This thesis presents an implementation of autonomous indoor perching using only onboard sensors on a low-cost, custom-built quadrotor. The perching aggressive maneuver is representative of a class of control problems for aerobatics that requires an agile and robust control system for maneuvering accurately at high speeds. Such research extends the typical functionality of micro air vehicles (MAV) from low speed and stationary observation to dynamic aerobatic transitions for broader operational capabilities including confined landings and evasive maneuvering. To achieve this, three major challenges are overcome: precise and real-time positioning, sensing of the perch and path to the perch, and control methods for robust and accurate tracking at high speeds. Navigation in unstructured, global positioning system (GPS)-denied environments is achieved using a visual Simultaneous Localization and Mapping (SLAM) algorithm that relies on an onboard monocular camera. A secondary camera, capable of detecting infrared light sources, is used to locate the pathway for the maneuver and the perch, simulating sensing of the actual perch, for perching without prior knowledge of the location of the perch. The full physical system architecture is covered in detail, indicating the components and integration necessary to obtain effective aggressive control of an inexpensive quadrotor. The difficulties of attitude stabilization on noisy and lower-quality sensors are successfully addressed so that the air vehicle can be treated as a simple second-order system for the purposes of navigation and response to dynamic maneuvering commands. The system utilizes nested controllers for attitude stabilization, vision-based navigation, and perching guidance, with the navigation controller implemented using novel nonlinear saturation control within a Proportional-Integral-Derivative (PID) structure. The quadrotor is therefore able to autonomously sense the perch, reach initial high speeds for obtaining rapid deceleration from aerodynamic effects, dynamically transition to a high angle of attack post-stall configuration, and make a low-speed accurate landing on an inclined surface, using only onboard sensors.

The use of unmanned systems is becoming widespread in commercial and military sectors. The ability of these systems to take on dull, dirty, and dangerous tasks which were formerly done by humans is encouraging their rapid adoption. In particular, a subset of these undesirable tasks are uniquely suited for small unmanned aerial vehicles such as quadrotor helicopters. Examples of such tasks include surveillance, mapping, and search and rescue. Many of these potential tasks require quadrotors to be deployed in environments where a degree of position estimation is required and traditional GPS-based positioning technologies are not applicable. Likewise, since unmanned systems in these environments are often intended to serve the purpose of scouts or first--responders, no maps or reference beacons will be available. Additionally, there is no guarantee of clear features within the environment which an onboard sensor suite (typically made up of a monocular camera and inertial sensors) will be able to track to maintain an estimate of vehicle position. Up to 90% of the features detected in the environment may produce motion estimates which are inconsistent with the true vehicle motion. Thus, new methods are needed to compensate for these environmental deficiencies and measurement inconsistencies. In this work, a RANSAC-based outlier rejection technique is combined with an Extended Kalman Filter (EKF) to generate estimates of vehicle position in a 2--D plane. A low complexity feature selection technique is used in place of more modern techniques in order to further reduce processor load. The overall algorithm was faster than the traditional approach by a factor of 4. Outlier rejection allows the abundance of low quality, poorly tracked image features to be filtered appropriately, while the EKF allows a motion model of the quadrotor to be incorporated into the position estimate. The algorithm is tested in real-time on a quadrotor vehicle in an indoor environment with no clear features and found to be able to successfully estimate position of the vehicle to within 40 cm, superior to those produced when no outlier rejection technique was used. It is also found that the choice of simple feature selection approaches is valid, as complex feature selection approaches which may take over 10 times as long to run still result in outliers being present. When the algorithm is used for vehicle control, periodic synchronization to ground truth data was required due to nearly 1 second of latency present in the closed--loop system. However, the system as a whole is a valid proof of concept for the use of low quality images for quadrotor position control. The overall results from the work suggest that it is possible for unmanned systems to use visual data to estimate state even in operational environments which are poorly suited for visual estimation techniques. The filter algorithm described in this work can be seen as a useful tool for expanding the operational capabilities of small aerial vehicles.

