An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Tsiakas, Kosmas; Papadimitriou, Alexios; Pechlivani, Eleftheria Maria; Giakoumis, Dimitrios; Frangakis, Nikolaos; Gasteratos, Antonios; Tzovaras, Dimitrios

doi:10.3390/robotics12060146

Cited by 8 publications

(4 citation statements)

References 33 publications

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“…In the articles [54][55][56][57][58][59][60], the authors conducted studies similar to ours. The Portuguese researchers D. Gomes et al proposed automatic shape and position detection using a two-dimensional (2D) industrial laser to extract three-dimensional (3D) data, where object motion adds a third dimension through the laser beam [54].…”

Section: Comparison Of Laboratory and Field Research Resultsmentioning

confidence: 81%

“…In the study [57], the authors from Greece conducted the closest research to ours for a rail robot in a greenhouse-they built a route trajectory and visualized the movement using a LiDAR sensor. In this case, the mathematical apparatus of finite-state machines was used, as well as neural networks (YOLO v.8).…”

Section: Comparison Of Laboratory and Field Research Resultsmentioning

confidence: 99%

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Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Kutyrev,

Kiktev,

Smirnov

2024

Sensors

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This article presents a developed motion control system for a robotic platform based on laser-ranging methods, a graph traversal algorithm and the search for the optimal path. The algorithm was implemented in an agricultural building and in the field. As a result, the most efficient algorithm for finding the optimal path (A*) for the robotic platform was chosen when performing various technological operations. In the Rviz visualization environment, a program code was developed for planning the movement path and setting the points of the movement trajectory in real time. To find the optimal navigation graph in an artificial garden, an application was developed using the C# programming language and Visual Studio 2019. The results of the experiments showed that field conditions can differ significantly from laboratory conditions, while the positioning accuracy is significantly lower. The statistical processing of the experimental data showed that, for the movement of a robotic platform along a given trajectory in the field, the most effective conditions are as follows: speed: 2.5 km/h; illumination: 109,600 lux; distance to the tree: 0.5 m. An analysis of the operating parameters of the LiDAR sensor showed that it provides a high degree of positioning accuracy under various lighting conditions at various speeds in the aisles of a garden 3 m wide with an inter-stem distance of 1.5 m and a tree crown width of 0.5 m. The use of sensors—rangefinders of the optical range—allows for the performance of positional movements of the robotic platform and ensures the autonomous performance of the basic technological operations of the units in intensive gardens with a deviation from the specified trajectory of no more than 8.4 cm, which meets the agrotechnical requirements.

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Section: Comparison Of Laboratory and Field Research Resultsmentioning

confidence: 81%

Section: Comparison Of Laboratory and Field Research Resultsmentioning

confidence: 99%

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Kutyrev,

Kiktev,

Smirnov

2024

Sensors

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“…The methodology section outlines the dataset utilized, details the object detection models implemented, and describes the evaluation metrics adopted to assess the effectiveness of these models in the detection of T. absoluta infestation on tomato plants. The model is tailored to run on a mobile platform aboard a fully autonomous mobile robot [27]. The collected data is processed within a robust decision support system [28] to ensure precise detection and evaluation of the infestations.…”

Section: Methodsmentioning

confidence: 99%

Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning

Giakoumoglou,

Pechlivani,

Frangakis

et al. 2023

Self Cite

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Early detection and efficient management practices to control Tuta absoluta (Meyrick) infestation is crucial for safeguarding tomato production yield and minimizing economic losses. This study investigates the detection of T. absoluta infestation on tomato plants using object detection models combined with ensemble techniques. Additionally, this study highlights the importance of utilizing a dataset captured in real settings in open-field and greenhouse environments to address the complexity of real-life challenges in object detection of plant health scenarios. The effectiveness of deep-learning-based models, including Faster R-CNN and RetinaNet, was evaluated in terms of detecting T. absoluta damage. The initial model evaluations revealed diminishing performance levels across various model configurations, including different backbones and heads. To enhance detection predictions and improve mean Average Precision (mAP) scores, ensemble techniques were applied such as Non-Maximum Suppression (NMS), Soft Non-Maximum Suppression (Soft NMS), Non-Maximum Weighted (NMW), and Weighted Boxes Fusion (WBF). The outcomes shown that the WBF technique significantly improved the mAP scores, resulting in a 20% improvement from 0.58 (max mAP from individual models) to 0.70. The results of this study contribute to the field of agricultural pest detection by emphasizing the potential of deep learning and ensemble techniques in improving the accuracy and reliability of object detection models.

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“…Over the past few years, there has been a significant expansion in the domains where robotics is employed. Beyond the conventional industrial applications such as material handling, welding, and machine tending, contemporary robotics finds its way into diverse sectors such as the construction sector [1], rescue robotics [2], agricultural [3,4] and food industry [5], waste management [6], entertainment [7], security systems [8], etc.…”

Section: Introductionmentioning

confidence: 99%

Applying Screw Theory to Design the Turmell-Bot: A Cable-Driven, Reconfigurable Ankle Rehabilitation Parallel Robot

Vargas-Riaño,

Agudelo-Varela,

Valera

2023

Robotics

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The ankle is a complex joint with a high injury incidence. Rehabilitation Robotics applied to the ankle is a very active research field. We present the kinematics and statics of a cable-driven reconfigurable ankle rehabilitation robot. First, we studied how the tendons pull mid-foot bones around the talocrural and subtalar axes. We proposed a hybrid serial-parallel mechanism analogous to the ankle. Then, using screw theory, we synthesized a cable-driven robot with the human ankle in the closed-loop kinematics. We incorporated a draw-wire sensor to measure the axes’ pose and compute the product of exponentials. We also reconfigured the cables to balance the tension and pressure forces using the axis projection on the base and platform planes. Furthermore, we computed the workspace to show that the reconfigurable design fits several sizes. The data used are from anthropometry and statistics. Finally, we validated the robot’s statics with MuJoCo for various cable length groups corresponding to the axes’ range of motion. We suggested a platform adjusting system and an alignment method. The design is lightweight, and the cable-driven robot has advantages over rigid parallel robots, such as Stewart platforms. We will use compliant actuators for enhancing human–robot interaction.

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An Autonomous Navigation Framework for Holonomic Mobile Robots in Confined Agricultural Environments

Cited by 8 publications

References 33 publications

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Laser Rangefinder Methods: Autonomous-Vehicle Trajectory Control in Horticultural Plantings

Enhancing Tuta absoluta Detection on Tomato Plants: Ensemble Techniques and Deep Learning

Applying Screw Theory to Design the Turmell-Bot: A Cable-Driven, Reconfigurable Ankle Rehabilitation Parallel Robot

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