The number of tasks that nowadays are accomplished by using unmanned aerial vehicles is rising across many civil applications, including agriculture. Thus, this work aims at providing the reader with an overview of the agronomical use of unmanned aerial vehicles. The work starts with a historical analysis of the use of aircrafts in agriculture, as pioneers of their use in modern precision agriculture techniques, currently applied by a high number of users. This survey has been carried out by providing a classification of the vehicles according to their typology and main sensorial and performance features. An extensive review of the most common applications and the advantages of using unmanned aerial vehicles is the core of the work. Finally, a brief summary of the key points of the legislation applicable to civil drones that could affect to agricultural applications is analyzed.

Technological breakthroughs in recent years have led to a revolution in fields such as Machine Vision and Search and Rescue Robotics (SAR), thanks to the application and development of new and improved neural networks to vision models together with modern optical sensors that incorporate thermal cameras, capable of capturing data in post-disaster environments (PDE) with rustic conditions (low luminosity, suspended particles, obstructive materials). Due to the high risk posed by PDE because of the potential collapse of structures, electrical hazards, gas leakage, etc., primary intervention tasks such as victim identification are carried out by robotic teams, provided with specific sensors such as thermal, RGB cameras, and laser. The application of Convolutional Neural Networks (CNN) to computer vision is a breakthrough for detection algorithms. Conventional methods for victim identification in these environments use RGB image processing or trained dogs, but detection with RGB images is inefficient in the absence of light or presence of debris; on the other hand, developments with thermal images are limited to the field of surveillance. This paper’s main contribution focuses on implementing a novel automatic method based on thermal image processing and CNN for victim identification in PDE, using a Robotic System that uses a quadruped robot for data capture and transmission to the central station. The robot’s automatic data processing and control have been carried out through Robot Operating System (ROS). Several tests have been carried out in different environments to validate the proposed method, recreating PDE with varying conditions of light, from which the datasets have been generated for the training of three neural network models (Fast R-CNN, SSD, and YOLO). The method’s efficiency has been tested against another method based on CNN and RGB images for the same task showing greater effectiveness in PDE main results show that the proposed method has an efficiency greater than 90%.

Recent developments in bioinspired technologies combined with the advance of intelligent and soft materials have allowed soft robots to replicate the behavior of different animal species. These devices can perform complicated tasks such as reaching or adapting in constrained and unstructured environments. This article proposes a methodology to develop a soft robot called “JellyRobcib” inspired in morphology and behavior by jellyfish, using shape-memory alloy springs as actuators (as bio-muscles). Such actuators can move the jellyfish both vertically and laterally by applying closed-loop fuzzy and visual controls. Additionally, Computer-Assisted Designs and Computational Fluid Dynamics simulations have been carried out to validate the soft robot model. The results show that the robot movements are very close to the morphological behavior of a real jellyfish regarding the curves of displacements, speeds and accelerations, after performing several experiments for autonomous movement: vertical ascent, lateral movements and trajectory tracking, obtaining an accuracy of ±1479 cm and repeatability of 0.944 for lateral movements for fuzzy visual control. Furthermore, thermal measurements were taken throughout a given path, allowing the generation of temperature gradients within the underwater environment for monitoring purposes.

In recent years, exploration tasks in disaster environments, victim localization and primary assistance have been the main focuses of Search and Rescue (SAR) Robotics. Developing new technologies in Mixed Reality (M-R) and legged robotics has taken a big step in developing robust field applications in the Robotics field. This article presents MR-RAS (Mixed-Reality for Robotic Assistance), which aims to assist rescuers and protect their integrity when exploring post-disaster areas (against collapse, electrical, and toxic risks) by facilitating the robot’s gesture guidance and allowing them to manage interest visual information of the environment. Thus, ARTU-R (A1 Rescue Tasks UPM Robot) quadruped robot has been equipped with a sensory system (lidar, thermal, and RGB-D cameras) to validate this proof of concept. On the other hand, Human-Robot interaction is executed by using the Hololens glasses. This work’s main contribution is the implementation and evaluation of a Mixed-Reality system based on a ROS-Unity solution, capable of managing at a high level the guidance of a complex legged robot through different interest zones (defined by a Neural Network and a vision system) of a post-disaster environment (PDE). The robot’s main tasks at each point visited involve detecting victims through thermal, RGB imaging, and neural networks and assisting victims with medical equipment. Tests have been carried out in scenarios that recreate the conditions of PDE (debris, simulation of victims, etc.). An average efficiency improvement of 48% has been obtained when using the immersive interface and a time optimization of 21.4% compared to conventional interfaces. The proposed method has proven to improve rescuers’ immersive experience of controlling a complex robotic system.

In recent years, legged (quadruped) robots have been subject of technological study and continuous development. These robots have a leading role in applications that require high mobility skills in complex terrain, as is the case of Search and Rescue (SAR). These robots stand out for their ability to adapt to different terrains, overcome obstacles and move within unstructured environments. Most of the implementations recently developed are focused on data collecting with sensors, such as lidar or cameras. This work seeks to integrate a 6DoF arm manipulator to the quadruped robot ARTU-R (A1 Rescue Tasks UPM Robot) by Unitree to perform manipulation tasks in SAR environments. The main contribution of this work is focused on the High-level control of the robotic set (Legged + Manipulator) using Mixed-Reality (MR). An optimization phase of the robotic set workspace has been previously developed in Matlab for the implementation, as well as a simulation phase in Gazebo to verify the dynamic functionality of the set in reconstructed environments. The first and second generation of Hololens glasses have been used and contrasted with a conventional interface to develop the MR control part of the proposed method. Manipulations of first aid equipment have been carried out to evaluate the proposed method. The main results show that the proposed method allows better control of the robotic set than conventional interfaces, improving the operator efficiency in performing robotic handling tasks and increasing confidence in decision-making. On the other hand, Hololens 2 showed a better user experience concerning graphics and latency time.

Technological advancements have facilitated the development of sophisticated vision systems, integrating optical sensors with artificial vision and machine learning techniques to create applications in different fields of robotics. One such field is Search and Rescue (SAR) robotics, which has historically played a significant role in assisting brigades following post-disaster events, particularly in exploration phases and, crucially, in victim identification. The importance of employing these systems in victim identification lies in their functionality under challenging conditions, enabling the capture of information across different light spectrum ranges (RGB, Thermal, Multispectral). This article proposes an innovative comparative analysis that scrutinizes the advantages and limitations of three sensor types in victim detection. It explores contemporary developments in the state-of-the-art and proposes new metrics addressing critical aspects, such as functionality in specific scenarios and the analysis of environmental disturbances. For the indoor and outdoor testing phase, a quadrupedal robot has been equipped with these cameras. The primary findings highlight the individual contributions of each sensor, particularly emphasizing the efficacy of the infrared spectrum for the thermal camera and the Near Infrared and Red Edge bands for the multispectral camera. Ultimately, following system evaluations, detection precisions exceeding 92% and 86%, respectively, were achieved.