Pair Of Unmanned Aerial Vehicles Research Articles

UAV Inventory of the Last Remaining Dragon Tree Forest on Earth

The last remaining Dragon Tree Forest on Earth survives on a small plateau (Roqeb di Firmihin) in Socotra Island (Yemen). The Socotran Dragon’s Blood Tree (Dracaena cinnabari Balf. f.) is endemic to the Socotra Archipelago UNESCO World Heritage Site. Being a culturally important and endangered tree species, its conservation is a priority. Despite this, a complete inventory of the Firmihin Dracaena forest (14.9 km2) has never been attempted before. We applied the use of unmanned aerial vehicles (UAVs) for the first time in conserving the Socotran Dragon’s Blood Tree. A pair of UAVs (small drones) were used during field surveys in 2021 to spatially describe individual tree positions, tree density, mortality, and the forest age structure. Aerial images were processed into a single orthophoto image of high spatial resolution (8 cm/pixel) used for detailed analysis. We applied image-enhancement techniques, used object-based classification, and corrected every entry manually during the inventory process. In total 35,542 individual living trees and 2123 uprooted trees were inventoried. The mean age of the forest, based on crown age (derived from crown size) was estimated at an average of ca. 300 years (291.5 years) with some individuals older than 500 years. Our analysis reveals that the trajectory of recent cyclones and the average direction of fallen trees in Firmihin are correlated, suggesting that intensified winds (as a result of global warming) catalyze the decline of the overmature forest. Our study illustrates the use of UAVs in collecting crucial data for the conservation and threat assessment of endangered tree species in Socotra, and regular drone inventories could be applied (e.g., after future cyclone events or landslides) to better evaluate the status of these vulnerable island ecosystems.

Biologically inspired herding of animal groups by robots

Abstract A single sheepdog can bring together and manoeuvre hundreds of sheep from one location to another. Engineers and ecologists are fascinated by this sheepdog herding because of the potential it provides for ‘bio‐herding’: a biologically inspired herding of animal groups by robots. Although many herding algorithms have been proposed, most are studied via simulation. There are a variety of ecological problems where management of wild animal groups is currently impossible, dangerous and/or costly for humans to manage directly, and which may benefit from bio‐herding solutions. Unmanned aerial vehicles (UAVs) now deliver significant benefits to the economy and society. Here, we suggest the use of UAVs for bio‐herding. Given their mobility and speed, UAVs can be used in a wide range of environments and interact with animal groups at sea, over the land and in the air. We present a potential roadmap for achieving bio‐herding using a pair of UAVs. In our framework, one UAV performs ‘surveillance’ of animal groups, informing the movement of a second UAV that herds them. We highlight the promise and flexibility of a paired UAV approach while emphasising its practical and ethical challenges. We start by describing the types of experiments and data required to understand individual and collective responses to UAVs. Next, we describe how to develop appropriate herding algorithms. Finally, we describe the integration of bio‐herding algorithms into software and hardware architecture.

Formation control for multi-UAVs systems based on Kullback-Leibler divergence

This paper presents a formation control method for multi unmanned aerial vehicles (UAVs) systems. The first step is to design two probability density functions describing to the desired formation and current formation, respectively. Then, through minimizing the Kullback-Leibler divergence, this method is able to bring the UAVs to a desired formation and stabilizes the desired formation in all initial conditions except the case where a pair of UAVs are in the same initial position. The gradient of Kullback-Leibler divergence is calculated using Monte Carlo method, by means of which it is not necessary to preplan route for every UAV and to take extra measure to avoid collisions between any two UAVs during the motion. At the end of this paper, the proposed method is adopted to carry out to some numerical simulations in a two-dimensional space and a three-dimensional space, respectively, to illustrate the effectiveness of the method. Conclusions show that the formation of the UAVs can converge to the desired formation under the control law proposed in this paper.

Distributed hierarchical control for multiple vertical takeoff and landing UAVs with a distance‐based network topology

SummaryA distributed formation control algorithm is proposed for multiple vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) in this paper. The neighboring information is obtained by active measurements rather than by nonactive communications as done in the current literatures. By properly expanding the distance‐based network topology structure between each pair of UAVs, a distributed algorithm is proposed such that the switching topology remains connected along the time. Since a VTOL UAV system is typically underactuated, a hierarchical idea is introduced to derive the control design procedure. More specifically, the studied formation control problem of multiple VTOL UAVs is first transformed into the consensus problem of their corresponding error systems. Then, a command force and an applied torque are synthesized for each VTOL UAV such that the error systems reach a consensus. It is demonstrated in terms of Lyapunov theory that the proposed distributed hierarchical control algorithm guarantees the formation realization of multiple VTOL UAVs while maintaining the network connectivity. Finally, simulations are performed to validate the theoretical results.