Micro Umanned Aerial Vehicle~(UAV) and Umanned Ground Vehicle~(UGV) teams present tremendous opportunities in expanding the range of operations for these vehicles. An effective coordination of these vehicles can take advantage of the strengths of both, while mediate each other's weaknesses. In particular, a micro UAV typically has limited flight time due to its weak payload capacity. To take advantage of the mobility and sensor coverage of a micro UAV in long range, long duration surveillance mission, a UGV can act as a mobile station for recharging or battery swap, and the ability to perform autonomous docking is a prerequisite for such operations. This work presents an approach to coordinate an autonomous docking between a quadrotor UAV and a skid-steered UGV. A joint controller is designed to eliminate the relative position error between the vehicles. The controller is validated in simulations and successful landing is achieved in indoor environment, as well as outdoor settings with standard sensors and real disturbances. Another goal for this work is to improve the autonomy of UAV-UGV teams in positioning denied environments, a very common scenarios for many robotics applications. In such environments, Simultaneous Mapping and Localization~(SLAM) capability is the foundation for all autonomous operations. A successful SLAM algorithm generates maps for path planning and object recognition, while providing localization information for position tracking. This work proposes an SLAM algorithm that is capable of generating high fidelity surface model of the surrounding, while accurately estimating the camera pose in real-time. This algorithm improves on a clear deficiency of its predecessor in its ability to perform dense reconstruction without strict volume limitation, enabling practical deployment of this algorithm on robotic systems.

Thesis (M.S.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 47-49). Also available on the World Wide Web.

The ANGEL project (Aerial Network Guided Electronic Lookout) takes a systems engineering approach to the design, development, testing and implementation of a quadrotor unmanned aerial vehicle. Many current research endeavors into the field of quadrotors for use as unmanned vehicles do not utilize the broad systems approach to design and implementation. These other projects use pre-fabricated quadrotor platforms and a series of external sensors in a mock environment that is unfeasible for real world use. The ANGEL system was designed specifically for use in a combat theater where robustness and ease of control are paramount. A complete simulation model of the ANGEL system dynamics was developed and used to tune a custom controller in MATLAB and Simulink®. This controller was then implemented in hardware and paired with the necessary subsystems to complete the ANGEL platform. Preliminary tests show successful operation of the craft, although more development is required before it is deployed in field. A custom high-level controller for the craft was written with the intention that troops should be able to send commands to the platform without having a dedicated pilot. A second craft that exhibits detachable limbs for greatly enhanced transportation efficiency is also in development.

The use of unmanned systems is becoming widespread in commercial and military sectors. The ability of these systems to take on dull, dirty, and dangerous tasks which were formerly done by humans is encouraging their rapid adoption. In particular, a subset of these undesirable tasks are uniquely suited for small unmanned aerial vehicles such as quadrotor helicopters. Examples of such tasks include surveillance, mapping, and search and rescue. Many of these potential tasks require quadrotors to be deployed in environments where a degree of position estimation is required and traditional GPS-based positioning technologies are not applicable. Likewise, since unmanned systems in these environments are often intended to serve the purpose of scouts or first--responders, no maps or reference beacons will be available. Additionally, there is no guarantee of clear features within the environment which an onboard sensor suite (typically made up of a monocular camera and inertial sensors) will be able to track to maintain an estimate of vehicle position. Up to 90% of the features detected in the environment may produce motion estimates which are inconsistent with the true vehicle motion. Thus, new methods are needed to compensate for these environmental deficiencies and measurement inconsistencies. In this work, a RANSAC-based outlier rejection technique is combined with an Extended Kalman Filter (EKF) to generate estimates of vehicle position in a 2--D plane. A low complexity feature selection technique is used in place of more modern techniques in order to further reduce processor load. The overall algorithm was faster than the traditional approach by a factor of 4. Outlier rejection allows the abundance of low quality, poorly tracked image features to be filtered appropriately, while the EKF allows a motion model of the quadrotor to be incorporated into the position estimate. The algorithm is tested in real-time on a quadrotor vehicle in an indoor environment with no clear features and found to be able to successfully estimate position of the vehicle to within 40 cm, superior to those produced when no outlier rejection technique was used. It is also found that the choice of simple feature selection approaches is valid, as complex feature selection approaches which may take over 10 times as long to run still result in outliers being present. When the algorithm is used for vehicle control, periodic synchronization to ground truth data was required due to nearly 1 second of latency present in the closed--loop system. However, the system as a whole is a valid proof of concept for the use of low quality images for quadrotor position control. The overall results from the work suggest that it is possible for unmanned systems to use visual data to estimate state even in operational environments which are poorly suited for visual estimation techniques. The filter algorithm described in this work can be seen as a useful tool for expanding the operational capabilities of small aerial vehicles.

