Design of a UGV Powered by Solar Energy for Precision Agriculture

Abstract

In this paper, a novel UGV (unmanned ground vehicle) for precision agriculture, named “Agri.q,” is presented. The Agri.q has a multiple degrees of freedom positioning mechanism and it is equipped with a robotic arm and vision sensors, which allow to challenge irregular terrains and to perform precision field operations with perception. In particular, the integration of a 7 DOFs (degrees of freedom) manipulator and a mobile frame results in a reconfigurable workspace, which opens to samples collection and inspection in non-structured environments. Moreover, Agri.q mounts an orientable landing platform for drones which is made of solar panels, enabling multi-robot strategies and solar power storage, with a view to sustainable energy. In fact, the device will assume a central role in a more complex automated system for agriculture, that includes the use of UAV (unmanned aerial vehicle) and UGV for coordinated field monitoring and servicing. The electronics of the device is also discussed, since Agri.q should be ready to send-receive data to move autonomously or to be remotely controlled by means of dedicated processing units and transmitter-receiver modules. This paper collects all these elements and shows the advances of the previous works, describing the design process of the mechatronic system and showing the realization phase, whose outcome is the physical prototype.

Robotic Technologies for High-Throughput Plant Phenotyping: Contemporary Reviews and Future Perspectives

“…Based on the advantages of solar panels and hydrogen fuel cell, both technologies can be used as renewable power sources for different components of the phenotyping robots (Underwood et al, 2017;Quaglia et al, 2020). However, there is not a wide range of application of these technologies for the phenotyping robots.…”

Estimates of Maize Plant Density from UAV RGB Images Using Faster-RCNN Detection Model: Impact of the Spatial Resolution

“…This explains why the model was not able to detect the plants, even when it is trained with the synthetic low-resolution images (T h⟶l gm ). Contrary to vectors that operate at an almost constant height like ground vehicles [16,[64][65][66] or fixed cameras [67][68][69][70], camera settings (aperture, focus and integration time) in UAVs need to be adapted to the flight conditions, especially flight altitude, to maximize image quality. Further, the jpg recording format of the images may also significantly impact image quality.…”

Unimodal and Multimodal Perception for Forest Management: Review and Dataset

“…Another application of perception systems in precision agriculture is characterising, monitoring, and phenotyping vegetative cultures using stereo vision [124,125], point clouds [126,127], satellite imagery [128], low-altitude aerial images [129], or multispectral imagery [130]. Along with these advances in terms of perception, several robotic platforms have appeared: for harvesting [101,102,[104][105][106], for precise spraying [131], for plant counting [132], and for general agricultural tasks [133][134][135][136]. A topic that is being increasingly studied in the agricultural sector is localisation and consequent autonomous navigation in crops.…”