move in rugged terrain [8]. Omnidirectional wheeled robots provide a versatile approach in agriculture which is able to handle tasks precisely and efficiently without compromising payload and speed both in open fields and greenhouses [9][14].
Considering the importance of automated processes within greenhouse settings and the advanced capabilities of contemporary robotic systems, it becomes clear that there is a substantial scope for further exploration in this direction. Researchers introduce a comprehensive autonomous navigation architecture of a holonomic mobile robot in greenhouses. The proposed method relies on the rails formed by the heating system of the greenhouse and can be performed using a single stereo camera. The overall architecture consists of discrete states that are orchestrated by incorporating a finite state machine, allowing the complete execution of a task in a fully automated manner.

2. Autonomous Navigation Framework for Holonomic Mobile Robots

Robotic systems and artificial intelligence methods have been widely adopted in every aspect of agricultural operations, including apple picking [15], strawberries harvesting [16], and grape detection [17].
Autonomous navigation of robotic platforms in agricultural environments still remains an open issue, despite the plenty of related works present in bibliography. In contrast with greenhouses, agricultural robots operating in outdoor open-fields might take advantage of GNSS measurements, combined with crop row detection to perform accurate localization and crop row following techniques [10]. An early work from González et al. [18] present a map-based navigation technique, utilizing a voronoi diagram for path planning and corridor centering using sonar measurements. A robotic system capable of navigating in greenhouse environments for the purpose of ultraviolet (UV) treatment of cucumber plants is described in [9]. The authors present its capability to navigate both in the headland and the heating pipes, detect the corridor starting points from the given map and estimate the robot’s pose relative to the rails given solely a 3D camera. Jiang et al. [19] combine 2D and 3D Simultaneous Localization And Mapping (SLAM) algorithms for greenhouse positioning, through the conversion of 3D pointcloud data into laser scan format, in order to perform navigation using Dynamic Window Approach (DWA) in a pre-defined occupancy grid map. Indoor greenhouse navigation of a mobile robot is demonstrated by [20] with the utilization of Hector SLAM for pose estimation and an Artifical Potential Field (APF) for autonomous navigation. Another operation of an intelligent vehicle operating in a commercial greenhouse is described in [21]. Navigation is performed both in rails and horizontal surfaces using two separate wheel drive mechanisms, while localization heavily relies on fiducial tags. Similarly, ref. [22] describes the operation of a spraying robot equipped with both mecanum wheels and a roller mechanism for navigation, while QR codes placed on the beginning and end of each corridor orchestrate the mission strategy.
The potential of combining navigation and perception has been demonstrated through real-world applications involving outdoor field robots, as described in [23]. The robotic system presented is capable of navigating between plant rows by taking advantage of standardized planting schemes, and performs full coverage throughout the field, solely using onboard cameras. A similar approach is presented in [24], with a visual-based navigation scheme that utilizes multiple crop-rows to navigate a BonnBot-I robot in five different fields. In [25], fundamental image processing algorithms, including Hough transform and Otsu thresholding, are employed for the segmentation of soil and plants in an image and, finally, the extraction of navigation trajectories in a greenhouse environment. A recent work that combines semantic perception with navigation for a robotic platform in agricultural fields is presented by [26]. This work is focused on open-field crops, such as canola and cucumber, and utilizes an end-to-end neural network for semantic line detection throughout the straight lines of crops.

