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][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. In Researchersthis paper, we 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.