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

Worldwide demand for robotic aircraft such as unmanned aerial vehicles (UAVs) and micro aerial vehicles (MAVs) is surging. Not only military but especially civil applications are being developed at a rapid pace. Unmanned vehicles offer major advantages when used for aerial surveillance, reconnaissance, and inspection in complex and inhospitable environments. UAVs are better suited for dirty or dangerous missions than manned aircraft and are more cost-effective. UAVs can operate in contaminated environments, for example, and at altitudes both lower and higher than those typically traversed by manned aircraft. Many technological, economic, and political factors have encouraged the development and operation of UAVs. New sensors, microprocessors, and propulsion systems are smaller, lighter, and more capable, leading to levels of endurance, efficiency, and autonomy that exceed human capacities. Comprising the latest research, this book describes step by step the development of small or miniature unmanned aerial vehicles and discusses in detail the integrated prototypes developed at the robotics laboratory of Chiba University. With demonstration videos, the book will interest not only graduate students, scientists, and engineers but also newcomers to the field.

Autonomous Flying Robots: Unmanned Aerial Vehicles and Micro Aerial Vehicles

This paper proposes a vision-based algorithm to autonomously track and chase a moving target with a small-size flying UAV. The challenging constraints associated with the UAV flight led us to consider a density-based representation of the object to track. The proposed approach to estimate the target's position, orientation and scale, is built on a robust color-based tracker using a multi-part representation. This object tracker can handle large displacements, occlusions and account for some image noise due to partial loss of wireless video link, thanks to the use of a particle filter. The information obtained from the visual tracker is then used to control the position and yaw angle of the UAV in order to chase the target. A hierarchical control scheme is designed to achieve the tracking task. Experiments on a quad-rotor UAV following a small moving car are provided to validate the proposed approach.

https://hal.inria.fr/hal-00639702/document

Chasing a moving target from a flying UAV

In this study, we focus on the problem of landing an unmanned aerial vehicle (UAV) in unknown and Global Navigation Satellite System(GNSS)-denied environments based on an infrared stereo vision system. This system is fixed on the ground and used to track the UAV's position during the landing process. In order to enlarge the search field of view (FOV), a pan-tilt unit (PTU) is employed to actuate the vision system. The infrared camera is chosen as the exteroceptive sensor for two main reasons: first, it can be used under all weather conditions and around the clock; second, infrared targets can be tracked based on infrared spectrum features at a lower computational cost compared to tracking texture features in visible spectrum. State-of-the-art active contour based algorithms and the mean shift algorithm have been evaluated with regard to detecting and tracking an infrared target. Field experiments have been carried out using an unmanned quadrotor and a fixed-wing unmanned aircraft, with both qualitative and quantitative evaluations. The results demonstrate that our system can track UAVs without artificial markers and is sufficient to enhance or replace the GNSS-based localization in GNSS-denied environment or where its information is inaccurate.

/pdf/autonomous-landing-of-an-uav-with-a-ground-based-actuated-3oszr5jbci.pdf

Autonomous landing of an UAV with a ground-based actuated infrared stereo vision system

Outdoor field measurements of bioaerosols are performed within a wide range of basic and applied scientific disciplines, each with its own goals, assumptions, and terminology. This paper contains brief reviews of outdoor field bioaerosol research from these diverse interests, with emphasis on perspectives from the atmospheric sciences. The focus is on a high level discussion of pressing scientific questions, grand challenges, and needs for cross-disciplinary collaboration. The research topics, in which bioaerosol field measurement are important, include (i) atmospheric physics, clouds, climate, and hydrological cycle; (ii) atmospheric chemistry; (iii) airborne allergen-containing particles; (iv) airborne human pathogens and national security; (v) airborne livestock and crop pathogens; and (vi) biogeography and biodiversity. We concisely review bioaerosol impacts and discuss properties that distinguish bioaerosols from abiological aerosols. We give extra focus to regions of specific interest, i.e. forests, polar regions, marine and coastal environments, deserts, urban and rural areas, and summarize key considerations related to bioaerosol measurements, such as of fluxes, long-range transport, and from both stationary and vessel-driven platforms. Keeping in mind a series of key scientific questions posed within the diverse communities, we suggest that pressing scientific questions include: (i) emission sources and flux estimates; (ii) spatial distribution; (iii) changes in distribution; (iv) atmospheric aging; (v) metabolic activity; (vi) urbanization of allergies; (vii) transport of human pathogens; and (viii) climate-relevant properties.