Path planning for two unmanned aerial vehicles in passive localization of radio sources

This paper studies the trajectory control problem for a pair of unmanned aerial vehicles (UAVs) equipped with time of arrival (TOA) sensors to measure the time difference of arrival (TDOA) of the transmitted radio signal to localize the source. The extended Kalman filter (EKF) is applied to estimate the source's position. The proposed trajectory control strategy encompasses three optimum experimental design criteria based on the position error covariance produced by the EKF. The control strategy steers the UAVs to the positions to minimize the uncertainty about the location of the source. The effectiveness of the proposed approach is illustrated through simulation examples.

Packet Delay in UAV Wireless Networks Under Non-saturated Traffic and Channel Fading Conditions

In this paper, we conduct a statistical analysis for the packet delay in a wireless network of unmanned aerial vehicles (UAVs) under non-saturated traffic and channel fading conditions. Each UAV runs the distributed coordination function of IEEE 802.11 at the medium access control layer, and all UAVs are one-hop neighbors. A pair of UAVs can communicate over the lossy wireless channel of a fixed data rate. A non-saturated traffic condition is used. By modeling each node UAV as a standard queueing system (i.e., $$M/M/1$$ or $$M/G/1$$ queue), we derive the mean packet delay under the non-saturated traffic condition. Numerical and simulation results show that the mean packet delay derived based on $$M/M/1$$ queue is accurate for UAV wireless networks under the non-saturated traffic condition and with an independent packet error rate. It is observed that the mean packet delay increases with either the number of UAVs in the network or the packet generation rate. More important, existing results in the literature, based on the saturated traffic condition (i.e., packets are always supplied for transmission), tend to overestimate by a large amount the mean packet delay for networks with non-saturated traffic. In the second part of this paper, we apply simulation data to analysis of the probability distribution function of the packet delay when the packet error rate equals zero. Using a distribution fitting tool, we observe that the packet delay can be well approximated by the sum of a deterministic delay, which corresponds to the time period during which the UAV senses the medium and is able to perform a successful transmission, and a random delay, which follows a Gamma distribution function.

Nonlinear Model Predictive Coordinated Standoff Tracking of a Moving Ground Vehicle

This paper proposes a nonlinear model-predictive control framework for coordinated standoff tracking by a pair of unmanned aerial vehicles. The benefit of this approach is to get optimal performance compared with using a decoupled controller structure: heading control for standoff-distance keeping and speed control for phase keeping. The overall controller structure is fully decentralized as each unmanned aerial vehicle optimizes its controller based solely on the future propagation of the pair vehicle states and the target estimates received via communication. This paper uses an acceleration model for sophisticated and realistic target dynamics, which can consider a more reasonable system noise covariance matrix reflecting the target’s motion characteristics. To simplify optimization formulation and decrease computation burden, a new manipulation using the inner product of position vectors of the unmanned aerial vehicles with respect to the target position is proposed for antipodal tracking instead of using the relative phase-angle difference. To consider a more realistic situation, inequality constraints are considered for collision avoidance between unmanned aerial vehicles and control input saturations using penalty functions in the model-predictive control scheme. Simulations with a pair of unmanned aerial vehicles are done using realistic car trajectory data in an urban environment in the United Kingdom to verify the feasibility and benefit of the proposed approach with comparing to a Lyapunov vector field guidance.

Comparison of Recursive Algorithms for Emitter Localisation using TDOA Measurements from a Pair of UAVs

This paper presents a comparative analysis of three nonlinear filters for estimation of the location and velocity of a moving emitter, using time difference of arrival (TDOA) measurements received by two unmanned aerial vehicles (UAVs) as they traverse the surveillance region. The TDOA measurements are generated over time by comparing and subtracting leading edge time of arrivals (TOAs) of signals. The Cramer Rao lower bound (CRLB) of estimation errors is derived and used as the benchmark in performance analysis. The three nonlinear filters considered in the comparison are: a Gaussian mixture measurement integrated track splitting filter (GMM-ITSF), a multiple model filter with unscented Kalman filters (UKFs) and a multiple-model filter with extended Kalman filters (EKFs).

Mapping and Tracking

This article presents the implementation of decentralized data fusion (DDF) and cooperative control algorithms on an unmanned aerial system (UAS).We conduct a number of demonstrations with a pair of unmanned aerial vehicles (UAVs) performing an information-gathering mission, and we show that significant benefits can be achieved by enabling cooperation through the sharing of information between members of the team. The objective is to utilize the UAV team to estimate the position and velocity of a number of ground-based features. The UAVs are given some prior knowledge of the feature states and are required to gather further information above a predefined threshold. This situation models a scenario where initial information is made available from an external source (e.g., a high-flying UAV or satellite imagery), which then prompts the start of the feature-localizationmission.