Micro Umanned Aerial Vehicle~(UAV) and Umanned Ground Vehicle~(UGV) teams present tremendous opportunities in expanding the range of operations for these vehicles. An effective coordination of these vehicles can take advantage of the strengths of both, while mediate each other's weaknesses. In particular, a micro UAV typically has limited flight time due to its weak payload capacity. To take advantage of the mobility and sensor coverage of a micro UAV in long range, long duration surveillance mission, a UGV can act as a mobile station for recharging or battery swap, and the ability to perform autonomous docking is a prerequisite for such operations. This work presents an approach to coordinate an autonomous docking between a quadrotor UAV and a skid-steered UGV. A joint controller is designed to eliminate the relative position error between the vehicles. The controller is validated in simulations and successful landing is achieved in indoor environment, as well as outdoor settings with standard sensors and real disturbances. Another goal for this work is to improve the autonomy of UAV-UGV teams in positioning denied environments, a very common scenarios for many robotics applications. In such environments, Simultaneous Mapping and Localization~(SLAM) capability is the foundation for all autonomous operations. A successful SLAM algorithm generates maps for path planning and object recognition, while providing localization information for position tracking. This work proposes an SLAM algorithm that is capable of generating high fidelity surface model of the surrounding, while accurately estimating the camera pose in real-time. This algorithm improves on a clear deficiency of its predecessor in its ability to perform dense reconstruction without strict volume limitation, enabling practical deployment of this algorithm on robotic systems.

abstract: There has been exciting progress in the area of Unmanned Aerial Vehicles (UAV) in the last decade, especially for quadrotors due to their nature of easy manipulation and simple structure. A lot of research has been done on achieving autonomous and robust control for quadrotors. Recently researchers have been utilizing linear temporal logic as mission specification language for robot motion planning due to its expressiveness and scalability. Several algorithms have been proposed to achieve autonomous temporal logic planning. Also, several frameworks are designed to compose those discrete planners and continuous controllers to make sure the actual trajectory also satisfies the mission specification. However, most of these works use first-order kinematic models which are not accurate when quadrotors fly at high speed and cannot fully utilize the potential of quadrotors. This thesis work describes a new design for a hierarchical hybrid controller that is based on a dynamic model and seeks to achieve better performance in terms of speed and accuracy compared with some previous works. Furthermore, the proposed hierarchical controller is making progress towards guaranteed satisfaction of mission specification expressed in Linear Temporal Logic for dynamic systems. An event-driven receding horizon planner is also utilized that aims at distributed and decentralized planning for large-scale navigation scenarios. The benefits of this approach will be demonstrated using simulations results. / Dissertation/Thesis / Masters Thesis Computer Science 2016

With numerous distinct advantages, quadrotors have found a wide range of applications, such as structural inspection, traffic control, search and rescue, agricultural surveillance, etc. To better serve applications in cluttered environment, quadrotors are further equipped with vision sensors to enhance their state sensing and environment perception capabilities. Moreover, visual information can also be used to guide the motion control of the quadrotor. This is referred to as visual servoing of quadrotor. In this thesis, we identify the challenging problems arising in the area of visual servoing of the quadrotor and propose effective control strategies to address these issues. The control objective considered in this thesis is to regulate the relative pose of the quadrotor to a ground target using a limited number of sensors, e.g., a monocular camera and an inertia measurement unit. The camera is attached underneath the center of the quadrotor and facing down. The ground target is a planar object consisting of multiple points. The image features are selected as image moments defined in a ``virtual image plane". These image features offer an image kinematics that is independent of the tilt motion of the quadrotor. This independence enables the separation of the high level visual servoing controller design from the low level attitude tracking control. A high-gain observer-based model predictive control (MPC) scheme is proposed in this thesis to address the image-based visual servoing of the quadrotor. The high-gain observer is designed to estimate the linear velocity of the quadrotor which is part of the system states. Due to a limited number of sensors on board, the linear velocity information is not directly measurable. The high-gain observer provides the estimates of the linear velocity and delivers them to the model predictive controller. On the other hand, the model predictive controller generates the desired thrust force and yaw rate to regulate the pose of the quadrotor relative to the ground target. By using the MPC controller, the tilt motion of the quadrotor can be effectively bounded so that the scene of the ground target is well maintained in the field of view of the camera. This requirement is referred to as visibility constraint. The satisfaction of visibility constraint is a prerequisite of visual servoing of the quadrotor. Simulation and experimental studies are performed to verify the effectiveness of the proposed control strategies. Moreover, image processing algorithms are developed to extract the image features from the captured images, as required by the experimental implementation. / Graduate / 2020-12-11

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