Human–Robot Interaction through Dynamic Movement Recognition for Agricultural Environments

Author:

Moysiadis Vasileios123ORCID,Benos Lefteris1ORCID,Karras George2ORCID,Kateris Dimitrios1ORCID,Peruzzi Andrea4ORCID,Berruto Remigio5ORCID,Papageorgiou Elpiniki6ORCID,Bochtis Dionysis13ORCID

Affiliation:

1. Institute for Bio-Economy and Agri-Technology (IBO), Centre of Research and Technology-Hellas (CERTH), 6th km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece

2. Department of Informatics and Telecommunications, University of Thessaly, 35100 Lamia, Greece

3. farmB Digital Agriculture S.A., 17th November 79, 55534 Thessaloniki, Greece

4. Department of Agronomy and Agroecosystem Management, University of Pisa, Via S. Michele degli Scalzi 2, 56124 Pisa, Italy

5. Interuniversity Department of Regional and Urban Studies and Planning, University of Torino, Viale Matttioli 39, 10125 Torino, Italy

6. Department of Energy Systems, University of Thessaly, Gaiopolis Campus, 41500 Larisa, Greece

Abstract

In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting the detection of a range of dynamic human movements into specific robot actions. Various machine learning models were evaluated to classify these movements, with Long Short-Term Memory (LSTM) demonstrating the highest performance. Furthermore, the Robot Operating System (ROS) software (Melodic Version) capabilities were employed to interpret the movements into certain actions to be performed by the unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based human activity recognition was successfully tested through three scenarios taking place in an orchard, including (a) a UGV following the authorized participant; (b) GPS-based navigation to a specified site of the orchard; and (c) a combined harvesting scenario with the UGV following participants and aid by transporting crates from the harvest site to designated sites. The main challenge was the precise detection of the dynamic hand gesture “come” alongside navigating through intricate environments with complexities in background surroundings and obstacle avoidance. Overall, this study lays a foundation for future advancements in human–robot collaboration in agriculture, offering insights into how integrating dynamic human movements can enhance natural communication, trust, and safety.

Publisher

MDPI AG

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