Data Collection Missions Research Articles

Plant Species Classification and Biodiversity Estimation from UAV Images with Deep Learning

Biodiversity is a characteristic of ecosystems that plays a crucial role in the study of their evolution, and to estimate it, the species of all plants need to be determined. In this study, we used Unmanned Aerial Vehicles to gather RGB images of mid-to-high-altitude ecosystems in the Zao mountains (Japan). All the data-collection missions took place in autumn so the plants present distinctive seasonal coloration. Patches from single trees and bushes were manually extracted from the collected orthomosaics. Subsequently, Deep Learning image-classification networks were used to automatically determine the species of each tree or bush and estimate biodiversity. Both Convolutional Neural Networks (CNNs) and Transformer-based models were considered (ResNet, RegNet, ConvNeXt, and SwinTransformer). To measure and estimate biodiversity, we relied on the Gini–Simpson Index, the Shannon–Wiener Index, and Species Richness. We present two separate scenarios for evaluating the readiness of the technology for practical use: the first scenario uses a subset of the data with five species and a testing set that has a very similar percentage of each species to those present in the training set. The models studied reach very high performances with over 99 Accuracy and 98 F1 Score (the harmonic mean of Precision and Recall) for image classification and biodiversity estimates under 1% error. The second scenario uses the full dataset with nine species and large variations in class balance between the training and testing datasets, which is often the case in practical use situations. The results in this case remained fairly high for Accuracy at 90.64% but dropped to 51.77% for F1 Score. The relatively low F1 Score value is partly due to a small number of misclassifications having a disproportionate impact in the final measure, but still, the large difference between the Accuracy and F1 Score highlights the complexity of finely evaluating the classification results of Deep Learning Networks. Even in this very challenging scenario, the biodiversity estimation remained with relatively small (6–14%) errors for the most detailed indices, showcasing the readiness of the technology for practical use.

Collaborative relay for achieving long-term and low-AoI data collection in UAV-aided IoT systems

In Internet of Things (IoT) systems, sensor nodes are frequently placed in remote and unattended locations to monitor environmental data. One significant challenge is ensuring the timely and efficient transmission of data generated by these sensor nodes back to the base station. The use of unmanned aerial vehicles (UAVs) can provide a practical solution to this challenge by acting as mobile relay nodes for facilitating data transmission. In most existing works, UAVs are typically restricted to collecting data within their designated areas and returning to the base station for data offloading, resulting in suboptimal data timeliness due to long-distance flights. A limited number of works have explored the utilization of relay collaboration by UAVs for data collection, enabling efficient and immediate transmission of sensor node data to the base station. Nevertheless, UAVs positioned at significant distances from the base station face challenges in obtaining timely energy replenishment. This makes them unable to effectively support long-duration data collection missions. In order to tackle these challenges, we develop a UAV-aided IoT collaborative data collection mechanism and propose a matching games-based data collection (MGDC) scheme. In this scheme, we begin by identifying convergence nodes within the ground sensor network, responsible for uploading sensor-generated data to passing UAVs. Furthermore, we divide the mission area into multiple subareas based on the number of available UAVs. Subsequently, using a matching game algorithm, we establish relay relationships between UAVs to enable efficient relay transmissions among paired UAVs. To achieve efficient data collection of UAVs, we employ an improved adaptive large neighborhood search (IALNS) algorithm for UAV flight path planning. Finally, we incorporate an alternating charging mode to ensure all UAVs have the opportunity to return to the base station for energy recharge. Through comprehensive experimentation, we confirm the significant enhancement provided by our proposed data collection scheme compared to existing schemes. This scheme effectively reduces system age of information (AoI) and extends the runtime of the system.

The Dynamic Nature of Wrack: An Investigation into Wrack Movement and Impacts on Coastal Marshes Using sUAS

This study investigates the use of small unoccupied aerial systems (sUAS) as a new remote sensing tool to identify and track the spatial distribution of wrack on coastal tidal marsh systems. We used sUAS to map the wrack movement in a Spartina alterniflora-dominated salt marsh monthly for one year including before and after Hurricane Isaias that brought strong winds, rain, and storm surge to the area of interest in August 2020. Flight parameters for each data collection mission were held constant including collection only during low tide. Wrack was visually identified and digitized in a GIS using every mission orthomosaic created from the mission images. The digitized polygons were visualized using a raster data model and a combination of all of the digitized wrack polygons. Results indicate that wrack mats deposited before and as a result of a hurricane event remained for approximately three months. Furthermore, 55% of all wrack detritus was closer than 10 m to river or stream water bodies, 64% were within 15 m, and 71% were within 20 m, indicating the spatial dependence of wrack location in a marsh system on water and water movement. However, following the passing of Isaias, the percentage of wrack closer than 10 m to a river or creek decreased to a low of 44%, which was not seen again during the year-long study. This study highlights the on-demand image collection of a sUAS for providing new insights into how quickly wrack distribution and vegetation can change over a short time.

