The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains, including real-time monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in the UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate more than $45 Billion market value of UAV usage. In this paper, we present UAV civil applications and their challenges. We also discuss the current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including charging challenges, collision avoidance and swarming challenges, and networking and security-related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges

With the rapidly evolving technology of the smart grid and electric vehicles (EVs), the battery has emerged as the most prominent energy storage device, attracting a significant amount of attention. The very recent discussions about the performance of lithium-ion (Li-ion) batteries in the Boeing 787 have confirmed so far that, while battery technology is growing very quickly, developing cells with higher power and energy densities, it is equally important to improve the performance of the battery management system (BMS) to make the battery a safe, reliable, and cost-efficient solution. The specific characteristics and needs of the smart grid and EVs, such as deep charge/discharge protection and accurate state-of-charge (SOC) and state-of-health (SOH) estimation, intensify the need for a more efficient BMS. The BMS should contain accurate algorithms to measure and estimate the functional status of the battery and, at the same time, be equipped with state-of-the-art mechanisms to protect the battery from hazardous and inefficient operating conditions.

Battery Management System: An Overview of Its Application in the Smart Grid and Electric Vehicles

For agricultural applications, regularized smart-farming solutions are being considered, including the use of unmanned aerial vehicles (UAVs). The UAVs combine information and communication technologies, robots, artificial intelligence, big data, and the Internet of Things. The agricultural UAVs are highly capable, and their use has expanded across all areas of agriculture, including pesticide and fertilizer spraying, seed sowing, and growth assessment and mapping. Accordingly, the market for agricultural UAVs is expected to continue growing with the related technologies. In this study, we consider the latest trends and applications of leading technologies related to agricultural UAVs, control technologies, equipment, and development. We discuss the use of UAVs in real agricultural environments. Furthermore, the future development of the agricultural UAVs and their challenges are presented.

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Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications

Particle filter-based prognostics: Review, discussion and perspectives

A critical review on unmanned aerial vehicles power supply and energy management: Solutions, strategies, and prospects

This paper presents a novel battery health management system for electric UAVs (unmanned aerial vehicles) based on a Bayesian inference driven prognostic framework. The aim is to be able to predict the end-of-discharge (EOD) event that indicates that the battery pack has run out of charge for any given flight of an electric UAV platform. The amount of usable charge of a battery for a given discharge profile is not only dependent on the starting state-of-charge (SOC), but also other factors like battery health and the discharge or load profile imposed. This problem is more pronounced in battery powered electric UAVs since different flight regimes like takeoff/landing and cruise have different power requirements and a dead stick condition (battery shut off in flight) can have catastrophic consequences. Since UAVs deployments are relatively new, there is a lack of statistically significant flight data to motivate data-driven approaches. Consequently, we have developed a detailed discharge model for the batteries used and used it in a Bayesian inference based filtering (Particle Filtering) technique to generate remaining useful life (RUL) distributions for a given discharge. The results section presents the validation of this approach in hardware-in-the-loop tests.12

Battery health management system for electric UAVs

This paper addresses the problem of building trust in online predictions of a battery powered aircraft's remaining available flying time. A set of ground tests is described that make use of a small unmanned aerial vehicle to verify the performance of remaining flying time predictions. The algorithm verification procedure described here uses a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected in flight. The fully integrated aircraft is repeatedly operated until the charge stored in powertrain batteries falls below a specified lower-limit. The time at which the lower-limit on battery charge is crossed is then used to measure the accuracy of remaining flying time predictions. Accuracy requirements are considered in this paper for an alarm that warns operators when remaining flying time is estimated to fall below a specified threshold.

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Verification of a Remaining Flying Time Prediction System for Small Electric Aircraft

The Linear Autoland Simulink model was created to be a modular test environment for testing of control system components in commercial aircraft. The input variables, physical laws, and referenced frames used are summarized. The state space theory underlying the model is surveyed and the location of the control actuators described. The equations used to realize the Dryden gust model to simulate winds and gusts are derived. A description of the pseudo-random number generation method used in the wind gust model is included. The longitudinal autopilot, lateral autopilot, automatic throttle autopilot, engine model and automatic trim devices are considered as subsystems. The experience in converting the Airlabs FORTRAN aircraft control system simulation to a graphical simulation tool (Matlab/Simulink) is described.

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B-737 Linear Autoland Simulink Model

Software-in-the-loop and Hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The creation of an offline framework for verifying and validating supervisory failure prognostics and decision making routines is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.

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SILHIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

Unmanned Aerial Vehicles (UAV) are playing an increasing role in aviation. Various methods exist for the computation of UAV attitude based on low cost microelectromechanical systems (MEMS) and Global Positioning System (GPS) receivers. There has been a recent increase in UAV autonomy as sensors are becoming more compact and onboard processing power has increased significantly. Correct UAV attitude estimation will play a critical role in navigation and separation assurance as UAVs share airspace with civil air traffic. This paper describes attitude estimation derived by post-processing data from a small low cost Inertial Navigation System (INS) recorded during the flight of a subscale commercial off the shelf (COTS) UAV. Two discrete time attitude estimation schemes are presented here in detail. The first is an adaptation of the Kalman Filter to accommodate nonlinear systems, the Extended Kalman Filter (EKF). The EKF returns quaternion estimates of the UAV attitude based on MEMS gyro, magnetometer, accelerometer, and pitot tube inputs. The second scheme is the complementary filter which is a simpler algorithm that splits the sensor frequency spectrum based on noise characteristics. The necessity to correct both filters for gravity measurement errors during turning maneuvers is demonstrated. It is shown that the proposed algorithms may be used to estimate UAV attitude. The effects of vibration on sensor measurements are discussed. Heuristic tuning comments pertaining to sensor filtering and gain selection to achieve acceptable performance during flight are given. Comparisons of attitude estimation performance are made between the EKF and the complementary filter.

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Edward F. Hogge

Papers

Battery health management system for electric UAVs

Verification of a Remaining Flying Time Prediction System for Small Electric Aircraft

B-737 Linear Autoland Simulink Model

SILHIL Replication of Electric Aircraft Powertrain Dynamics and Inner-Loop Control for V&V of System Health Management Routines

An Application of UAV Attitude Estimation Using a Low-Cost Inertial Navigation System