Automated Fruit Quality Testing using an Electrical Impedance Tomography-Enabled Soft Robotic Gripper

Almanzor, Elijah; Thuruthel, Thomas George; Iida, Fumiya

doi:10.1109/iros47612.2022.9981987

Cited by 8 publications

(9 citation statements)

References 34 publications

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“…CC BY 4.0. monitoring through gripping and tactile sensors for ripeness assessment has also been reported in the literature [31,32]. Another method equips robotic grippers with electrodes to measure intrinsic qualities of fruits such as acidity, sugar content, and even weight through electric impedance tomography (EIT) technologies [33]. Notable examples of crop care and post-processing robots are given in figure 4.…”

Section: Weeding Crop Care and Monitoringmentioning

confidence: 98%

Soft robotics for farm to fork: applications in agriculture & farming

Armanini,

Junge,

Johnson

et al. 2024

Bioinspir. Biomim.

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Agricultural tasks and environments range from harsh field conditions with semi-structured produce or animals, through to post-processing tasks in food processing environment. From farm to fork, the development and application of soft robotics offers a plethora of potential uses. Robust yet compliant interactions between farm produce and machines and will enable new capabilities and optimize existing processes. There is also an opportunity to explore how modelling tools used in soft robotics can be applied to improve our representation and understanding of the soft and compliant structures common in agriculture. In this review we seek to highlight the potential for soft robotics technologies within the food system, and also the unique challenges that must be addressed when developing soft robotics systems for this problem domain. We conclude with an outlook on potential directions for meaningful and sustainable impact, and also how our outlook on both soft robotics and agriculture must evolve in order to achieve the required paradigm shift.

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Section: Weeding Crop Care and Monitoringmentioning

confidence: 98%

Soft robotics for farm to fork: applications in agriculture & farming

Armanini,

Junge,

Johnson

et al. 2024

Bioinspir. Biomim.

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“…A similar, yet simpler system of this type was shown to assess the firmness of an eggplant (Blanes et al, 2015). Fin Ray grippers (Crooks et al, 2016) are also widely used for fruit harvesting (Zhou et al, 2021) and can do fruit ripeness assessment (Almanzor et al, 2022) when fitted with electro-impedance tomography kit (Bera & Iriguchi, 2014). Design and testing of a similar gripper and its testing on various fruits are shown by Liu et al (2018).…”

Section: Grippers and Tactile Feedbackmentioning

confidence: 99%

“…This approach was used for tasks like salad assembly (Dexai, 2021), making hot dogs (Zabka, 2022), frying sausages (Mauch et al, 2017), making scrambled F I G U R E 1 Collage of robotic grippers especially useful for food handling and sensing. (a) Sensorized pneumatic gripper handling a potato chip (Hughes et al, 2020), (b) sensorized gripper able to assess cooked-ness (Sochacki, Hughes, et al, 2022), (c) gripper using conductance measurement to assess ripeness (Almanzor et al, 2022), (d) soft gripper for fruit harvesting (Zhou et al, 2021), (e) tactile feedback to assess mango ripeness (Scimeca et al, 2019), (f) vacuum cup gripper peels lettuce (Hughes et al, 2018), and (g) gripper with chemical sensor at its fingertips (Ciui et al, 2018).…”

Section: Integrationmentioning

confidence: 99%

Towards practical robotic chef: Review of relevant work and future challenges

Sochacki,

Zhang,

Abdulali

et al. 2024

Journal of Field Robotics

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Robotic chefs are a promising technology that can improve the availability of quality food by reducing the time required for cooking, therefore decreasing food's overall cost. This paper clarifies and structures design and benchmarking rules in this new area of research, and provides a comprehensive review of technologies suitable for the construction of cooking robots. The diner is an ultimate judge of the cooking outcome, therefore we put focus on explaining human food preferences and perception of taste and ways to use them for control. Mechanical design of robotic chefs at a practically low cost remains the challenge, but some recently published gripper designs as well as whole robotic systems show the use of cheap materials or off‐the‐shelf components. Moreover, technologies like taste sensing, machine learning, and computer vision are making their way into robotic cooking enabling smart sensing and therefore improving controllability and autonomy. Furthermore, objective assessment of taste and food palatability is a challenge even for trained humans, therefore the paper provides a list of procedures for benchmarking the robot's tasting and cooking abilities. The paper is written from the point of view of a researcher or engineer building a practical robotic system, therefore there is a strong priority for solutions and technologies that are proven, robust and self‐contained enough to be a part of a larger system.

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“…The average measured reflectance of ripe fruit (16.78) and unripe fruit (21.70) falls into two distinct regions for accurate estimation of ripeness during the harvest. In another report, Iida’s group demonstrated 132 soft grippers mounted with electrical impedance-tomography-based sensors to estimate various characteristics of the fruit, such as weight, ripeness, and acidity (pH). The sensor works on the principle of bioimpedance, i.e., when an alternating current applied across organic matter biological tissue impedes the flow of current.…”

Section: Sensing Technologies For Soft Roboticsmentioning

confidence: 99%

Sensing in Soft Robotics

Hegde,

Su,

Tan

et al. 2023

ACS Nano

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Soft robotics is an exciting field of science and technology that enables robots to manipulate objects with human-like dexterity. Soft robots can handle delicate objects with care, access remote areas, and offer realistic feedback on their handling performance. However, increased dexterity and mechanical compliance of soft robots come with the need for accurate control of the position and shape of these robots. Therefore, soft robots must be equipped with sensors for better perception of their surroundings, location, force, temperature, shape, and other stimuli for effective usage. This review highlights recent progress in sensing feedback technologies for soft robotic applications. It begins with an introduction to actuation technologies and material selection in soft robotics, followed by an in-depth exploration of various types of sensors, their integration methods, and the benefits of multimodal sensing, signal processing, and control strategies. A short description of current market leaders in soft robotics is also included in the review to illustrate the growing demands of this technology. By examining the latest advancements in sensing feedback technologies for soft robots, this review aims to highlight the potential of soft robotics and inspire innovation in the field.

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Automated Fruit Quality Testing using an Electrical Impedance Tomography-Enabled Soft Robotic Gripper

Cited by 8 publications

References 34 publications

Soft robotics for farm to fork: applications in agriculture & farming

Soft robotics for farm to fork: applications in agriculture & farming

Towards practical robotic chef: Review of relevant work and future challenges

Sensing in Soft Robotics

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