Perception-Aware Tag Placement Planning for Robust Localization of UAVs in Indoor Construction Environments

Tag-based visual-inertial localization is a lightweight method for enabling autonomous data collection missions of low-cost unmanned aerial vehicles (UAVs) in indoor construction environments. However, finding the optimal tag configuration (i.e., number, size, and location) on dynamic construction sites remains challenging. This work proposes a perception-aware genetic algorithm-based tag placement planner (PGA-TaPP) to determine the optimal tag configuration using four-dimensional (4D) building information models (BIM), considering the project progress, safety requirements, and UAV’s localizability. The proposed method provides a 4D plan for tag placement by maximizing the localizability in user-specified regions of interest (ROIs) while limiting the installation costs. Localizability is quantified using the Fisher information matrix (FIM) and encapsulated in navigable grids. The experimental results show the effectiveness of our method in finding an optimal 4D tag placement plan for the robust localization of UAVs on under-construction indoor sites.

UAV Trajectory Optimization for Time-Constrained Data Collection in UAV-Enabled Environmental Monitoring Systems

This article studies the unmanned aerial vehicle (UAV) trajectory planning problem in a UAV-enabled environmental monitoring system and considers a typical data collection scenario where a UAV is dispatched to a geographical area to collect time-constrained data in a set of monitoring areas and transmit collected data to a ground base station (GBS). We formulate the UAV trajectory planning problem as an optimization problem with the objective to minimize the UAV’s mission completion time by jointly optimizing the UAV’s flying speeds, hovering positions, and visiting sequence, taking into account the Age of Information (AoI) of data in monitoring areas, and the on-board energy of the UAV. To solve the problem, we decompose the formulated optimization problem into two subproblems: a UAV speed optimization problem and a UAV path optimization problem, and propose successive convex approximation (SCA) method-based and generic algorithm (GA)-based algorithms to solve the subproblems. Based on the proposed algorithms, we further propose an AoI-and-energy-aware trajectory optimization (AoI-EaTO) algorithm to solve the main problem. Simulation results show that the proposed AoI-EaTO algorithm can find a better solution to the problem than two benchmark algorithms. Moreover, given the UAV’s on-board energy and maximum speed as well as the positions of the GBS and monitoring areas, the AoI limitation threshold that the system is able to satisfy can be obtained through simulation results. This threshold can be used to decide if the UAV is able to finish a particular data collection mission, which is useful to the deployment of the mission.

Completion Time Minimization for UAV-UGV-Enabled Data Collection.

In unmanned aerial vehicle (UAV)-enabled data collection systems, situations where sensor nodes (SNs) cannot upload their data successfully to the UAV may exist, due to factors such as SNs’ insufficient energy and the UAV’s minimum flight altitude. In this paper, an unmanned ground vehicle (UGV)-UAV-enabled data collection system is studied, where data collection missions are conducted by a UAV and a UGV cooperatively. Two cooperative strategies are proposed, i.e., collaboration without information interaction, and collaboration with information interaction. In the first strategy, the UGV collects data from remote SNs (i.e., the SNs that cannot upload data to the UAV) as well as some normal SNs (i.e., the SNs that can upload data to the UAV), while the UAV only collects data from some normal SNs. Then, they carry the data back to the data center (DC) without interacting with each other. In the second strategy, the UGV only collects data from remote SNs, while transmitting the collected data to the UAV at a data interaction point, then the data are carried back to the DC by the UAV. There are mobile data collection nodes on the ground and in the air, and the task is to find trajectories to minimize the data collection time in the data center. A collaborative strategy selection algorithm, combining a multi-stage-based SN association and UAV-UGV path optimization algorithm, is proposed to solve the problem effectively, where techniques including convex optimization and genetic algorithm are adopted. The simulation result shows that the proposed scheme reduces the mission completion time by 36% compared with the benchmark scheme.

