An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation

Tao, Yongting; Zhou, Jun; Wang, Mingjun; Zhang, Na; Meng, Yimeng

doi:10.1177/1729881417724190

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…The outcomes of the fitting process notably revealed that the coefficient of determination (R 2 ) surpassed 0.99 for all fittings, confirming the efficacy of the selected model. Similar conclusions were found in the study by Tao et al [34]. The shape of the creep behavior curve is profoundly influenced by the developmental stage of tomatoes, as illustrated in Figure 6.…”

Section: Creep Testsupporting

confidence: 89%

Study on Mechanical Properties of Tomatoes for the End-Effector Design of the Harvesting Robot

Zheng,

He,

Jia

et al. 2023

Agriculture

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Agricultural robotics has emerged as a research area within robotics, with a particular focus on designing end effectors that are adapted to the physical characteristics of the target fruits. Acquiring a comprehensive understanding of the physical and mechanical properties specific to tomato fruits not only minimizes mechanical damage during grasping processes but also serves as a foundation for the optimal design of gripping components. In this study, the Syngenta Sibede variety of tomatoes was used as the experimental material. The reversible viscoelastic behavior and deformation characteristics of tomato fruits were approximated using a four-element Burgers model through creep testing. The fitting coefficients for the model exceeded 0.99. The creep parameters for the four ripening stages of tomatoes were obtained, and the correlation between the ripening stage, deformation value, and creep parameters was analyzed. Correlation analysis was performed to examine the relationships between each parameter and creep deformation, revealing significant and highly significant correlations. Inter-parameter correlations were also found to be highly significant. Puncture tests were conducted on tomatoes. The exocarp rupture force of the green-ripening stage was 9.224 ± 0.901 N, which was 53.87%, 70.63%, and 104.01% higher than that of the semi-ripening stage, early firm-ripening stage, and mid-late firm-ripening stage, respectively. This study suggests that when harvesting tomatoes at the semi-ripening stage and beyond, attention should be paid to trimming the stem. Compression experiments were conducted on tomatoes, and it was discovered that under the same ripening stage, the axial compressive rupture force of tomatoes was greater than the radial rupture force. Tomatoes exhibited anisotropic behavior. The grasping direction is axial, which can be used as the new design direction of the end-effector.

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Section: Creep Testsupporting

confidence: 89%

Study on Mechanical Properties of Tomatoes for the End-Effector Design of the Harvesting Robot

Zheng,

He,

Jia

et al. 2023

Agriculture

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“…Agriculture Robots are already investigated in the precision farming topic. Recent research in this area covers the adaptability of robot design to the agriculture sector, the improvement of navigation conditions through additional sensing [82,83,203] and localization capabilities as well as real-time image processing [84,85] and camera detection [86] to maximize the operational capabilities and behaviour of robots and collaborative robots (cobots). Robots can assist humans [87,88] for difficult tasks or replace them for difficult ones.…”

Section: -Precision Farming Techniques and Robot Developmentmentioning

confidence: 99%

Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

Lezoche

Hernández

Ruiz

et al. 2020

Computers in Industry

571

339

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income.pdf 4.0 should help meeting sustainable challenges by increasing the agri-food supply chain stakeholders revenues as well as decreasing their pressure for handling complex and external factors they cannot control, such as weather, market behaviours and policies, but also to react on time by visualising current trends in needs. This paper focus principally on the factory farm model issued from the European and north American style. In those typologies of farms the Industry 4.0 elements can be introduced and merged with the agricultural domain to create the Agri-Food 4.0. The focus is more specialised on the industrial farming although the Agri-Food 4.0 is started to be adapted also to the organic and silvo-pasture paradigms. Thus, this paper, considering a review over than two hundred papers, provides a contribution to knowledge by establishing the linkages between new 4.0 trends in technologies and Agri-Food Supply Chain Challenges, which opens the 4.0 research field for a multidisciplinary work. The choice of the literature review methodology focused firstly on key words related to the Agri-food technology, then the second approach was to focus on selected journals and at the end we looked for the data-based repository and the linked publications. To accomplish this, the structure in this paper is as follows: in the first place, a compressive state of the art of Agri-Food 4.0 related technologies is covered. In the second place, a review and contribution coming from digital technologies to the new supply chain methods in Agri-Food shows linkages with next trends and technologies. Next, and linked to the agri-food supply chain challenges, the fourth section presents new trends and models in agri-food supply chains, especially in the domain of risk management, collaboration, governance, cold chain management, globalization, information and communication technologies, Logistics, supply chain structures and sustainable agri-food supply chains. Finally, the fifth section covers the main conclusions, visions and perspectives for this novel research. 2-Agri-food 4.0 and Technologies a State of the Art The agricultural sector has been active in digital innovation for decades already. Especially the advances in Precision Agriculture, remote sensing, robots, farm management information

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“…Nevertheless, the traditional methods, such as tactile, visual, and visuo‐tactile servoing methods consider the physical properties [12] limiting its robustness over the data‐driven learning‐based methods for grasping soft fruits. Supervised learning‐based identification of grasp locations from an image was presented [13, 14] predicted grasp locations without severe overfitting via Convolution Neural Network (CNN).…”

Section: Introductionmentioning

confidence: 99%

Review on human‐like robot manipulation using dexterous hands

Sampath

Wang

Wu³

et al. 2023

Cognitive Comp and Systems

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In recent years, human hand‐based robotic hands or dexterous hands have gained attention due to their enormous capabilities of handling soft materials compared to traditional grippers. Back in the earlier days, the development of a hand model close to that of a human was an impossible task but with the advancements made in technology, dexterous hands with three, four or five‐fingered robotic hands have been developed to mimic human hand nature. However, human‐like manipulation of dexterous hands to this date remains a challenge. Thus, this review focuses on (a) the history and motivation behind the development of dexterous hands, (b) a brief overview of the available multi‐fingered hands, and (c) learning‐based methods such as traditional and data‐driven learning methods for manipulating dexterous hands. Additionally, it discusses the challenges faced in terms of the manipulation of multi‐fingered or dexterous hands.

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An optimum strategy for robotic tomato grasping based on real-time viscoelastic parameters estimation

Cited by 5 publications

References 10 publications

Study on Mechanical Properties of Tomatoes for the End-Effector Design of the Harvesting Robot

Study on Mechanical Properties of Tomatoes for the End-Effector Design of the Harvesting Robot

Agri-food 4.0: A survey of the supply chains and technologies for the future agriculture

Review on human‐like robot manipulation using dexterous hands

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