Range Of Unmanned Aerial Vehicles Research Articles

3D Smooth Trajectory Planning for UAVs under Navigation Relayed by Multiple Stations Using Bézier Curves

Navigation relayed by multiple stations (NRMS) is a promising technique that can significantly extend the operational range of unmanned aerial vehicles (UAVs) and hence facilitate the execution of long-range tasks. However, NRMS employs multiple external stations in sequence to guide a UAV to its destination, introducing additional variables and constraints for UAV trajectory planning. This paper investigates the trajectory planning problem for a UAV under NRMS from its initial location to a pre-determined destination while maintaining a connection with one of the stations for safety reasons. Instead of line segments used in prior studies, a piecewise Bézier curve is applied to represent a smooth trajectory in three-dimensional (3D) continuous space, which brings both benefits and complexity. This problem is a bi-level optimisation problem consisting of upper-level station routing and lower-level UAV trajectory planning. A station sequence must be obtained first to construct a flight corridor for UAV trajectory planning while the planned trajectory evaluates it. To tackle this challenging bi-level optimisation problem, a novel efficient decoupling framework is proposed. First, the upper-level sub-problem is solved by leveraging techniques from graph theory to obtain an approximate station sequence. Then, an alternative minimisation-based algorithm is presented to address the non-linear and non-convex UAV trajectory planning sub-problem by optimising the spatial and temporal parameters of the piecewise Bézier curve iteratively. Computational experiments demonstrate the efficiency of the proposed decoupling framework and the quality of the obtained approximate station sequence. Additionally, the alternative minimisation-based algorithm is shown to outperform other non-linear optimisation methods in finding a better trajectory for the UAV within the given computational time.

Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach

With the development of aviation agricultural technology, the number of farmers adopting the use of drones in daily agricultural activities is growing rapidly in recent decades. Among these, a large portion constitutes agricultural drones being used in pest control and crop protection practices, e.g. agriculture spraying of pesticides. Spraying pesticides with drones have proven to be faster than other traditional methods. On the downside, flight time and range of Unmanned Aerial Vehicles (UAV) are often limited. Thus, a proper arrangement of flight height and velocity will greatly improve spraying efficiency. A new strategy to optimize the flight parameters, i.e. flight height and flight velocity, for fixed-wing UAV with a 3D simulation-based approach together with an automatic optimization algorithm was proposed in this study. To find the optimal parameters for a UAV to fly and spray under certain environmental conditions, a three-dimensional model of the target crop was established first, followed by a detailed simulation of droplet spraying. As a demonstration case, a grass model was developed and used as the target plant, and a physics-based method was used to simulate realistically the movement of the droplets in the air as well as the interaction between the droplets and the plant to obtain the droplet deposition rate under the specified operating parameters. Furthermore, the standard Particle Swarm Optimization (PSO) algorithm was used to optimize the UAV operating parameters to obtain the best operating parameters. The results indicate that using the standard PSO algorithm to optimize the operating parameters of the drone could significantly improve the deposition rate and find the best operating parameters. Keywords: unmanned aerial vehicle, droplet deposition, 3D simulation, particle swarm optimization algorithm DOI: 10.25165/j.ijabe.20231601.6581 Citation: Xu L F, Yang Z Z, Huang Z S, Ding W L, Buck-Sorlin G. Effects of flight parameters for plant protection UAV on droplets deposition rate based on a 3D simulation approach. Int J Agric & Biol Eng, 2023; 16(1): 66–72.

Intelligent Omni-Surface Enhanced Aerial Secure Offloading

Different from conventional reflecting-only metasurfaces, intelligent omni-surfaces (IOSs) are capable of reflecting and transmitting the received signals simultaneously. As such, users located at the both sides of IOSs can be served efficiently. In this paper, a novel IOS-enhanced aerial secure offloading system is proposed in the presence of multiple ground eavesdroppers. Specifically, the IOS is adopted to prevent information leakage, improve legitimate reception quality and expand the security deployment range of unmanned aerial vehicles (UAVs). To maximize the secrecy energy efficiency (SEE) of the considered system, a non-convex resource allocation problem is formulated by allocating computing frequency, determining offloading strategy, controlling transmit power, designing phase shifts, and deploying UAV locations. The formulated problem is non-trivial and challenging to be solved directly due to the highly coupled variables. To tackle this issue, the alternating optimization technique is invoked to decouple the original one into five subproblems, and then the computing and communication settings are alternatively optimized using a low complexity iterative algorithm. Finally, simulation results demonstrate that: i) In terms of the SEE performance, our IOS-enhanced aerial secure offloading system significantly outperforms the benchmark schemes; ii) Compared with reflecting-only surfaces, IOSs can take full advantage of the flexible deployment ability of UAVs, such that their secure offloading space can be considerably expanded.

