Autonomous Visual Navigation of a Quadrotor VTOL in complex and dense environments

This thesis presents a system design of a micro aerial vehicle platform, specifically a quadrotor, that is aimed at autonomous vision-based reactive obstacle avoidance in dense and complex environments. Most modern aerial system are incapable of autonomously navigating in environments with a high density of trees and bushes. The presented quadrotor design uses leading-edge technologies and inexpensive off-the-shelf components to build a system that presents a step forward in technologies aimed at overcoming the issues with dense and complex environments. Several major system requirements were met to make the design effective and safe. It had to be completely autonomous in standard operations and have a manual override function. It had to have its computational capability completely on-board along with vision processing ability. As such, all state estimation and visual guidance had to be performed on-board the vehicle, removing the need for remote connection which can easily fail in forest-like environments. The quadrotor had to be made from mostly off-the-shelf components to reduce cost and make it replicable. It also had to remain under 2kg to meet Australian commercial aerial vehicle regulations regarding licencing. In order to meet the system requirements, many design decisions were developed and altered as needed. The main body of the quadrotor platform was based on off-the-shelf hobby assemblies. A Pixhawk 2.1 was the flight controller used due to its open-source code and design which included all sensors needed for state estimation, has manual override for control, and control the motors. A leading-edge computational device called the NVIDIA Tegra TX2 was used for vision processing on the quadrotor. The NVIDIA Tegra TX2's embedded NVIDIA Graphics Processing Unit (GPU), is compact and consumes low amounts of power. It also is capable of estimating dense optical flow on the GPU at rates of 120Hz when using a camera that outputs grey-scale images at a resolution of 376x240. The vision processor is responsible for providing directional guidance to the on-board flight controller. A design decision during the project was to include a 3-axis gimbal to stabilise the camera. The quadrotor was shown to be able to hover and locally move both indoors and outdoors using the optical flow measurements. Optical flow measurements give a sense of velocity which can be integrated to get a position estimate, though it was susceptible to drift. The drift was compensated using a combination of recognisable targets and positioning systems such as GPS. The experimental data obtained during the project showed that the algorithms presented in this thesis are capable of performing reactive obstacle avoidance. The reactive obstacle avoidance experiments were performed in both simulation and in real world environments, including the dense forest-like environments. By fusing vehicle speed estimates with optical flow measurements, visible points in 3D space can have their distance estimated relative to the quadrotor. By projecting a 3D cylinder in the direction of travel onto the camera plane, the system can perform reactive obstacle avoidance by steering the cylinder (direction of travel) to a point with minimal interference. This system is intended to augment a point to point navigation system such that the quadrotor responds to fine obstacle that may have otherwise not been detected

Development of Intelligent Unmanned Aerial Vehicles with Effective Sense and Avoid Capabilities

Ph.DDOCTOR OF PHILOSOPH

Trajectory optimization and motion planning for quadrotors in unstructured environments

Trajectory optimization and motion planning for quadrotors in unstructured environments Coming out from university labs robots perform tasks usually navigating through unstructured environment. The realization of autonomous motion in such type of environments poses a number of challenges compared to highly controlled laboratory spaces. In unstructured environments robots cannot rely on complete knowledge of their sorroundings and they have to continously acquire information for decision making. The challenges presented are a consequence of the high-dimensionality of the state-space and of the uncertainty introduced by modeling and perception. This is even more true for aerial-robots that has a complex nonlinear dynamics a can move freely in 3D-space. To avoid this complexity a robot have to select a small set of relevant features, reason on a reduced state space and plan trajectories on short-time horizon. This thesis is a contribution towards the autonomous navigation of aerial robots (quadrotors) in real-world unstructured scenarios. The first three chapters present a contribution towards an implementation of Receding Time Horizon Optimal Control. The optimization problem for a model based trajectory generation in environments with obstacles is set, using an approach based on variational calculus and modeling the robots in the SE(3) Lie Group of 3D space transformations. The fourth chapter explores the problem of using minimal information and sensing to generate motion towards a goal in an indoor bulding-like scenario. The fifth chapter investigate the problem of extracting visual features from the environment to control the motion in an indoor corridor-like scenario. The last chapter deals with the problem of spatial reasoning and motion planning using atomic proposition in a multi-robot environments with obstacles

Towards an autonomous vision-based unmanned aerial system againstwildlife poachers

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.Peer Reviewe

Towards an autonomous vision-based unmanned aerial system against wildlife poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly $ 213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing

