Recently, there has been growing interest in developing unmanned aircraft systems (UAS) with advanced onboard autonomous capabilities. This paper describes the current state of the art in autonomous rotorcraft UAS (RUAS) and provides a detailed literature review of the last two decades of active research on RUAS. Three functional technology areas are identified as the core components of an autonomous RUAS. Guidance, navigation, and control (GNC) have received much attention from the research community, and have dominated the UAS literature from the nineties until now. This paper first presents the main research groups involved in the development of GNC systems for RUAS. Then it describes the development of a framework that provides standard definitions and metrics characterizing and measuring the autonomy level of a RUAS using GNC aspects. This framework is intended to facilitate the understanding and the organization of this survey paper, but it can also serve as a common reference for the UAS community. The main objective of this paper is to present a comprehensive survey of RUAS research that captures all seminal works and milestones in each GNC area, with a particular focus on practical methods and technologies that have been demonstrated in flight tests. These algorithms and systems have been classified into different categories and classes based on the autonomy level they provide and the algorithmic approach used. Finally, the paper discusses the RUAS literature in general and highlights challenges that need to be addressed in developing autonomous systems for unmanned rotorcraft. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

Survey of advances in guidance, navigation, and control of unmanned rotorcraft systems

Despite the perception people may have regarding the agricultural process, the reality is that today's agriculture industry is data-centered, precise, and smarter than ever. The rapid emergence of the Internet-of-Things (IoT) based technologies redesigned almost every industry including “smart agriculture” which moved the industry from statistical to quantitative approaches. Such revolutionary changes are shaking the existing agriculture methods and creating new opportunities along a range of challenges. This article highlights the potential of wireless sensors and IoT in agriculture, as well as the challenges expected to be faced when integrating this technology with the traditional farming practices. IoT devices and communication techniques associated with wireless sensors encountered in agriculture applications are analyzed in detail. What sensors are available for specific agriculture application, like soil preparation, crop status, irrigation, insect and pest detection are listed. How this technology helping the growers throughout the crop stages, from sowing until harvesting, packing and transportation is explained. Furthermore, the use of unmanned aerial vehicles for crop surveillance and other favorable applications such as optimizing crop yield is considered in this article. State-of-the-art IoT-based architectures and platforms used in agriculture are also highlighted wherever suitable. Finally, based on this thorough review, we identify current and future trends of IoT in agriculture and highlight potential research challenges.

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Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk

Emerging technologies such as Internet of Things (IoT) can provide significant potential in Smart Farming and Precision Agriculture applications, enabling the acquisition of real-time environmental data. IoT devices such as Unmanned Aerial Vehicles (UAVs) can be exploited in a variety of applications related to crops management, by capturing high spatial and temporal resolution images. These technologies are expected to revolutionize agriculture, enabling decision-making in days instead of weeks, promising significant reduction in cost and increase in the yield. Such decisions enable the effective application of farm inputs, supporting the four pillars of precision agriculture, i.e., apply the right practice, at the right place, at the right time and with the right quantity. However, the actual proliferation and exploitation of UAVs in Smart Farming has not been as robust as expected mainly due to the challenges confronted when selecting and deploying the relevant technologies, including the data acquisition and image processing methods. The main problem is that still there is no standardized workflow for the use of UAVs in such applications, as it is a relatively new area. In this article, we review the most recent applications of UAVs for Precision Agriculture. We discuss the most common applications, the types of UAVs exploited and then we focus on the data acquisition methods and technologies, appointing the benefits and drawbacks of each one. We also point out the most popular processing methods of aerial imagery and discuss the outcomes of each method and the potential applications of each one in the farming operations.

https://www.mdpi.com/2078-2489/10/11/349/pdf

A Review on UAV-Based Applications for Precision Agriculture

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

Remote sensing has traditionally be done with satellites and manned aircraft. While these methods can yield useful scientific data, satellites and manned aircraft have limitations in data frequency, process time, and real time re-tasking. Small low-cost unmanned aerial vehicles (UAVs) can bridge the gap for personal remote sensing for scientific data. Precision aerial imagery and sensor data requires an accurate dynamics model of the vehicle for controller development. One method of developing a dynamics model is system identification (system ID). The purpose of this paper is to provide a survey and categorization of current methods and applications of system ID for small low-cost UAVs. This paper also provides background information on the process of system ID with in-depth discussion on practical implementation for UAVs. This survey divides the summaries of system ID research into five UAV groups: helicopter, fixed-wing, multirotor, flapping-wing, and lighter-than-air. The research literature is tabulated into five corresponding UAV groups for further research.

