Humanitarian Crisis scenarios typically require immediate rescue intervention. In many cases, the conditions at a scene may be prohibitive for human rescuers to provide instant aid, because of hazardous, unexpected, and human threatening situations. These scenarios are ideal for autonomous mobile robot systems to assist in searching and even rescuing individuals. In this study, we present a synchronous ground-aerial robot collaboration approach, under which an Unmanned Aerial Vehicle (UAV) and a humanoid robot solve a Search and Rescue scenario locally, without the aid of a commonly used Global Navigation Satellite System (GNSS). Specifically, the UAV uses a combination of Simultaneous Localization and Mapping and OctoMap approaches to extract a 2.5D occupancy grid map of the unknown area in relation to the humanoid robot. The humanoid robot receives a goal position in the created map and executes a path planning algorithm in order to estimate the FootStep navigation trajectory for reaching the goal. As the humanoid robot navigates, it localizes itself in the map while using an adaptive Monte-Carlo Localization algorithm by combining local odometry data with sensor observations from the UAV. Finally, the humanoid robot performs visual human body detection while using camera data through a Darknet pre-trained neural network. The proposed robot collaboration scheme has been tested under a proof of concept setting in an exterior GNSS-denied environment.

Nowadays, various unmanned aerial vehicle (UAV) applications become increasingly demanding since they require real-time, autonomous and intelligent functions. Towards this end, in the present study, a fully autonomous UAV scenario is implemented, including the tasks of area scanning, target recognition, geo-location, monitoring, following and finally landing on a high speed moving platform. The underlying methodology includes AprilTag target identification through Graphics Processing Unit (GPU) parallelized processing, image processing and several optimized locations and approach algorithms employing gimbal movement, Global Navigation Satellite System (GNSS) readings and UAV navigation. For the experimentation, a commercial and a custom made quad-copter prototype were used, portraying a high and a low-computational embedded platform alternative. Among the successful targeting and follow procedures, it is shown that the landing approach can be successfully performed even under high platform speeds.

The systematic environmental monitoring of the land, atmosphere, oceans and their coupling zones, is assisted by the use of unmanned aerial vehicles (UAVs) that can operate over rural and/or urban areas to provide enhanced spatial and temporal measurement resolutions compared against corresponding satellite products. The international UAV market includes a vast number of solutions that carry sensors for environmental monitoring varying in type, flight time, carrying weight, communication, and autonomous flight. The majority of these commercial UAVs (especially the low-cost ones) have been designed for specific applications and their main disadvantage is that they can only integrate the payload they have been initially designed to carry, thus presenting minimal modularity. This work presents a modular and affordable platform where the user can easily adapt almost any type of environmental monitoring sensor, which can transmit its measurements to the UAV flight controller without the need for any additional modification. A novel communication protocol has been developed that is also capable to incorporate proximity sensors for collision avoidance. In addition, a wireless mobile telecommunications module incorporation through the use of mobile devices on the UAV provides real-time animated map generation along with cooperative capabilities for fleet missions.

Uncrewed aerial vehicles (UAVs) are continuously gaining popularity in a wide spectrum of applications, while their positioning and navigation most often relies on Global Navigation Satellite Systems (GNSS). However, numerous conditions and practices require UAV operation in GNSS-denied environments, including confined spaces, urban canyons, vegetated areas and indoor places. For the purposes of this study, an integrated UAV navigation system was designed and implemented which utilizes GNSS, visual, depth and inertial data to provide real-time localization. The implementation is built as a package for the Robotic Operation System (ROS) environment to allow ease of integration in various systems. The system can be autonomously adjusted to the flight environment, providing spatial awareness to the aircraft. This system expands the functionality of UAVs, as it enables navigation even in GNSS-denied environments. This integrated positional system provides the means to support fully autonomous navigation under mixed environments, or malfunctioning conditions. Experiments show the capability of the system to provide adequate results in open, confined and mixed spaces.

Phytoliths constitute microscopic SiO₂-rich biominerals formed in the cellular system of many living plants and are often preserved in soils, sediments and artefacts. Their analysis contributes significantly to the identification and study of botanical remains in (paleo)ecological and archaeological contexts. Traditional identification and classification of phytoliths rely on human experience, and as such, an emerging challenge is to automatically classify them to enhance data homogeneity among researchers worldwide and facilitate reliable comparisons. In the present study, a deep artificial neural network (NN) is implemented under the objective to detect and classify phytoliths, extracted from modern wheat (Triticum spp.). The proposed methodology is able to recognise four phytolith morphotypes: (a) Stoma, (b) Rondel, (c) Papillate, and (d) Elongate dendritic. For the learning process, a dataset of phytolith photomicrographs was created and allocated to training, validation and testing data groups. Due to the limited size and low diversity of the dataset, an end-to-end encoder-decoder NN architecture is proposed, based on a pre-trained MobileNetV2, utilised for the encoder part and U-net, used for the segmentation stage. After the parameterisation, training and fine-tuning of the proposed architecture, it is capable to classify and localise the four classes of phytoliths in unknown images with high unbiased accuracy, exceeding 90%. The proposed methodology and corresponding dataset are quite promising for building up the capacity of phytolith classification within unfamiliar (geo)archaeological datasets, demonstrating remarkable potential towards automatic phytolith analysis.

Water vapor is one of the essential variables in monitoring the Earth’s climate. The Ocean and Land Color Instrument (OLCI) on-board the Copernicus Sentinel-3 missions measures the Integrated Water Vapor (IWV) column over land and ocean surfaces. Post-launch calibration and validation of satellite measurements constitutes a key process in the operational phase of Earth observation satellites. This work presents the external and independent validation of OLCI-A IWV product using the regional network of continuously operating Global Navigation Satellite System (GNSS) comprised 10 stations distributed over the island of Crete in the eastern Mediterranean. The Sentinel-3A/-3B OLCI imagery that captures in a single scene the entire area of Crete has been examined. For each OLCI image, the IWV value of cloud-free pixels containing the GNSS stations have been derived and compared against simultaneous GNSS-derived measurements. The absolute as well as the relative bias between OLCI-A and OLCI-B IWV measurements have been determined. There is a good agreement between OLCI and GNSS with a bias of −0.57 mm ± 2.90 mm for OLCI(A) and +2.42 ± 3.41 mm for OLCI(B). The results of this regional validation activity are compared against other studies and the regular validation carried out at the Sentinel-3 Mission Performance Center. This work concludes that the accuracy of the OLCI IWV products is within its design requirements. The potential synergy between Sentinel-2 and Sentinel-3 IWV products is also discussed.