Due to the complex geometry of the monuments, it is often necessary to adapt the image collection process for their mapping. For the optimal mapping of the stronghold of Lazaritsa Chorygi (Greece) and its slopes, vertical, inclined, and horizontal images from different heights were collected using an Unmanned Aircraft System. Thus, for a monument of special archaeological/historical interest and natural beauty, a large set of high-spatial resolution data and final products (digital surface model and orthophotomosaic with spatial resolution 5.6 cm and 2.8 cm, respectively) is available. In addition, in the wider area of the fortified site, military structures (fire trenches, communication trenches, shelters, front and support trenches, and strong points) of the Great War length of 9 km were identified and mapped, which were identified in the 2003 or 2004 Google Earth Pro images, but worryingly are almost absent from the contemporary Google Earth Pro images.
The behaviour of co-axial rotors is well understood, and they are especially practical for large UAVs due to their increased thrust without changing the vehicle footprint. However, for co-axial systems with varying propeller diameters between the two disks, research is more limited. The goal of this paper was to determine an optimal configuration for several different unequal co-axial setups using numerous different propeller combinations and separation ratios. Propellers with diameters of 26 and 29 inches are tested at separation ratios of 0.05 to 0.35. Thrust and power were collected using an off-the-shelf FS15-TYTO thrust stand, with the upstream and downstream propellers running at equal throttles. From this, performance was assessed through efficiency, thrust, and power consumption, and comparisons were made to an ideal combination without losses. The results show that for unequal combinations, the user should place the smaller propeller upstream for greater efficiency, but for maximum thrust capacity, two equal propellers are preferred. When compared to two independent rotors of the same size, a 26″ upstream rotor and a 29″ downstream rotor minimised thrust loss to 16%, compared to 23% for the opposite arrangement. It was also found that the optimal separation ratio is always approximately 0.2.
This paper addresses the finite-time stabilization problem for a nonholonomic wheeled mobile robot (NWMR) with input constraints. By utilizing the hyperbolic tangent function tanh(·), bounded finite-time stabilization controllers are developed. In addition, an explicit upper-bound estimate for the closed-loop settling time is given, and the level of input constraints is characterized by parameters that depend on the actuator’s capacity. A thorough finite-time stability analysis is carried out using appropriate Lyapunov functions. For a compact set contained in the domain of attraction, a guideline is presented to clarify how to construct it. Finally, simulation results show the effectiveness of the developed controllers.
In this paper, the distributed leader-follower consensus of a group of agents with second-order dynamics under the undirected graph communication topology is studied. The main objective of this study is to solve a major practical multi-agent problem in which the acceleration of the leader is not communicated to each follower. In contrast, the follower agents include some unknown dynamics in their intrinsic structure. By assuming a linear regression structure for leader acceleration and agent’s unknown dynamics, Lyapunov-based adaptive control algorithms are devised to control the network of agents in the presence of the communication loss and modeling uncertainties. The presented study describes two multi-agent control strategies called fully-distributed adaptive control (FDAC) and partially-distributed adaptive control (PDAC) systems in the first method, the followers do not have any a priori information about the communication graph, while in the second method, some information about the eigenvalues of the communication graph is available. The mathematical manipulations required to prove the stability of the FDAC and PDAC methods are presented. Finally, illustrative simulations are conducted to render the proposed algorithms’ merits and efficiencies.
In response to the ever-growing global demand for Unmanned Aerial Vehicles, efficient battery solutions have become vital. This paper proposes a design and concept of an Autonomous Mid Air Battery Swapping System for Vertical Take-Off and Landing Unmanned Aerial Vehicles. The proposed design integrates Aerial Mechatronics, Lighter than Air Systems, and Digital Modelling by leveraging the innovative concept of aerostats for battery swapping. This adaptive and effective technology paves the way for the next generation of autonomous Vertical Take-Off and Landing, ensuring a longer flight time and range. Modern-day technologies have empowered Unmanned Aerial Vehicles to operate autonomously and be remotely controlled, expanding their utility across diverse industries. The enhanced Vertical Take-Off and Landing capabilities include the ability to dock on an aerostat-mounted system, facilitating seamless battery swapping without human intervention and ensuring extended flight duration and operational flexibility. These advancements promise to broaden the applications of Unmanned Aerial Vehicles across various industries.