Deep Reinforcement Learning Approach for Joint Trajectory Design in Multi-UAV IoT Networks

In this paper, we investigate an unmanned aerial vehicle (UAV) communication system, where the trajectories of multi-UAVs are designed for the data collection mission of IoT nodes. We aim at minimizing the mission time with constraints of UAV’s maximum speed and acceleration, the collision avoidance, and communication interference among UAVs. We propose a three-step approach to solve this problem, which is based on the K-means algorithm, and Deep Reinforcement Learning (DRL) with a distributed manner and a centralized manner. The mutual influences like collision avoidance and interference among UAVs are explicitly expressed in our algorithm. Numerical results show the advantage of our proposed approach.

Assessment of the Applicability of UAV for the Creation of Digital Surface Model of a Small Peatland

Rapid development and growing availability of Unmanned Aerial Vehicles (UAV) translates into their more wide-spread application in monitoring of the natural environment. Moreover, advances in computer analysis techniques allow the imaging performed with UAVs to be used in creating Digital Elevation Models (DEM) and Digital Surface Models (DSM). DEMs are often employed in studies on geology, environment, engineering, and architecture. The presented paper discusses the procedures enabling the making of a precise DEM, discusses the aerial imaging data processing technique as well as determines the accuracy of obtained products in comparison with an existing Digital Elevation Model. Based on available literature the author indicates four sets of input parameters applicable in UAV imaging. Data collection missions were performed on two separate days in the area of a small peatland located in the Tuchola Pinewood, Poland. The study aims to address two research issues. Firstly, the author investigates the possibility of creating a DSM based on UAV imaging performed under unfavorable conditions and indicates whether results obtained via this method display sufficient quality to be seen as an alternative to the traditional surveying techniques (LiDAR). Secondly, the article determines the input parameters for a photogrammetric flight that ensure the highest accuracy of a resulting DSM. The analyses show a strong positive correlation between the DSMs prepared based on UAV imaging with data obtained by means of traditional methods (LiDAR). Mean correlation coefficient ranged from 0.45 to 0.75 depending on the type of land use and input parameters selected for a given flight. Furthermore, the analysis revealed that DSMs prepared based on UAV imaging—provided the most suitable input parameters are selected—can be a viable alternative to standard measurements, with the added benefit of low cost and the capacity for repeatable data collection in time. Admittedly, the method in question cannot be utilized in relation to peatlands overgrown with high vegetation (trees, shrubs) as it effectively diminishes the accuracy of obtained DSMs.

Reinforcement Learning-Enabled UAV Itinerary Planning for Remote Sensing Applications in Smart Farming

UAV path planning for remote sensing aims to find the best-fitted routes to complete a data collection mission. UAVs plan the routes and move through them to remotely collect environmental data from particular target zones by using sensory devices such as cameras. Route planning may utilize machine learning techniques to autonomously find/select cost-effective and/or best-fitted routes and achieve optimized results including: minimized data collection delay, reduced UAV power consumption, decreased flight traversed distance and maximized number of collected data samples. This paper utilizes a reinforcement learning technique (location and energy-aware Q-learning) to plan UAV routes for remote sensing in smart farms. Through this, the UAV avoids heuristically or blindly moving throughout a farm, but this takes the benefits of environment exploration–exploitation to explore the farm and find the shortest and most cost-effective paths into target locations with interesting data samples to collect. According to the simulation results, utilizing the Q-learning technique increases data collection robustness and reduces UAV resource consumption (e.g., power), traversed paths, and remote sensing latency as compared to two well-known benchmarks, IEMF and TBID, especially if the target locations are dense and crowded in a farm.

Multi-UAV Enabled Data Collection with Efficient Joint Adaptive Interference Management and Trajectory Design

