Flight Planning for Unmanned Aerial Vehicles

The proliferation of unmanned aerial vehicles (UAVs) has driven the emergence of urban air mobility (UAM) as a solution to alleviate the increasing demand for UAVs applications in various domains. The concept of UAM envisions a future where UAVs play a pivotal role in short-distance transportation, delivery services, surveillance, emergency rescue, and more. However, the rapid growth of UAVs poses significant challenges arising from the need to ensure the safe coexistence of UAVs with other manned/unmanned aerial vehicles. To ensure the safe integration of UAVs into low-altitude urban airspace and support high-density on-demand mobility, this research focuses on handling the safety-centric challenges related to UAV collision risk assessment, obstacle avoidance, and conflict resolution in complex urban environments.
\nOne of the main challenges is the intrusion of UAVs into airport runways. Airports serve as critical hubs for civil aircraft takeoff and landing operations. However, the increasing presence of UAVs has raised concerns about intruding into airports, leading to flight delays and temporary runway closures. To quantify the potential interference of UAVs on civil aircraft, a collision-course based trajectory planning model is proposed to evaluate the collision risk between manned civil aircraft and intruding UAVs. In this risk assessment model, the trajectory of the intruding UAV is formulated using a collision-course concept, taking into account the limited trajectory information available about intruding UAVs. The planned trajectory of the UAVs is then used to predict its relative position concerning civil aircraft. To account for the possible disturbances of the relative distance, a stochastic position prediction model based on Brownian motion is developed. The collision risk is assessed based on this stochastic kinematic model, providing a quantitative measure of the potential collisions. The proposed model is validated through extensive simulations involving different UAV initial positions, position updates, and different sizes of collision zone tests, as well as comparative tests with the Monte-Carlo algorithm, demonstrating its robustness in assessing the collision risk of the airport restricted area. To facilitate the application, the model is finally used to develop a platform to generate runway heatmaps, accounting for airport runway risk distributions. 
\nAnother safety-centric challenge is ensuring autonomous obstacle avoidance and conflict resolution of UAVs. To enable UAV autonomous safe operations in uncontrolled urban low-altitude airspace, a comprehensive collision-free path planning framework is developed to address the avoidance of both static obstacles and dynamic threats. Firstly, to handle static obstacles, a novel algorithm of 3D voxel jump point research is introduced to generate a global reference path for UAV navigation to avoid static obstacles, particularly for a free flight at different altitudes. Additionally, to optimize the reference path for specific mission requirements, techniques such as trajectory de-diagonalization, reconstruction, and smoothness are proposed to reduce the risk of static collisions, shorten the fly range, and enable UAVs to negotiate turns more efficiently. Secondly, real-time conflict resolution actions based on the Markov decision process are developed to avoid local dynamic threats encountered along the optimized desired path. This allows the host UAV to dynamically adjust its course to a resolution trajectory while maintaining its planned speed without hovering to avoid the potential collision with the approaching intruders. By avoiding near mid-air collisions (NMAC) and preventing the UAV from hovering, the proposed method ensures the timely completion of UAV missions. Simulation results demonstrate that the developed method successfully achieves UAV autonomous global path planning and local real-time conflict resolution in complex urban low-altitude environments, thereby avoiding both static obstacles and dynamic threats. 
\nMoreover, to bridge the research gap of UAV hard conflict resolution in mitigating NMAC risks arising from local dynamic threats, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. To improve UAV operating environments in urban airspace, a concept of tiered aerial passages is first designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity and optimizes the utilization of urban airspace. For UAV tiered aerial passage operations, a situational NMAC risk assessment module coupled with a conflict resolution decision-making module is formulated, enabling the host UAV to evaluate and respond to real-time conflicts, ensuring avoidance of potential collisions. To assess NMAC risks, a general risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the overtaking, approaching head-on, and converging UAV encounters are analyzed, and situational risk potential scores are created using the Gaussian kernel function in the CRF algorithm, considering advisory, caution, and warning risk alert levels. To resolve conflict, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed scheme is validated by simulating geometric encounter situations and comparative tests with the previous hard resolution technique. The results indicate that the proposed method outperforms UAV NMAC risk assessment and conflict resolution. Even in the most dangerous unacceptable resolution decision-making scenarios across three conflict encounters, the proposed algorithm achieves NMAC rates of 0.036, 0.070, and 0.043, which are lower than the corresponding NMAC rates associated with the hard resolution approach. Together with the acceptable and tolerable scenarios, the proposed negotiation resolution effectively tailors different risk requirements to accommodate diverse UAV missions. 
\nWith a primary focus on ensuring the safety of the UAM architecture, this research endeavors to assess UAV NMAC risks and efficiently manage potential conflicts. The proposed work are thoroughly validated to demonstrate their effectiveness in real-world scenarios. As a result, this study significantly contributes to the establishment of a robust UAM architecture, facilitating the realization of safe and reliable UAM.

