Cooperative Grape Harvesting Using Heterogeneous Autonomous Robots

Lytridis, Chris; Bazinas, Christos; Kalathas, Ioannis; Siavalas, George; Tsakmakis, Christos; Spirantis, Theodoros; Badeka, Eftichia; Pachidis, Theodore; Kaburlasos, Vassilis G.

doi:10.3390/robotics12060147

Cited by 6 publications

(3 citation statements)

References 44 publications

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“…The manual labor-intensive process of hand-harvesting fruit is associated with substantial costs [46]. Advancing technologies related to grape-picking robots can enhance grape harvesting efficiency, mitigate labor costs and shortages, and bolster the economic viability of the grape industry [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s

Wang,

Shi,

Liu

et al. 2024

Agriculture

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Rising labor costs and a workforce shortage have impeded the development and economic benefits of the global grape industry. Research and development of intelligent grape harvesting technologies is desperately needed. Therefore, rapid and accurate identification of grapes is crucial for intelligent grape harvesting. However, object detection algorithms encounter multiple challenges in unstructured vineyards, such as similar background colors, light obstruction from greenhouses and leaves, and fruit occlusion. All of these factors contribute to the difficulty of correctly identifying grapes. The GrapeDetectNet (GDN), based on the YOLO (You Only Look Once) v5s, is proposed to improve grape detection accuracy and recall in unstructured vineyards. dual-channel feature extraction attention (DCFE) is a new attention structure introduced in GDN. We also use dynamic snake convolution (DS-Conv) in the backbone network. We collected an independent dataset of 1280 images after a strict selection process to evaluate GDN’s performance. The dataset encompasses examples of Shine Muscat and unripe Kyoho grapes, covering a range of complex outdoor situations. The results of the experiment demonstrate that GDN performed outstandingly on this dataset. Compared to YOLOv5s, this model increased metrics such as 2.02% of mAP0.5:0.95, 2.5% of mAP0.5, 1.4% of precision, 1.6% of recall, and 1.5% of F1 score. Finally, we test the method on a grape-picking robot, and the results show that our algorithm works remarkably well in harvesting experiments. The results indicate that the GDN grape detection model in this study exhibits high detection accuracy. It is proficient in identifying grapes and demonstrates good robustness in unstructured vineyards, providing a valuable empirical reference for the practical application of intelligent grape harvesting technology.

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Section: Discussionmentioning

confidence: 99%

An Unstructured Orchard Grape Detection Method Utilizing YOLOv5s

Wang,

Shi,

Liu

et al. 2024

Agriculture

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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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“…In many cases, multiple cooperating robots can be deployed to reduce task duration, perform an operation not possible with a single robot, or perform an operation more effectively. In [11], a cooperation strategy is demonstrated that allows two heterogeneous robots to collaborate to harvest grapes, and its implementation is demonstrated. More specifically, the cooperative grape harvesting task involves two heterogeneous robots, where one robot (i.e., the expert) is assigned the grape harvesting task, whereas the second robot (i.e., the helper) is tasked with supporting the harvesting task by carrying the harvested grapes.…”

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confidence: 99%