A Survey and Categorization of Small Low-Cost Unmanned Aerial Vehicle System Identification

Flight-Testing of a UAV Aircraft for Autonomous Operation using Piccolo II Autopilot

This paper presents the continued research on UAV dynamic modeling at the Aerospace Engineering Department of the University of Kansas using a Thunder Tiger Raptor 50 V2 remote control (RC) helicopter. Previously, an uncoupled three-degree-of-freedom (3-DoF) linear parameter varying (LPV) dynamic model based on stability and control derivatives was presented. The response obtained from this model showed a high degree of correlation with the actual flight test data. The previous paper also highlighted the flight test instrumentation and data collection procedures. As a result of the on-going research on the dynamic modeling of the UAV helicopter, a complete six-degree-of-freedom (6-DoF) LPV model has been developed in the MathWork’s MATLAB and Simulink environment. An effort has been made to take into account the aerodynamic and inertial coupling between the longitudinal and lateral dynamics of the helicopter. Validation of the model was accomplished by comparing the results of the simulation with flight test data along with the data obtained from CIFER (Comprehensive Identification from FrEquency Response) model. The 6-DoF model showed a good correlation between actual flight test data and CIFER.

Six-DoF Dynamic Modeling and Flight Testing of a UAV Helicopter

6-DoF Dynamic Model for a Raptor 50 UAV Helicopter Including Stabilizer Bar Dynamics

This paper presents the work being done at Cal Poly Pomona on using UAVs for search and rescue missions. A Sig Kadet Senior UAV airplane has been equipped with an autopilot for autonomous waypoint operation and a camera for target identification. OpenCV software was used for image processing, segmentation, and for identifying objects of interest. In order to provide targeted aerial assistance, an autonomous payload delivery algorithm was developed. A flight management computer and drop mechanism has been integrated into the UAV. A payload delivery algorithm was programmed onto the flight management computer and linked to the drop mechanism. The flight management computer received telemetry from the autopilot as inputs to the payload drop algorithm to determine the position of the target relative to the aircraft. The algorithm calculates the release point before triggering a payload release.

Search and Rescue using Unmanned Aerial Vehicles

This paper presents the use of stereoscopic vision as a means of sensing and detecting obstacles and other aircraft for collision avoidance system for a multicopter. A ZED stereo camera is mounted on a DJI S900 Hexacopter UAS to generate depth maps. A NVIDIA Jetson TX1 board is used for onboard processing of the depth maps and collision avoidance algorithm. The board communicates with the Pixhawk autopilot, which transmits data to the ground control station via XBee radios. By using the ZED software development kit (SDK), it is possible to obtain depth maps directly from the camera and use them in the implementation of obstacle avoidance. The algorithm that is used partitions the depth maps into multiple sections in order to find the section of the image that represents the objects furthest away. This selected section is the section that is free of obstacles. The hexacopter can maneuver itself to that selected section autonomously, thus avoiding obstacles in its path. The developed algorithm and flight test results are discussed.

Subodh Bhandari

Papers

Flight-Testing of a UAV Aircraft for Autonomous Operation using Piccolo II Autopilot

Six-DoF Dynamic Modeling and Flight Testing of a UAV Helicopter

6-DoF Dynamic Model for a Raptor 50 UAV Helicopter Including Stabilizer Bar Dynamics

Search and Rescue using Unmanned Aerial Vehicles

Autonomous Collision Avoidance System for a Multicopter using Stereoscopic Vision