This paper studies interference in a data collection scenario in which multiple unmanned aerial vehicles (UAVs) are dispatched to wirelessly collect data from a set of distributed sensors. To improve the communication throughput and minimize the completion time, we design a joint resource allocation and trajectory optimization framework that not only is compatible with the traditional time-division scheme and interference coordination scheme but also combines their advantages. First, we analyse a basic quasi-stationary scenario with two UAVs and four devices, in which the two UAVs hover at optimal displacements to execute the data collection mission, and it is proven that the proposed optimal resource allocation and trajectory solution is adaptively adjustable according to the severity of the interference and that the common throughput of the network is non-decreasing. Second, for the general mobile case, we design an efficient algorithm to jointly address resource allocation and trajectory optimization, in which we first apply the block coordinate descent method to decompose the original non-convex problem into three non-convex sub-problems and then employ a dedicated genetic algorithm, a penalty function and the sequential convex approximation (SCA) technique to efficiently solve the individual sub-problems and obtain a satisfactory locally optimal solution with an adaptive initialization scheme. Subsequently, numerical experiments are presented to demonstrate that the completion time of the data collection task with our proposed method is at least 25% shorter than those with several baseline dynamic orthogonal schemes when 4 UAVs are deployed. Finally, we provide a practical application principle concerning the maximum suitable number of UAVs to avoid the inherent deficiencies of the proposed algorithm.

An efficient data collection framework in the sky: An affine transformation approach based on Internet of unmanned aerial vehicles

Due to a large amount of time-series data (e.g. precipitation, temperature, humidity) in the air, reliable and fast data collection is a challenging issue. Using an unmanned aerial vehicle (UAV) as a data collector to collect time-series data is an effective method. However, due to the limited storage capacity of the UAV, the UAV must access the sensor multiple times to collect time-series data from the deployed sensors and dump the data to the data centre, which leads to significantly increased time and cost for data collection mission. To address this challenge, an efficient time-series data collection framework is proposed based on the Internet of UAVs. In this system, a min-maximum data processing strategy is adopted based on data value to store the collected time-series data. Specifically, efficient data compression storage is achieved with minimal loss of precision by extracting the dominant dataset with maximum value. Furthermore, an efficient affine transformation method is proposed to improve the efficiency of the system. Extensive case studies on some real-world datasets demonstrate that the proposed framework can achieve efficient data management and compressed storage.

Deep Reinforcement Learning Based Energy Efficient Multi-UAV Data Collection for IoT Networks

Unmanned aerial vehicles (UAVs) are regarded as an emerging technology, which can be effectively utilized to perform the data collection tasks in the Internet of Things (IoT) networks. However, both the UAVs and the sensors in these networks are energy-limited devices, which necessitates an energy-efficient data collection procedure to ensure the network lifetime. In this paper, we propose a multi-UAV-assisted network, where the UAVs fly to the ground sensors and control the sensor's transmit power during the data collection time. Our goal is to minimize the total energy consumption of the UAVs and the sensors, which is needed to accomplish the data collection mission. We formulate this problem into three sub-problems of single UAV navigation, sensor power control as well as multi-UAV scheduling and model each part as a finite-horizon Markov Decision Process (MDP). We deploy deep reinforcement learning (DRL)-based frameworks to solve each part. Specifically, we use deep deterministic policy gradient (DDPG) method to generate the best trajectory for the UAVs in an obstacle-constraint environment, given its starting position and the target sensor. We also deploy DDPG to control the sensor's transmit power during data collection. To schedule activity plans for each UAV to visit the sensors, we propose a multi-agent deep Q-learning (DQL) approach by taking the total energy consumption of the UAVs on each path into account. Our simulations show that the UAVs can find a safe and optimal path for each of their trips. Continuous power control of the sensors achieves better performance over the fixed power approaches in terms of the total energy consumption during data collection. In addition, compared to the two commonly used baselines, our scheduling framework achieves better and near-optimal results.

Data Collection Task Planning of a Fixed-Wing Unmanned Aerial Vehicle in Forest Fire Monitoring

This article studies the data collection task planning for a fixed-wing unmanned aerial vehicle (UAV) in forest fire monitoring. Multiple wireless-based detection nodes (DNs) are distributed in high-risk areas of the forest to monitor the surrounding environment. The task of UAV is to circularly fly to them and collect the environmental data. Because of the kinematic constraints of UAV and the effective communication range between UAV and DN, this problem can be generally regarded as a Dubins traveling salesman problem with neighborhood (DTSPN). A bi-level hybridization-based metaheuristic algorithm (BLHMA) is proposed for solving this problem. At the first level, differential evolution (DE) optimizes the continuous-valued communication positions and UAV headings by the population-based search. For the asymmetric traveling salesman problem (ATSP) corresponding to the combination of the positions and headings generated by DE, a constructive heuristic based on self-organized multi-agent competition (SOMAC) is proposed to determine the discrete collection sequence. By competitive iterations in such a cooperative way in DE, a high-quality data collection tour can be generated. At the second level, a local search based on multistage approximate gradient is proposed to further refine the positions and headings, which accelerates the convergence of the BLHMA. Referring to a real-world scene of forest fire monitoring, the simulation experiments are designed, and comparative results show that BLHMA can find significantly shorter data collection tours in most cases over three state-of-the-art algorithms. The proposed UAV data collection planning algorithm is conducive to the efficient execution of the forest fire monitoring data collection mission and the energy saving of UAV.