References

  1. Sarkar, S.; Gil, J.D.B.; Keeley, J.; Jansen, K. The Use of Pesticides in Developing Countries and Their Impact on Health and the Right to Food; European Union: Maastricht, The Netherlands, 2021.
  2. Sharma, A.; Kumar, V.; Shahzad, B.; Tanveer, M.; Sidhu, G.P.S.; Handa, N.; Kohli, S.K.; Yadav, P.; Bali, A.S.; Parihar, R.D.; et al. Worldwide pesticide usage and its impacts on ecosystem. SN Appl. Sci. 2019, 1, 1446.
  3. Balaska, V.; Adamidou, Z.; Vryzas, Z.; Gasteratos, A. Sustainable Crop Protection via Robotics and Artificial Intelligence Solutions. Machines 2023, 11, 774.
  4. Vatistas, C.; Avgoustaki, D.D.; Bartzanas, T. A systematic literature review on controlled-environment agriculture: How vertical farms and greenhouses can influence the sustainability and footprint of urban microclimate with local food production. Atmosphere 2022, 13, 1258.
  5. Bagagiolo, G.; Matranga, G.; Cavallo, E.; Pampuro, N. Greenhouse Robots: Ultimate Solutions to Improve Automation in Protected Cropping Systems—A Review. Sustainability 2022, 14, 6436.
  6. Prathibha, S.; Hongal, A.; Jyothi, M. IoT based monitoring system in smart agriculture. In Proceedings of the 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT), Bangalore, India, 16–17 March 2017; IEEE: Piscataway, NJ, USA, 2017; pp. 81–84.
  7. Abhiram, R.; Megalingam, R.K. Autonomous Fertilizer Spraying Mobile Robot. In Proceedings of the 2022 IEEE 19th India Council International Conference (INDICON), Kochi, India, 24–26 November 2022; pp. 1–6.
  8. Fountas, S.; Mylonas, N.; Malounas, I.; Rodias, E.; Hellmann Santos, C.; Pekkeriet, E. Agricultural robotics for field operations. Sensors 2020, 20, 2672.
  9. Grimstad, L.; Zakaria, R.; Le, T.D.; From, P.J. A novel autonomous robot for greenhouse applications. In Proceedings of the 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 1–5 October 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 1–9.
  10. Winterhalter, W.; Fleckenstein, F.; Dornhege, C.; Burgard, W. Localization for precision navigation in agricultural fields—Beyond crop row following. J. Field Robot. 2021, 38, 429–451.
  11. Chan, S.H.; Wu, P.T.; Fu, L.C. Robust 2D indoor localization through laser SLAM and visual SLAM fusion. In Proceedings of the 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Miyazaki, Japan, 7–10 October 2018; IEEE: Piscataway, NJ, USA; pp. 1263–1268.
  12. Chen, H.; Lan, Y.; Fritz, B.K.; Hoffmann, W.C.; Liu, S. Review of agricultural spraying technologies for plant protection using unmanned aerial vehicle (UAV). Int. J. Agric. Biol. Eng. 2021, 14, 38–49.
  13. Bellicoso, C.D.; Bjelonic, M.; Wellhausen, L.; Holtmann, K.; Günther, F.; Tranzatto, M.; Fankhauser, P.; Hutter, M. Advances in real-world applications for legged robots. J. Field Robot. 2018, 35, 1311–1326.
  14. McCool, C.; Perez, T.; Upcroft, B. Mixtures of lightweight deep convolutional neural networks: Applied to agricultural robotics. IEEE Robot. Autom. Lett. 2017, 2, 1344–1351.
  15. Yan, B.; Fan, P.; Lei, X.; Liu, Z.; Yang, F. A real-time apple targets detection method for picking robot based on improved YOLOv5. Remote Sens. 2021, 13, 1619.
  16. Xiong, Y.; Ge, Y.; Grimstad, L.; From, P.J. An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation. J. Field Robot. 2020, 37, 202–224.
  17. Kleitsiotis, I.; Mariolis, I.; Giakoumis, D.; Likothanassis, S.; Tzovaras, D. Anisotropic Diffusion-Based Enhancement of Scene Segmentation with Instance Labels. In Proceedings of the Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, 28–30 September 2021; Springer: Berlin/Heidelberg, Germany, 2021; pp. 383–391.
  18. González, R.; Rodríguez, F.; Sánchez-Hermosilla, J.; Donaire, J.G. Navigation techniques for mobile robots in greenhouses. Appl. Eng. Agric. 2009, 25, 153–165.
  19. Jiang, S.; Wang, S.; Yi, Z.; Zhang, M.; Lv, X. Autonomous navigation system of greenhouse mobile robot based on 3D Lidar and 2D Lidar SLAM. Front. Plant Sci. 2022, 13, 815218.
  20. Harik, E.H.C.; Korsaeth, A. Combining Hector SLAM and Artificial Potential Field for Autonomous Navigation Inside a Greenhouse. Robotics 2018, 7, 22.
  21. Wu, C.; Tang, X.; Xu, X. System Design, Analysis, and Control of an Intelligent Vehicle for Transportation in Greenhouse. Agriculture 2023, 13, 1020.
  22. Fei, M.; Wendong, H.; Wu, C.; Sai, W. Design and experimental test of multi-functional intelligent vehicle for greenhouse. In Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Victoria, BC, Canada, 10–12 May 2021; IEEE: Piscataway, NJ, USA, 2021; pp. 755–760.
  23. Ahmadi, A.; Nardi, L.; Chebrolu, N.; Stachniss, C. Visual servoing-based navigation for monitoring row-crop fields. In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 31 May–31 August 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 4920–4926.
  24. Ahmadi, A.; Halstead, M.; McCool, C. Towards Autonomous Visual Navigation in Arable Fields. In Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Kyoto, Japan, 23–27 October 2022; IEEE: Piscataway, NJ, USA, 2022; pp. 6585–6592.
  25. Chen, J.; Qiang, H.; Wu, J.; Xu, G.; Wang, Z.; Liu, X. Extracting the navigation path of a tomato-cucumber greenhouse robot based on a median point Hough transform. Comput. Electron. Agric. 2020, 174, 105472.
  26. Panda, S.K.; Lee, Y.; Jawed, M.K. Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 18–22 June 2023; pp. 6271–6280.
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Subjects: Remote Sensing
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Kosmas Tsiakas
,
Alexios Papadimitriou
,
Eleftheria Maria Pechlivani
,
Dimitrios Giakoumis
,
Nikolaos Frangakis
,
Antonios Gasteratos
,
Dimitrios Tzovaras
View Times: 310
Revisions: 2 times (View History)
Update Date: 13 Nov 2023
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