This paper describes the Naval Air Systems Command (NAVAIR) unmanned aerial vehicle (UAV) / unmanned combat aerial vehicle (UCAV) distributed simulation infrastructure. This highly flexible and powerful infrastructure was exercised in a realistic interoperability demonstration. In this effort, the adopted scenario successfully ran as planned from beginning to end with a direct hit on the intended target. This demonstration established the value that NAVAIR brings, through the modeling and simulation tools offered in its facilities, to fully develop and exploit the capabilities and interoperability of UAVs and UCAVs. The exercise provided a baseline for a linked UAV infrastructure. However, some suggested modifications would greatly improve the fidelity and enhance the capabilities of the demonstration. The ultimate product would facilitate studying the interactions between UAVs and UCAVs to enhance our warfighters’ capabilities.

We consider an Unmanned Aerial Vehicle (UAV, also known as drone) as an aerial sink to travel along a natural landscape or rural industrial linear infrastructure to collect data from deployed sensors. We study a joint schedule problem that involves flight planning for the drone and transmission scheduling for sensors, such that the maximum amount of data can be collected with a limited individual energy budget for the UAV and the sensors, respectively. On one hand, the flight planning decides the flight speed and flight path based on sensor locations, energy budgets, and the transmission schedule. On the other hand, the transmission schedule decides for each sensor when to deliver data and what transmission power to use based on the energy budgets and flight plan. By observing three import optimality properties, we decouple the joint problem into two subproblems: drone flight planning and sensor transmission scheduling. For the first problem, we propose a dynamic programming algorithm to produce the optimal flight planning. For the second problem, with a flight plan as input, we introduce a novel technique (water-tank), which together with dynamic programming, is the key to achieve an optimal transmission schedule that maximizes data collection. Simulations show that the separately determined flight plan and transmission schedule are near-optimal for the original joint problem.

An evaluative review of the VTOL technologies for unmanned and manned aerial vehicles

Heterogeneous unmanned ground vehicle-unmanned aerial vehicle (UGV-UAV) collaborative systems offer clear advantages for field exploration. However, when tethered unmanned aerial vehicles (TUAVs) are introduced to extend mission capability, a major compatibility gap emerges for small and highly maneuverable UGVs: existing industrial tethered ground stations are generally too heavy and bulky to be carried by such platforms. In addition, on unstructured ground, residual station tilt can significantly complicate UAV launch and recovery. To address these issues, this paper develops an ultralight vehicle-mounted tethered ground station for micro unmanned aerial vehicles (micro-UAVs) that can be integrated directly with small UGVs. Through co-design of a 2-degree-of-freedom (2-DOF) self-leveling launch platform and a passive tether-assisted recovery scheme without visual fiducials, in which a customized UAV flight-control loop is coordinated with the state transitions of the ground tether-management system, the proposed system achieves practical tether-assisted recovery. Experiments show that the complete platform weighs only 4.1 kg and that the self-leveling mechanism compensates for ground inclinations over a total range of 24 degrees. Repeated passive-landing tests further demonstrate the feasibility of the proposed recovery scheme and its tolerance to moderate bay tilt and terminal off-axis activation. System-level flight validation confirms practical tether-assisted recovery without visual fiducials. In addition, we conduct a simplified exploratory simulation of tether-based ground-anchor localization under the proposed system architecture. Overall, these results establish a lightweight and low-cost hardware design and a practically viable recovery strategy for multimodal micro air-ground robotic systems.

