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.
We consider a remote sensing system in which fixed sensors are placed in a region, and a single drone flies over the region to collect information from cluster heads. We assume that the drone has a fixed maximum range and that the energy consumption for information transmission from the cluster heads increases with distance according to a power law. Given these assumptions, we derive local optimum conditions for a drone path that either minimizes the total or maximum energy required by the cluster heads to transmit information to the drone. We show how a homotopy approach can produce a family of solutions for different drone path lengths so that a locally optimal solution can be found for any drone range. We implement the homotopy solution in Python and demonstrate the tradeoff between drone range and cluster head power consumption for several geometries. Execution time is sufficiently rapid for the computation to be performed in real time so that the drone path can be recalculated on the fly. The solution is shown to be globally optimal for sufficiently long drone path lengths. A proof of concept implementation in Python is available on GitHub. For future work, we indicate how the solution can be modified to accommodate moving sensors.
This paper presents, an autonomous and scalable monitoring system for early detection and spread estimation of wildfires by leveraging low-cost UAVs, satellite data and ground sensors. An array of ground sensors, such as fixed towers equipped with infrared cameras and IoT sensors strategically placed in areas with a high probability of wildfire, will work in tandem with the space domain as well as the air domain to generate an accurate and comprehensive flow of information. This system-of-systems approach aims to take advantage of the key benefits across all systems while ensuring seamless cooperation. Having scalability and effectiveness in mind, the system is designed to work with low-cost COTS UAVs that leverage infrared and RGB sensors which will act as the primary situational awareness generator on demand. AI task allocation algorithms and swarming-oriented area coverage methods are at the heart of the system, effectively managing the aerial assets High-level mission planning takes place in the GCS, where information from all sensors is gathered and compiled into a user-understandable schema. In addition, the GCS issues warnings for events such as the detection of fire and hardware failures, live video feed and lower-level control of the swarm and IoT sensors when requested. By performing intelligent sensor fusion, this solution will offer unparalleled reaction times to wildfires while also being resilient and reconfigurable should any hardware failures arise by incorporating state of the art swarming capabilities.
While aerial photography continues to play an integral role in forest management, its data acquisition can now be obtained through an unmanned aerial vehicle (UAV), commonly referred as a drone, instead of conventional manned aircraft. With its feasibility, a drone can be programed to take off, fly over an area following predefined paths and take images, then return to the home spot automatically. When flying over forests, it requires that there is an open space for a vertical takeoff drone to take off vertically and return safely. Hence, the automatic return-to-home feature on the drone is crucial when operating in a woodland landscape. In this project, we assessed the return-to-home landing accuracy based on a permanently marked launch pad nested in a wooded area on the campus of Stephen F. Austin State University in Nacogdoches, Texas. We compared four models of the DJI drone line, with each flown 30 missions over multiple days under different weather conditions. When each drone returned to the home launch spot and landed, the distance and direction from the launch spot to the landing position was measured. Results showed that both the Phantom 4 Advanced and the Spark had superior landing accuracy, whereas the Phantom 3 Advanced was the least accurate trailing behind the Phantom 4 Pro.
The rising cost and scarcity of human labor pose challenges in security patrolling tasks, such as facility security. Drones offer a promising solution to replace human patrols. This paper proposes two methods for finding the minimum number of drones required for efficient surveillance routing: an ILP-based method and a greedy method. We evaluate these methods through experiments, comparing the minimum number of required drones and algorithm runtime. The findings indicate that the ILP-based method consistently yields the same or a lower number of drones needed for surveillance compared to the greedy method, with a 73.3% success rate in achieving better results. However, the greedy method consistently finishes within one second, whereas the ILP-based method sometimes significantly increases when dealing with 14 more locations. As a case study, we apply the greedy method to identify the minimum drone surveillance route for the Osaka-Ibaraki Campus of Ritsumeikan University.