Development of Visual Servoing-Based Autonomous Docking Capabilities in a Heterogeneous Swarm of Marine Robots

This paper describes the design, development, and testing of both hardware and software for a visual servoing-based system that enables agents within a heterogeneous marine robotic swarm to share energy. The goal of this system is prolonging the active operational time of the swarm as a whole, allowing it to perform long-term environmental monitoring and data collection missions. The implementation presented in the paper features an over-actuated autonomous surface platform docking up to four floating sensor nodes at a time and replenishing their batteries using wireless inductive charging. Mechanical solutions for each robot segment related to the docking procedure are presented, along with pertinent high-level and low-level control structures. A node featuring an extended Kalman filter and additional heuristics is used to fuse measurements from a neural network trained for object detection and a hue thresholding image processing algorithm, in order to track the docking target and achieve visual servoing. Finally, experimental results in both a controlled environment and challenging conditions on-site are presented. Once deployed, the developed system successfully enables the approach and docking of the designated target, showing its feasibility in different real-life conditions.

Age of Information Aware Trajectory Planning of UAVs in Intelligent Transportation Systems: A Deep Learning Approach

Unmanned aerial vehicles (UAVs) are envisioned to play a key role in intelligent transportation systems to complement the communication infrastructure in future smart cities. UAV-assisted vehicular networking research typically adopts throughput and latency as the main performance metrics. These conventional metrics, however, are not adequate to reflect the freshness of the information, an attribute that has been recently identified as a critical requirement to enable services such as autonomous driving and accident prevention. In this paper, we consider a UAV-assisted single-hop vehicular network, wherein sensors (e.g., LiDARs and cameras) on vehicles generate time sensitive data streams, and UAVs are used to collect and process this data while maintaining a minimum age of information (AoI). We aim to jointly optimize the trajectories of UAVs and find scheduling policies to keep the information fresh under minimum throughput constraints. The formulated optimization problem is shown to be mixed integer non-linear program (MINLP) and generally hard to be solved. Motivated by the success of machine learning (ML) techniques particularly deep learning in solving complex problems with low complexity, we reformulate the trajectories and scheduling policies problem as a Markov decision process (MDP) where the system state space considers the vehicular network dynamics. Then, we develop deep reinforcement learning (DRL) to learn the vehicular environment and its dynamics in order to handle UAVs' trajectory and scheduling policy. In particular, we leverage Deep Deterministic Policy Gradient (DDPG) for learning the trajectories of the deployed UAVs to efficiently minimize the Expected Weighted Sum AoI (EWSA). Simulations results demonstrate the effectiveness of the proposed design and show the deployed UAVs adapt their velocities during the data collection mission in order to minimize the AoI.

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach.

Data Gathering from a Multimodal Dense Underwater Acoustic Sensor Network Deployed in Shallow Fresh Water Scenarios

The Robotic Vessels as-a-Service (RoboVaaS) project intends to exploit the most advanced communication and marine vehicle technologies to revolutionize shipping and near-shore operations, offering on-demand and cost-effective robotic-aided services. In particular, the RoboVaaS vision includes a ship hull inspection service, a quay walls inspection service, an antigrounding service, and an environmental and bathymetry data collection service. In this paper, we present a study of the underwater environmental data collection service, performed by a low-cost autonomous vehicle equipped with both a commercial modem and a very low-cost acoustic modem prototype, the smartPORT Acoustic Underwater Modem (AHOI). The vehicle mules the data from a network of low cost submerged acoustic sensor nodes to a surface sink. To this end, an underwater acoustic network composed by both static and moving nodes has been implemented and simulated with the DESERT Underwater Framework, where the performance of the AHOI modem has been mapped in the form of lookup tables. The performance of the AHOI modem has been measured near the Port of Hamburg, where the RoboVaaS concept will be demonstrated with a real field evaluation. The transmission with the commercial modem, instead, has been simulated with the Bellhop ray tracer thanks to the World Ocean Simulation System (WOSS), by considering both the bathymetry and the sound speed profile of the Port of Hamburg. The set up of the polling-based MAC protocol parameters, such as the maximum backoff time of the sensor nodes, appears to be crucial for the network performance, in particular for the low-cost low-rate modems. In this work, to tune the maximum backoff time during the data collection mission, an adaptive mechanism has been implemented. Specifically, the maximum backoff time is updated based on the network density. This adaptive mechanism results in an 8% improvement of the network throughput.