https://hal.archives-ouvertes.fr/hal-02319383/document

Bioaerosol field measurements: Challenges and perspectives in outdoor studies

This paper describes a vision-based tracking system using an autonomous Quadrotor Unmanned Micro-Aerial Vehicle (MAV). The vision-based control system relies on color target detection and tracking algorithm using integral image, Kalman filters for relative pose estimation, and a nonlinear controller for the MAV stabilization and guidance. The vision algorithm relies on information from a single onboard camera. An arbitrary target can be selected in real-time from the ground control station, thereby outperforming template and learning-based approaches. Experimental results obtained from outdoor flight tests, showed that the vision-control system enabled the MAV to track and hover above the target as long as the battery is available. The target does not need to be pre-learned, or a template for detection. The results from image processing are sent to navigate a non-linear controller designed for the MAV by the researchers in our group.

Visual Servoing of Quadrotor Micro-Air Vehicle Using Color-Based Tracking Algorithm

On ground stereo vision system is used for autonomous hovering and landing of a quadrotor Micro Aerial Vehicle (MAV). This kind of system has an advantage to support embedded vision system for autonomous hovering and landing, since an embedded vision system occasionally gives inaccurate distance calculation due to either vibration problem or unknown geometry of the landing target. Color based object tracking by using Continuously Adaptive Mean Shift (CAMSHIFT) algorithm was examined. Nonlinear model of quad-rotor MAV and a PID controller were used for autonomous hovering and landing. The result shows that the Camshift based object tracking algorithm has good performance. Additionally, the comparison between the stereo vision system based and GPS based autonomous hovering of a quad-rotor MAV shows that stereo vision system has better performance. The accuracy of the stereo vision system is about 1 meter in the longitudinal and lateral direction when the quad-rotor flies in 6 meters of altitude. In the same experimental condition, the GPS based system accuracy is about 3 meters. Additionally, experiment on autonomous landing gives a reliable result.

/pdf/autonomous-hovering-and-landing-of-a-quad-rotor-micro-aerial-133gwjmaxi.pdf

Autonomous Hovering and Landing of a Quad-rotor Micro Aerial Vehicle by Means of on Ground Stereo Vision System

This paper describes an object tracking system using an autonomous Micro Air Vehicle (MAV) and demonstrate its potential use for civilian purposes. The vision-based control system relies on a color and feature based vision algorithm for target detection and tracking, Kalman filters for relative pose estimation, and a nonlinear controller for MAV stabilization and guidance. The vision algorithm relies on information from a single onboard camera. An arbitrary target can be selected in real-time from the ground control station, thereby outperforming template and learning-based approaches. Experimental results obtained from outdoor flight tests, showed that the vision-control system enabled the MAV to track and hover above the target as long as the battery is available.

Visual servoing of an autonomous Micro Air Vehicle for ground object tracking

This paper presents the development of a quadrotor unmanned aerial vehicle (UAV) that is capable of detecting and avoiding collision with obstacles through the implementation of Kalman filter-based multi-sensor fusion and cascaded PID position and velocity controllers. Sensor fusion of ultrasonic (US) and infrared (IR) sensors is performed to obtain a reliable range data for obstacle detection which then fed into collision avoidance controller (CAC) for generating necessary response in terms of attitude commands. Results showed that sensor fusion provided accurate range estimation by reducing noises and errors that were present in individual sensors measurements. Flight tests performed proved the capability of UAV to avoid collisions with the obstacle that was introduced to it during flight successfully.

Syaril Azrad

Papers

Visual Servoing of Quadrotor Micro-Air Vehicle Using Color-Based Tracking Algorithm

Autonomous Hovering and Landing of a Quad-rotor Micro Aerial Vehicle by Means of on Ground Stereo Vision System

Visual servoing of an autonomous Micro Air Vehicle for ground object tracking

Multi-sensor fusion based uav collision avoidance system

Classification of oil palm female inflorescences anthesis stages using machine learning approaches