Blockchain Interoperability in Unmanned Aerial Vehicles Networks: State-of-the-Art and Open Issues

The breakthrough of blockchain technology has facilitated the emergence and deployment of a wide range of unmanned aerial vehicles (UAV) networks-based applications. Yet, the full utilization of these applications is still limited due to the fact that each application is operating on an isolated blockchain. Thus, it is inevitable to orchestrate these blockchain fragments by introducing a cross-blockchain platform that governs the inter-communication and transfer of assets in the UAV networks context. In this paper, we survey the literature on the state-of-the-art cross blockchain frameworks to highlight the latest advances in the field. We also provide an up-to-date review of blockchain-based UAV networks applications. Based on the outcomes of our survey, we introduce a spectrum of scenarios related to UAV networks that may leverage the potentials of the currently available cross-blockchain solutions. Finally, we identify open issues and potential challenges associated with the application of a cross-blockchain scheme for UAV networks that will hopefully guide future research directions.

An Efficient Energy Harvesting and Optimal Clustering Technique for Sustainable Postdisaster Emergency Communication Systems

The energy consumption and coverage range of unmanned aerial vehicles (UAVs) are major challenges in UAV-based postdisaster communications. To address these challenges, energy harvesting is employed to power communication devices and prolong the lifetime of the wireless communication network during a disaster. In addition, clustering techniques and device-to-device (D2D) communication are needed to increase the overall network coverage and provide sustainable connectivity during the disaster and postdisaster phases. We have proposed a novel emergency communication system (ECS) using the optimal cluster head (CH) technique to improve the energy transfer efficiency for sustainable network connectivity. We have developed a UAV deployment model assisted by the clustering technique and D2D links that is capable of harvesting energy to increase the network lifetime. This new approach is expected to enhance the reliability of the network in disaster situations. The proposed methods have been evaluated by measuring the energy efficiency performance and the network outage probability. The simulation results demonstrate improved performance with the deployment of optimal CHs, while the outage probability has been effectively reduced. Moreover, the proposed approach has been proven to reduce the computational complexity. In conclusion, UAV deployment with the optimal CH algorithm is a suitable network design to recover from natural disasters and potentially save many lives.

New opportunities and challenges in surveying underground cavities using photogrammetric methods

The exploitation of the underground is currently undergoing a major transformation process. Besides mining, alternative ways of utilization are playing an increasingly important role. These alternative utilizations include the storage of gases, the disposal of radioactive wastes, and the implementation of geothermal storage facilities. In addition, more and more mining operations are being shut down, like the German hard coal mining industry in 2018. But the integrity of closed mines must be ensured in the post-mining phase. In order to capture such large and complex structures efficiently and reliably, new surveying techniques are required. A promising approach is the use of photogrammetric methods to generate three-dimensional models of the cavity. Photogrammetry is currently known mainly from aerial photography. It is conceivable to use such methods for underground applications. In recent years, significant progress has been made in the algorithm-based orientation of unstructured image sets. More powerful computer systems and the growing range of unmanned aerial vehicles could enable partially automated capture. However, a number of problems have to be solved for underground imaging techniques. Unfavorable exposure conditions complicate image alignment. Furthermore, the models must be integrated into existing surveying systems with a sufficiently high degree of accuracy. In order to consider the numerous technical aspects, the development of an integrated capture and evaluation approach is necessary. This could make an important contribution to the monitoring of critical cavities of different commodities. Such an approach would also fit into the current digitization trend in the mining industry and provide new opportunities for ground control in particular.

