Biomechanical Effects on Lower Extremities in Human-Robot Collaborative Agricultural Tasks

Self Cite

In the pursuit of optimizing the efficiency, flexibility, and adaptability of agricultural practices, human–robot interaction (HRI) has emerged in agriculture. Enabled by the ongoing advancement in information and communication technologies, this approach aspires to overcome the challenges originating from the inherent complex agricultural environments. Τhis paper systematically reviews the scholarly literature to capture the current progress and trends in this promising field as well as identify future research directions. It can be inferred that there is a growing interest in this field, which relies on combining perspectives from several disciplines to obtain a holistic understanding. The subject of the selected papers is mainly synergistic target detection, while simulation was the main methodology. Furthermore, melons, grapes, and strawberries were the crops with the highest interest for HRI applications. Finally, collaboration and cooperation were the most preferred interaction modes, with various levels of automation being examined. On all occasions, the synergy of humans and robots demonstrated the best results in terms of system performance, physical workload of workers, and time needed to execute the performed tasks. However, despite the associated progress, there is still a long way to go towards establishing viable, functional, and safe human–robot interactive systems.

Section: Resultsmentioning

confidence: 99%

Human–Robot Interaction in Agriculture: A Systematic Review

Benos

Moysiadis

Kateris

et al. 2023

Self Cite

“…Future developments include the automatization of the analysis procedure to provide the results in real time as the system navigates in the orchard. This will enhance situation awareness for safe and undisturbed navigation of the robotic platform in complex environments for the sake of avoiding possible injuries or damages [53]. In a broader perspective, further research is required towards improving the speed and accuracy of the existing cameras and image processing systems as well as decreasing the overall complexity [7,[54][55][56].…”

Section: Discussionmentioning

confidence: 99%

Proposing UGV and UAV Systems for 3D Mapping of Orchard Environments

Tagarakis

Filippou

Kalaitzidis

et al. 2022

Self Cite

During the last decades, consumer-grade RGB-D (red green blue-depth) cameras have gained popularity for several applications in agricultural environments. Interestingly, these cameras are used for spatial mapping that can serve for robot localization and navigation. Mapping the environment for targeted robotic applications in agricultural fields is a particularly challenging task, owing to the high spatial and temporal variability, the possible unfavorable light conditions, and the unpredictable nature of these environments. The aim of the present study was to investigate the use of RGB-D cameras and unmanned ground vehicle (UGV) for autonomously mapping the environment of commercial orchards as well as providing information about the tree height and canopy volume. The results from the ground-based mapping system were compared with the three-dimensional (3D) orthomosaics acquired by an unmanned aerial vehicle (UAV). Overall, both sensing methods led to similar height measurements, while the tree volume was more accurately calculated by RGB-D cameras, as the 3D point cloud captured by the ground system was far more detailed. Finally, fusion of the two datasets provided the most precise representation of the trees.

“…While there have been studies on the biomechanical effects of HRC using wearable sensors in other fields, similar studies conducted in the agriculture field are quite rare, especially those involving advanced and intelligent collaborative robots (cobots). One study by Benos et al investigated the appropriate height configuration for a harvesting robot [ 24 ]. Their study focused on optimizing the loads on the lower back of agricultural workers as they lifted produce baskets.…”

Section: Related Workmentioning

confidence: 99%

Wearable Sensors Assess the Effects of Human–Robot Collaboration in Simulated Pollination

Yerebakan,

2024

Pollination for indoor agriculture is hampered by environmental conditions, requiring farmers to pollinate manually. This increases the musculoskeletal illness risk of workers. A potential solution involves Human–Robot Collaboration (HRC) using wearable sensor-based human motion tracking. However, the physical and biomechanical aspects of human interaction with an advanced and intelligent collaborative robot (cobot) during pollination remain unknown. This study explores the impact of HRC on upper body joint angles during pollination tasks and plant height. HRC generally resulted in a significant reduction in joint angles with flexion decreasing by an average of 32.6 degrees (p ≤ 0.001) for both shoulders and 30.5 degrees (p ≤ 0.001) for the elbows. In addition, shoulder rotation decreased by an average of 19.1 (p ≤ 0.001) degrees. However, HRC increased the left elbow supination by 28.3 degrees (p ≤ 0.001). The positive effects of HRC were reversed when the robot was unreliable (i.e., missed its target), but this effect was not applicable for the left elbow. The effect of plant height was limited with higher plant height increasing right shoulder rotation but decreasing right elbow pronation. These findings aim to shed light on both the benefits and challenges of HRC in agriculture, providing valuable insights before deploying cobots in indoor agricultural settings.