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Soft Robotics as an Enabling Technology for Agroforestry Practice and Research

Author

Listed:
  • Girish Chowdhary

    (Department of Agricultural & Biological Engineering, University of Illinois at Urbana-Champaign (UIUC), Champaign, IL 61801, USA)

  • Mattia Gazzola

    (Department of Mechanical Science & Engineering, UIUC, Champaign, IL 61801, USA)

  • Girish Krishnan

    (Department of Industrial and Enterprise Systems Engineering, UIUC, Champaign, IL 61801, USA)

  • Chinmay Soman

    (EarthSense, Inc Champaign, Champaign IL 61820, USA)

  • Sarah Lovell

    (School of Natural Resources, University of Missouri, Columbia, MO 65211, USA)

Abstract

The shortage of qualified human labor is a key challenge facing farmers, limiting profit margins and preventing the adoption of sustainable and diversified agroecosystems, such as agroforestry. New technologies in robotics could offer a solution to such limitations. Advances in soft arms and manipulators can enable agricultural robots that can have better reach and dexterity around plants than traditional robots equipped with hard industrial robotic arms. Soft robotic arms and manipulators can be far less expensive to manufacture and significantly lighter than their hard counterparts. Furthermore, they can be simpler to design and manufacture since they rely on fluidic pressurization as the primary mechanisms of operation. However, current soft robotic arms are difficult to design and control, slow to actuate, and have limited payloads. In this paper, we discuss the benefits and challenges of soft robotics technology and what it could mean for sustainable agriculture and agroforestry.

Suggested Citation

  • Girish Chowdhary & Mattia Gazzola & Girish Krishnan & Chinmay Soman & Sarah Lovell, 2019. "Soft Robotics as an Enabling Technology for Agroforestry Practice and Research," Sustainability, MDPI, vol. 11(23), pages 1-21, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6751-:d:291841
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    References listed on IDEAS

    as
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    2. Xiaotian Zhang & Fan Kiat Chan & Tejaswin Parthasarathy & Mattia Gazzola, 2019. "Modeling and simulation of complex dynamic musculoskeletal architectures," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
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    4. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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