This paper proposes a distributed reinforcement learning method for multi-robot cooperative target search based on policy gradient in 3D dynamic environments. The objective is to find all hostile drones which are considered as targets with the minimal search time while avoiding obstacles. First, the motion model for unmanned aerial vehicles and obstacles in a dynamic 3D environments is presented. Then, a reward function is designed based on environmental feedback and obstacle avoidance. A loss function and its gradient are designed based on the expected cumulative reward and its differentiation. Next, the expected cumulative reward is optimized by a reinforcement learning algorithm that makes the loss function update in the direction of the gradient. When the variance of the expected cumulative reward is lower than a specified threshold, the unmanned aerial vehicle obtains the optimal search policy. Finally, simulation results demonstrate that the proposed method effectively enables unmanned aerial vehicles to identify all targets in the dynamic 3D airspace while avoiding obstacles.
Amid a global metacrisis of health, environmental and economic challenges, medical delivery drones (or uncrewed aerial vehicles) offer a promising method to prepare for, and rapidly respond, to future emergencies. This opinion article summarizes the current medical delivery drone landscape, evidence base, and policy implications in the context of public health emergencies, such as pandemics, natural disasters, and humanitarian crises, with a particular emphasis on the region of sub-Saharan Africa. Using a multilateral, international health policy perspective, key challenges and opportunities, such as the development of sustainable funding mechanisms, robust regulatory frameworks, and capacity building, are identified.
This paper, intended for expert and non-expert audiences, evaluates the technical and regulatory requirements for Unmanned Aerial Systems (UAS) to operate beyond visual line of sight (BVLOS) services. UAS BVLOS operations have the potential to unlock value for the industry. However, the regulatory requirements and process can be complex and challenging for UAS operators. The work explored the BVLOS regulatory regime in the UK, Europe and the US and found similarities in process and requirements covering themes like Detect and Avoid (DAA), Remote identification and Reliable Connectivity. A unifying goal across these jurisdictions is to operate BVLOS safely and securely in non-segregated airspace. However, operating BVLOS in segregated airspace as the default or routine mode could accelerate approval and adoption. The paper reviewed existing challenges, highlighting Coverage, Capacity and Redundancy as critical for UAS BVLOS Operations. The work also highlighted the crucial role of Non-terrestrial Network (NTN) assets like Satellites and HAPS (High Altitude Platform Station) since terrestrial networks (not optimised for aerial platform coverage) may not be reliable for BVLOS connectivity.
Wildfires have proven to be a significantly exigent issue over the past decades. An increasing amount of research has recently been focused on the use of Unmanned Aerial Vehicles (UAVs) and multi-UAV systems for wildfire monitoring. This work focuses on the development of a decentralized framework for the purpose of monitoring active wildfires and their surrounding areas with fixed wing UAVs. It proposes a distributed fire data update methodology, a new formation algorithm based on virtual forces, fine-tuned by a Genetic Algorithm (GA), to arrange virtual agents into the monitoring area, and a control strategy to safely and efficiently guide fixed wing UAVs to loiter over the structured virtual agents. The system is tested in Software In The Loop (SITL) simulation with up to eight UAVs. The simulation results demonstrate the effectiveness of the system in monitoring the fire in a persistent manner and providing updated situational awareness data. The experiments show that the proposed framework is able to achieve and maintain coverage up to 100% over the area of interest, and very accurate fire representation. However, the performance is decreased for the experiments with low UAV numbers and large fire sizes.
In recent years, there has been a growing interest in utilizing drones for parcel delivery among companies, aiming to address logistical challenges. However, effective optimization of delivery routes is essential. A theoretical framework termed the Flight Speed-aware Vehicle Routing Problem (FSVRP) has emerged to address the variability in drone flight speed based on payload weight. Several approximate methods have been proposed to solve the FSVRP. Our research endeavors to optimize parcel delivery efficiency and reduce delivery times by introducing a novel delivery problem. This problem accounts for multiple deliveries while considering the variability in flight speed due to diverse payloads. Through experimentation, we evaluate the efficacy of our proposed method compared to existing approaches. Specifically, we assess total flight distance and flight time. Our findings indicate that even in cases where the payload exceeds maximum capacity, all parcels can be delivered through multiple trips. Furthermore, employing a multi-trip FSVRP approach results in an average reduction of 10% in total flight time, even when payload capacities are not exceeded.
