IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i23p6751-d291841.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/23/6751/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/23/6751/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Daniela Rus & Michael T. Tolley, 2015. "Design, fabrication and control of soft robots," Nature, Nature, vol. 521(7553), pages 467-475, May.
    2. 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.
    3. 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.
    4. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    2. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    3. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    4. Neha Soni & Enakshi Khular Sharma & Narotam Singh & Amita Kapoor, 2019. "Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models," Papers 1905.02092, arXiv.org.
    5. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    6. Taejong Joo & Hyunyoung Jun & Dongmin Shin, 2022. "Task Allocation in Human–Machine Manufacturing Systems Using Deep Reinforcement Learning," Sustainability, MDPI, vol. 14(4), pages 1-18, February.
    7. Mahmoud Mahfouz & Tucker Balch & Manuela Veloso & Danilo Mandic, 2021. "Learning to Classify and Imitate Trading Agents in Continuous Double Auction Markets," Papers 2110.01325, arXiv.org, revised Oct 2021.
    8. Oleh Lukianykhin & Tetiana Bogodorova, 2021. "Voltage Control-Based Ancillary Service Using Deep Reinforcement Learning," Energies, MDPI, vol. 14(8), pages 1-22, April.
    9. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    10. Chen, Jiaxin & Shu, Hong & Tang, Xiaolin & Liu, Teng & Wang, Weida, 2022. "Deep reinforcement learning-based multi-objective control of hybrid power system combined with road recognition under time-varying environment," Energy, Elsevier, vol. 239(PC).
    11. Amirhosein Mosavi & Yaser Faghan & Pedram Ghamisi & Puhong Duan & Sina Faizollahzadeh Ardabili & Ely Salwana & Shahab S. Band, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Mathematics, MDPI, vol. 8(10), pages 1-42, September.
    12. Zhang, Yihao & Chai, Zhaojie & Lykotrafitis, George, 2021. "Deep reinforcement learning with a particle dynamics environment applied to emergency evacuation of a room with obstacles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 571(C).
    13. Alessio Brini & Daniele Tantari, 2021. "Deep Reinforcement Trading with Predictable Returns," Papers 2104.14683, arXiv.org, revised May 2023.
    14. Georgios D. Kontes & Georgios I. Giannakis & Víctor Sánchez & Pablo De Agustin-Camacho & Ander Romero-Amorrortu & Natalia Panagiotidou & Dimitrios V. Rovas & Simone Steiger & Christopher Mutschler & G, 2018. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings," Energies, MDPI, vol. 11(12), pages 1-23, December.
    15. Weifan Long & Taixian Hou & Xiaoyi Wei & Shichao Yan & Peng Zhai & Lihua Zhang, 2023. "A Survey on Population-Based Deep Reinforcement Learning," Mathematics, MDPI, vol. 11(10), pages 1-17, May.
    16. Chanjuan Liu & Jinmiao Cong & Tianhao Zhao & Enqiang Zhu, 2023. "Improving Agent Decision Payoffs via a New Framework of Opponent Modeling," Mathematics, MDPI, vol. 11(14), pages 1-15, July.
    17. Yifeng Guo & Xingyu Fu & Yuyan Shi & Mingwen Liu, 2018. "Robust Log-Optimal Strategy with Reinforcement Learning," Papers 1805.00205, arXiv.org.
    18. Hamed Khalili, 2024. "Deep Learning Pricing of Processing Firms in Agricultural Markets," Agriculture, MDPI, vol. 14(5), pages 1-14, April.
    19. Guan, Xiaoshu & Xiang, Zhengliang & Bao, Yuequan & Li, Hui, 2022. "Structural dominant failure modes searching method based on deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    20. Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6751-:d:291841. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.