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Autonomous Vehicles Management in Agriculture with Bluetooth Low Energy (BLE) and Passive Radio Frequency Identification (RFID) for Obstacle Avoidance

Author

Listed:
  • Danilo Monarca

    (Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

  • Pierluigi Rossi

    (Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

  • Riccardo Alemanno

    (Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

  • Filippo Cossio

    (Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

  • Paolo Nepa

    (Department of Information Engineering, University of Pisa, 56122 Pisa, Italy)

  • Andrea Motroni

    (Department of Information Engineering, University of Pisa, 56122 Pisa, Italy)

  • Roberto Gabbrielli

    (Department of Civil and Industrial Engineering, University of Pisa, 56122 Pisa, Italy)

  • Marco Pirozzi

    (National Institute for Insurance against Accidents at Work (INAIL), 4th Laboratory Division—Process and Production Plant Safety, 00143 Rome, Italy)

  • Carla Console

    (National Institute for Insurance against Accidents at Work (INAIL), 4th Laboratory Division—Process and Production Plant Safety, 00143 Rome, Italy)

  • Massimo Cecchini

    (Department of Agriculture and Forest Sciences (DAFNE), Tuscia University, 01100 Viterbo, Italy)

Abstract

Obstacle avoidance is a key aspect for any autonomous vehicles, and their usage in agriculture must overcome additional challenges such as handling interactions with agricultural workers and other tractors in order to avoid severe accidents. The simultaneous presence of autonomous vehicles and workers on foot definitely calls for safer designs, vehicle management systems and major developments in personal protective equipment (PPE). To cope with these present and future challenges, the “SMARTGRID” project described in this paper deploys an integrated wireless safety network infrastructure based on the integration of Bluetooth Low Energy (BLE) devices and passive radio frequency identification (RFID) tags designed to identify obstacles, workers, nearby vehicles and check if the right PPE is in use. With the aim of detecting workers at risk by scanning for passive RFID-integrated into PPE in danger areas, transmitting alerts to workers who wear them, tracking of near-misses and activating emergency stops, a deep analysis of the safety requirements of the obstacle detection system is shown in this study. Test programs have also been carried out on an experimental farm with detection ranging from 8 to 12 meters, proving that the system might represent a good solution for collision avoidance between autonomous vehicles and workers on foot.

Suggested Citation

  • Danilo Monarca & Pierluigi Rossi & Riccardo Alemanno & Filippo Cossio & Paolo Nepa & Andrea Motroni & Roberto Gabbrielli & Marco Pirozzi & Carla Console & Massimo Cecchini, 2022. "Autonomous Vehicles Management in Agriculture with Bluetooth Low Energy (BLE) and Passive Radio Frequency Identification (RFID) for Obstacle Avoidance," Sustainability, MDPI, vol. 14(15), pages 1-13, August.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9393-:d:877455
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    References listed on IDEAS

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    1. Jessie Adams & Alison Kennedy & Jacqueline Cotton & Susan Brumby, 2021. "Child Farm-Related Injury in Australia: A Review of the Literature," IJERPH, MDPI, vol. 18(11), pages 1-16, June.
    2. Florence Becot & Casper Bendixsen & Kathrine Barnes & Josie Rudolphi, 2021. "Broadening Our Understanding of Farm Children’s Risk Exposure by Considering Their Parents’ Farming Background," IJERPH, MDPI, vol. 18(10), pages 1-18, May.
    3. Andrea Colantoni & Danilo Monarca & Vincenzo Laurendi & Mauro Villarini & Filippo Gambella & Massimo Cecchini, 2018. "Smart Machines, Remote Sensing, Precision Farming, Processes, Mechatronic, Materials and Policies for Safety and Health Aspects," Agriculture, MDPI, vol. 8(4), pages 1-11, March.
    4. Mario Fargnoli & Mara Lombardi & Nicolas Haber & Daniele Puri, 2018. "The Impact of Human Error in the Use of Agricultural Tractors: A Case Study Research in Vineyard Cultivation in Italy," Agriculture, MDPI, vol. 8(6), pages 1-21, June.
    5. Byung-Wan Jo & Yun-Sung Lee & Jung-Hoon Kim & Do-Keun Kim & Pyung-Ho Choi, 2017. "Proximity Warning and Excavator Control System for Prevention of Collision Accidents," Sustainability, MDPI, vol. 9(8), pages 1-20, August.
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