With the development of technologies related to unmanned vehicles, including unmanned aerial vehicle and unmanned surface vehicle, unmanned vehicles play a pivotal role in maritime management and cruise activities. Meanwhile, lots of evidence shows that a single unmanned vehicle is not qualified for operation under complex environment, whereas the unmanned aerial vehicle–unmanned surface vehicle formation consists of multiple unmanned aerial vehicles and unmanned surface vehicles, which can enhance the performance of the single unmanned vehicle significantly. However, the cooperative operation performance of the unmanned aerial vehicle–unmanned surface vehicle formation is constrained by its communication capacity. Consequently, a cooperative communication framework is designed for the unmanned aerial vehicle–unmanned surface vehicle formation. A distributed dynamic network topology is constructed for fulfilling effective communication for the unmanned aerial vehicle–unmanned surface vehicle formation based on ad hoc network. Experiments are carried out by employing the built unmanned aerial vehicle–unmanned surface vehicle formation platform. Results verify the superiority of the designed cooperative communication framework for the unmanned aerial vehicle–unmanned surface vehicle formation.

The deployment of battery powered Unmanned Aerial Vehicle (UAV) is constrained mainly due to its lack of range per single charge. In this paper, load modelling of an UAV at different stages like take-off, level flight and landing at different conditions like constant altitude - variable speed, constant speed - variable altitude, constant altitude and speed - variable take-off and landing angle; has been derived. Analysis of Thrust and Power requirement of UAV at different stages has been discussed in the paper. This analysis can be used for the selection of components and battery required for the UAV, depending upon the particular application. This can also be used in power estimation and flight planning accordingly.

This paper addresses the problem of controlling a heterogeneous system composed of multiple Unmanned Aerial Vehicles (UAVs) and Autonomous Underwater Vehicles (AUVs) for formation and containment maintenance. The proposed approach considers actuator time delay and, in addition to formation and containment, considers obstacle avoidance, and offers a robust navigation algorithm and uses a reliable middleware for data transmission and exchange. The methodology followed uses both flocking technique and modified L1 adaptive control to ensure the proper navigation and coordination while avoiding obstacles. The data exchange between all the agents is provided through the data distribution services (DDS) middleware, which solves the interoperability issue when dealing with heterogeneous multiagent systems. The modified L1 controller is a local controller for stabilizing the dynamic model of each UAV and AUV, and the flocking approach is used to coordinate the followers around the leader or within the space delimited by their leaders. Potential Field (PF) allows obstacle avoidance during the agents’ movement. The performance of the proposed approach under the considerations mentioned above are verified and demonstrated using simulations.

Unmanned aerial vehicles (UAVs) are rapidly developing in major fields but also exposed it to security vulnerabilities. UAVs operate in high airborne network where it encounters many obstacles and uncertainty. And the existing path planning algorithms don’t consider path deviation attacks where the data communication is not secured. But, in multiple unmanned aerial vehicles, path planning is a distinctive optimization problem. And for swarm of unmanned aerial vehicles (UAVs) deployed in an uncertain dynamic environment, optimizing the UAV coverage area is a major research area that it needs to be addressed to support effective and secure data transmission. For optimized path planning in UAV swarm, we proposed a modified particle swarm optimization (MPSO) algorithm which encodes the multidimensional points for instant optimal path. The factors that are considered for the encoding helps to find the global optimum. And the blockchain distributed ledger in UAV nodes will protect the UAV network from security attacks such as denial-of-service and man-in-the-middle attacks. It also supports unrestricted coverage area, where the storage is made active only on demand which is dynamically cost-effective in UAV network. Simulation results shows that the proposed algorithm provides instant optimal path with unrestricted coverage aided with data security.KeywordsSwarm optimizationDenial-of-service attackMan-in-the-middle attackBlockchainData securityUnmanned aerial vehicles