Data collection planning with non-zero sensing distance for a budget and curvature constrained unmanned aerial vehicle

Data collection missions are one of the many effective use cases of unmanned aerial vehicles (UAVs), where the UAV is required to visit a predefined set of target locations to retrieve data. However, the flight time of a real UAV is time constrained, and therefore only a limited number of target locations can typically be visited within the mission. In this paper, we address the data collection planning problem called the Dubins Orienteering Problem with Neighborhoods (DOPN), which sets out to determine the sequence of visits to the most rewarding subset of target locations, each with an associated reward, within a given travel budget. The objective of the DOPN is thus to maximize the sum of the rewards collected from the visited target locations using a budget constrained path between predefined starting and ending locations. The variant of the orienteering problem addressed here uses curvature-constrained Dubins vehicle model for planning the data collection missions for UAV. Moreover, in the DOPN, it is also assumed that the data, and thus the reward, may be collected from a close neighborhood sensing distance around the target locations, e.g., taking a snapshot by an onboard camera with a wide field of view, or using a sensor with a long range. We propose a novel approach based on the Variable Neighborhood Search (VNS) metaheuristic for the DOPN, in which combinatorial optimization of the sequence for visiting the target locations is simultaneously addressed with continuous optimization for finding Dubins vehicle waypoints inside the neighborhoods of the visited targets. The proposed VNS-based DOPN algorithm is evaluated in numerous benchmark instances, and the results show that it significantly outperforms the existing methods in both solution quality and computational time. The practical deployability of the proposed approach is experimentally verified in a data collection scenario with a real hexarotor UAV.

Motion Control of Underwater Mine Explorer Robot UX-1: Field Trials

In this paper, we present the results of a series of field trials conducted with an underwater vehicle in the increasingly complex underwater environments of the Kaatiala mine in Finland, the Idrija mine in Slovenia, and the Urgeirica mine in Portugal. These field experiments have been performed to test and validate the motion control systems of the underwater explorer robot (UX-1), which are: a variable ballast system for buoyancy control, a variable pitch system for pitch control, and a propulsion system for depth and heading stabilization. The control method implemented is based on a state feedback linearization algorithm, previously tested and validated in a controlled water tank environment. Several experiments are shown which demonstrate the successful operation of the motion control systems in individual performance tests. Afterward, a full exploration and geo-scientific data collection mission is presented, where all of the motion control systems are active simultaneously during a deep dive to more than 100 m. The results obtained in the field tests demonstrate the effectiveness of the motion control systems and validate the UX-1 Robot platform as capable of performing the complex task of navigation and control in flooded mine environments.

Developing a Modular Unmanned Aerial Vehicle (UAV) Platform for Air Pollution Profiling.

The unmanned aerial vehicle (UAV) offers great potential for collecting air quality data with high spatial and temporal resolutions. The objective of this study is to design and develop a modular UAV-based platform capable of real-time monitoring of multiple air pollutants. The system comprises five modules: the UAV, the ground station, the sensors, the data acquisition (DA) module, and the data fusion (DF) module. The hardware was constructed with off-the-shelf consumer parts and the open source software Ardupilot was used for flight control and data fusion. The prototype UAV system was tested in representative settings. Results show that this UAV platform can fly on pre-determined pathways with adequate flight time for various data collection missions. The system simultaneously collects air quality and high precision X-Y-Z data and integrates and visualizes them in a real-time manner. While the system can accommodate multiple gas sensors, UAV operations may electronically interfere with the performance of chemical-resistant sensors. Our prototype and experiments prove the feasibility of the system and show that it features a stable and high precision spatial-temporal platform for air sample collection. Future work should be focused on gas sensor development, plug-and-play interfaces, impacts of rotor wash, and all-weather designs.