Vision-based control and autonomous landing of a VTOL-UAV

In recent years the popularity of quadrotor unmanned aerial vehicles (UAVs) has increased. Today, UAVs are widely used by military and police forces for surveillance. They are used by industry for such tasks as traffic monitoring, infrastructure inspection or even delivery of goods. They are used by individuals for hobby flying and aerial photography. It is currently of great interest in the research community to improve the level of autonomy of the UAV for these and future uses. One particular problem is the ability to stabilize over and land on a moving platform. This situation can easily arise for a quadrotor returning to a ship at sea or even a landing pad affixed to a vehicle. Many current techniques rely on knowledge of the platform and its motion, or a predictive model. This information is not always available or accurate. A solution that does not require knowledge of the target is desirable. This thesis deals with practical implementation of optical flow based position stabilization and autonomous landing algorithms for a quadrotor UAV. The quadrotor used is a common low cost platform with a large open source community. Firstly, non-linear estimation and control techniques are implemented for the attitude stabilization using low-cost sensors and limited computational power. Some methods for the system parameters estimation are presented and some challenges related to the implementation are discussed. Despite the ability of the attitude controller to stabilize the orientation of the quadrotor, hovering and landing precisely over a specific area is not possible without a position stabilization scheme. In applications where GPS signals are not available and the hovering target is a priori unknown, it is common to rely on visual information. In this context, this thesis aims for the development of an efficient optical-flow-based position stabilization and autonomous landing scheme for the quadrotor UAV

Robust hovering and trajectory tracking control of a quadrotor helicopter using acceleration feedback and a novel disturbance observer

Hovering and trajectory tracking control of rotary-wing aircrafts in the presence of uncertainties and external disturbances is a very challenging task. This thesis focuses on the development of the robust hovering and trajectory tracking control algorithms for a quadrotor helicopter subject to both periodic and aperiodic disturbances along with noise and parametric uncertainties. A hierarchical control structure is employed where high-level position controllers produce reference attitude angles for the low-level attitude controllers. Reference attitude angles are usually determined analytically from the position command signals that control the positional dynamics. However, such analytical formulas may produce large and non-smooth reference angles which must be saturated and low-pass filtered. In this thesis, desired attitude angles are determined numerically using constrained nonlinear optimization where certain magnitude and rate constraints are imposed. Furthermore, an acceleration based disturbance observer (AbDOB) is designed to estimate and suppress disturbances acting on the positional dynamics of the quadrotor. For the attitude control, a nested position, velocity, and inner acceleration feedback control structure consisting of PID and PI type controllers are developed to provide high sti ness against external disturbances. Reliable angular acceleration is estimated through an extended Kalman filter (EKF) cascaded with a classical Kalman lter (KF). This thesis also proposes a novel disturbance observer which consists of a bank of band-pass filters connected parallel to the low-pass filter of a classical disturbance observer. Band-pass filters are centered at integer multiples of the fundamental frequency of the periodic disturbance. Number and bandwidth of the band-pass filters are two crucial parameters to be tuned in the implementation of the new structure. Proposed disturbance observer is integrated with a sliding mode controller to tackle the robust hovering and trajectory tracking control problem. The sensitivity of the proposed disturbance observer based control system to the number and bandwidth of the band-pass filters are thoroughly investigated via several simulations. Simulations are carried out on a high delity model where sensor biases and measurement noise are also considered. Results show that the proposed controllers are very effective in providing robust hovering and trajectory tracking performance when the quadrotor helicopter is subject to the wind gusts generated by the Dryden wind model along with plant uncertainties and measurement noise. A comparison with the classical disturbance observer-based control is also provided where better tracking performance with improved robustness is achieved in the presence of noise and external disturbance

Nonlinear Model Predictive Control for Multi-Micro Aerial Vehicle Robust Collision Avoidance

Multiple multirotor Micro Aerial Vehicles sharing the same airspace require a reliable and robust collision avoidance technique. In this paper we address the problem of multi-MAV reactive collision avoidance. A model-based controller is employed to achieve simultaneously reference trajectory tracking and collision avoidance. Moreover, we also account for the uncertainty of the state estimator and the other agents position and velocity uncertainties to achieve a higher degree of robustness. The proposed approach is decentralized, does not require collision-free reference trajectory and accounts for the full MAV dynamics. We validated our approach in simulation and experimentally.Comment: Video available on: https://www.youtube.com/watch?v=Ot76i9p2ZZo&t=40

Development of Cross Style Quadrotor

Master'sMASTER OF ENGINEERIN