Laser scanning of forested areas helps in analyzing and understanding various aspects of forest conditions, including distribution of plants and trees, height distribution of trees, tree density, size and volume of wood, as well as ground surface properties. However, laser scanning of forest areas is also very challenging for many reasons. The best time for scanning is before trees leaf out in the spring or after trees cast their leaves in autumn before snowfall so an unmanned aerial vehicle (UAV) laser scanner can penetrate the forest from the tops of the trees down to the ground surface. To receive highly accurate laser data and high point density, the flight planning must be adjusted judiciously. Flight planning will be even more complex in steep terrain where the UAV cannot operate at a constant altitude. This paper discusses a UAV-based 3D laser data recording — light detection and ranging (LiDAR) scanning — of a forestry area with high accuracy and point cloud resolution. In addition, the point cloud of airborne laser scanning is compared with local terrestrial laser-scanning results. The forest area consists of mixed forests containing varying tree sizes and branch deformation. This paper summarizes our latest results in UAV-based LiDAR acquisition over a forest area to extract detailed forest and ground information and finds that UAV-based laser scanning is well suited for provision of both high-quality forest structural and terrain elevation information.

Unmanned Aerial Vehicles (UAVs) are used in various applications, including crowd management, crime prevention, accident detection, and rescue operations. However, since UAVs perform their tasks independently, some UAV applications are dynamic and geographically distributed, which may require extensive real-time processing capabilities. Thus, processing UAV data locally can be challenging due to their limited computing capabilities. To overcome such limitations, fog and cloud computing can facilitate UAV application development by providing additional resource capacities when needed. Despite this, designing sophisticated and efficient UAV task offloading strategies that collaborate with fog and cloud technologies considering their service latency and energy consumption, is rarely addressed in the literature. Therefore, a collaborative offloading strategy for UAV applications is presented in this work, leveraging fog and cloud computing advantages and capabilities. This approach aims to minimize UAVs’ service latency and energy consumption, as well as provide the required resources and services in real time. In addition, task offloading decisions are formulated using the Mixed-Integer Linear Programming (MILP) model to reduce the energy consumption of the entire UAV-fog-cloud system by optimizing the allocation of computation resources and communication requested by each UAV. The simulation results demonstrate that the proposed strategy can significantly reduce UAV service latency by 15.38%, 35.29%, and 59.26%, as well as decrease overall energy consumption (including processing and networking) by 3.3%, 7.37%, and 12% when compared to alternative standalone strategies (namely UAV, fog, and cloud).

In this chapter, the regulation of an Unmanned Aerial Vehicle (UAV) communication network is investigated in the presence of dynamic changes in the UAV lineup and user distribution. We target an optimal UAV control policy which is capable of identifying the upcoming change in the UAV lineup (quit or join-in) or user distribution, and proactively relocating the UAVs ahead of the change rather than passively dispatching the UAVs after the change. Specifically, a deep reinforcement learning (DRL)-based UAV control framework is developed to maximize the accumulated user satisfaction (US) score for a given time horizon which is able to handle the change in both the UAV lineup and user distribution. The framework accommodates the changed dimension of the state-action space before and after the UAV lineup change by deliberate state transition design. In addition, to handle the continuous state and action space, deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic based DRL method, is exploited. Furthermore, to promote learning exploration around the timing of the change, the original DDPG scheme is adapted into an asynchronous parallel computing (APC) structure which leads to better training performance in both the critic and actor networks. Finally, extensive simulations are conducted to validate the convergence of the proposed learning approach, and demonstrate its capability in jointly handling the dynamics in UAV lineup and user distribution as well as its superiority over a passive reaction method.KeywordsUnmanned aerial vehicle (UAV)UAV-based communication networksDeep reinforcement learningDynamic user spatial distributionUAV trajectory design