Unmanned Aerial Vehicle Distributed Formation Rotation Control Inspired by Leader-Follower Reciprocation of Migrant Birds

Close formation flying with rotation would extend the flight range of unmanned aerial vehicle (UAV) swarms. Unfortunately, some complex conditions, such as the non-uniform fuel configuration, irregular formations, and individual accidents, would pose great challenges to the conventional cyclical formation rotation method. Inspired by the direct reciprocity mechanism observed in the migrant birds that the amount of time a bird spends following another is proportional to the duration in turn, this paper proposes a novel approach to address the challenge. First, a leader–follower reciprocation model is constructed, in which each individual will follow identic rules to accept followers and switch among following, leading, and accelerating modes. On such a basis, a distributed formation rotation algorithm is presented to coordinate the UAV swarms to fly in a changing and compact line formation to increase the swarm range under the above-mentioned complex conditions. Numerical simulations are carried out to show the feasibility, validity, and superiority of the algorithm.

Visible and thermal infrared remote sensing for the detection of white‐tailed deer using an unmanned aerial system

ABSTRACTWildlife management is based on various measurements representative of the health of populations and their habitats. Some agencies are focusing on animal surveys to manage species such as white‐tailed deer (Odocoileus virginianus). Current survey methods are faced with the challenge of reduced operating costs as well as estimating and correcting detection biases. Our pilot study (data collected on 6 Nov 2012 at Saint‐David‐de‐Falardeau, QC, Canada) assessed the potential of a new approach detect and count deer based on visible and thermal infrared image processing at very‐high spatial resolutions using an unmanned aerial system (UAS). Supervised and unsupervised pixel‐based image classification approaches as well as object‐based image analysis (OBIA) were assessed for different spatial resolutions and with different combinations of spectral bands. None of the pixel‐based approaches were effective for detecting deer. The OBIA approach detected deer with a rate of up to 100% under the best conditions by using a combination of visible and thermal infrared imagery at a spatial resolution of 0.8 cm/pixel. Overall, this approach had an average detection rate of 0.5, which is comparable to conventional aerial surveys. Visual obstructions by coniferous canopy and the spectral confusion associated with certain elements (e.g., bare soil, rocks) are problems that remain unresolved. Using UASs with image processing for surveys of deer and other species of large mammals is promising, but currently limited by the flight range of unmanned aerial vehicles and the associated regulations. © 2016 The Wildlife Society.

Effective Range of Unmanned Aerial Vehicle (UAV) in the Malaysian Army Tactical Operations

Unmanned Aerial Vehicle (UAV) has been used in military application even during the conflicts in World War II. The utilization of UAV was further expanded during the Vietnam War and the role of “eye in the sky” has seen UAV became the ultimate ISTAR (Intelligence, Surveillance, Target Acquisition and Reconnaissance) tool for almost all conflicts. The evolution of UAV in various sizes and features will see UAV saturating the battlefield theater’s cloud in the future. This paper is to present the concept of operations of UAV in the military, segmented by units in battle to enable researchers to concentrate their research on types of UAV to suit the needs of the Malaysian Army. Using effective firepower range of all Malaysian Army arsenals and the commonality in terms of tasking, this study is able to establish the classes in terms of range needed for UAV in the Malaysian Army. From the observation, it is clear that the majority of the intended UAV will be operating in the less than 3 km combat radius.

A Compact Guidance, Navigation, and Control System for Unmanned Aerial Vehicles

The Flight Control System 20 (FCS20) is a compact, self-contained Guidance, Navigation, and Control system that has recently been developed to enable advanced autonomous behavior in a wide range of Unmanned Aerial Vehicles (UAVs). The FCS20 uses a floating point Digital Signal Processor (DSP) for high level serial processing, a Field Programmable Gate Array (FPGA) for low level parallel processing, and GPS and Micro Electro Mechanical Systems (MEMS) sensors. In addition to guidance, navigation, and control functions, the FCS20 is capable of supporting advanced algorithms such as automated reasoning, artificial vision, and multi-vehicle interaction. The unique contribution of this paper is that it gives a complete overview of the FCS20 GN&C system, including computing, communications, and information aspects. Computing aspects of the FCS20 include details about the design process, hardware components, and board configurations, and specifications. Communications aspects of the FCS20 include descriptions of internal and external dataflow.The information section describes the FCS20 Operating System (OS), the Support Vehicle Interface Library (SVIL) software, the navigation Extended Kalman Filter, and the neural network based adaptive controller. Finally, simulation-based results as well as actual flight test results that demonstrate the operation of the guidance, navigation, and control algorithms on a real Unmanned Aerial Vehicle (UAV) are presented.