Abstract: This review paper offers a comprehensive exploration of the multifaceted applications of Unmanned Aerial Vehicles (UAVs) in various domains, showcasing their transformative impact in addressing complex challenges. The evaluation of cloud-based UAV systems' stability reveals their robustness and reliability, underlining their significance in numerous industries. Additionally, their role in enhancing robot navigation in intricate environments signifies a substantial advancement in robotics and automation. The integration of blockchain technology for secure Internet of Things (IoT) data transfer emphasizes the critical importance of data integrity and confidentiality in the IoT era. Furthermore, the optimization of energy-efficient data collection in IoT networks through UAVs demonstrates their potential to revolutionize data-driven decision-making processes, particularly in fields reliant on data accuracy and timeliness. The paper also highlights the application of deep reinforcement learning to enhance UAV-assisted IoT data collection, showcasing the synergy between advanced machine learning techniques and UAV technology. Finally, the discussion underscores the pivotal role of UAVs in precision agriculture, where they facilitate ecological farming practices and monitor environmental conditions, contributing to the pursuit of sustainable and efficient agriculture. This review reaffirms UAVs' status as transformative tools, reshaping industries and unlocking new frontiers of innovation and problem-solving. With ongoing technological advancements, UAVs are poised to play an increasingly central role in a wide range of applications, promising a future marked by ground breaking possibilities. Key findings include the dominance of the United States and China in the field, exploration of characteristics such as crop production, and innovative UAV-based methods for grassland mapping, maize growth assessment, and Arctic plant species monitoring. The research underscores the potential of UAVs in bridging field data and satellite mapping, providing valuable insights into diverse applications, from soil analysis to yield predictions, highlighting their transformative role in environmental monitoring and precision agriculture

As a complicated multi-input, multi-output, and time-varying nonlinear system, flight control system of unmanned aerial vehicle (UAV), which determines the whole system’s performance directly, is crucial for the simulation training system design. This chapter mainly focuses on parameter identification of flight control system based on the modeling of UAVs and a specific controller design for the pendulum-like oscillation in micro aerial vehicle (MAV). A predator–prey particle swarm optimization (PSO) algorithm for identifying parameters of UAV flight control system is presented, with the aim of reducing the workload of the designers during the process of designing complicated UAV control system. Besides, a software environment for UAV controller design was developed based on the UAV model and the proposed method. Then a specific kind of controller design involving the pendulum-like oscillation in the hover and stare state for a MAV in the presence of external disturbances is discussed in detail, since the pendulum-like oscillation caused by uncertainty and external disturbances badly jeopardize the performance of hover and stare, resulting in blurred images or even MAV’s overturning. A novel type of PSO-based linear-quadratic regulator (LQR) controller for stabilizing the pendulum-like oscillation is developed, which can enhance the MAV’s performance efficiently.

High-throughput unmanned aerial vehicle (UAV) communication may unleash the true potential of novel applications for aerial vehicles but also represents a threat for cellular networks due to the high levels of generated interference. In this article, we investigate how a beamforming system installed on board a UAV can be efficiently used to ensure high-throughput uplink UAV communications with minimum impact on the services provided to users on the ground. We study two potential benefits of beamforming, namely, spatial filtering of interference and load balancing, considering different beam switching methodologies. Our analysis is based on system-level simulations followed by a series of measurement campaigns in live Long-Term Evolution (LTE) networks. Our results show that using UAV-side beamforming has a great potential to increase uplink throughput of a UAV while mitigating interference. When beamforming is used, even up to twice as many UAVs may be served within a network compared with UAVs using omni-directional antennas, assuming a constant uplink throughput target. However, to fully exploit the potential of beamforming, a standardized solution ensuring alignment between network operators and UAV manufacturers is required.