Design, Construction and Control of a Quadrotor Helicopter Using a New Multirate Technique

This thesis describes the design, development, analysis and control of an autonomous Quadrotor Uninhabited Aerial Vehicle (UAV) that is controlled using a novel approach for multirate sampled-data systems. This technique uses three feedback loops: one loop for attitude, another for velocity and a third loop for position, yielding a piece-wise affine system. Appropriate control actions are also computed at different rates. It is shown that this technique improve the system's stability under sampling rates that are significantly lower than the ones required with more classical approaches. The control strategy, that uses sensor data that is sampled at different rates in different nodes of a network, is also applied to a ground wheeled vehicle. Simulations and experiments show very smooth tracking of set-points and trajectories at a very low sampling frequency, which is the main advantage of the new technique

Agile load transportation systems using aerial robots

In this dissertation, we address problems that can occur during load transport using aerial robots, i.e., small scale quadrotors. First, detailed models of such transportation system are derived. These models include nonlinear models of a quadrotor, a model of a quadrotor carrying a fixed load and a model of a quadrotor carrying a suspended load. Second, the problem of quadrotor stabilization and trajectory tracking with changes of the center of gravity of the transportation system is addressed. This problem is solved using model reference adaptive control based on output feedback linearization that compensates for dynamical changes in the center of gravity of the quadrotor. The third problem we address is a problem of a swing-free transport of suspended load using quadrotors. Flying with a suspended load can be a very challenging and sometimes hazardous task as the suspended load significantly alters the flight characteristics of the quadrotor. In order to deal with suspended load flight, we present a method based on dynamic programming which is a model based offline method. The second investigated method we use is based on the Nelder-Mead algorithm which is an optimization technique used for nonlinear unconstrained optimization problems. This method is model free and it can be used for offline or online generation of the swing-free trajectories for the suspended load. Besides the swing-free maneuvers with suspended load, load trajectory tracking is another problem we solve in this dissertation. In order to solve this problem we use a Nelder-Mead based algorithm. In addition, we use an online least square policy iteration algorithm. At the end, we propose a high level algorithm for navigation in cluttered environments considering a quadrotor with suspended load. Furthermore, distributed control of multiple quadrotors with suspended load is addressed too. The proposed hierarchical architecture presented in this doctoral dissertation is an important step towards developing the next generation of agile autonomous aerial vehicles. These control algorithms enable quadrotors to display agile maneuvers while reconfiguring in real time whenever a change in the center of gravity occurs. This enables a swing-free load transport or trajectory tracking of the load in urban environments in a decentralized fashion

Adaptive and learning-based formation control of swarm robots

Autonomous aerial and wheeled mobile robots play a major role in tasks such as search and rescue, transportation, monitoring, and inspection. However, these operations are faced with a few open challenges including robust autonomy, and adaptive coordination based on the environment and operating conditions, particularly in swarm robots with limited communication and perception capabilities. Furthermore, the computational complexity increases exponentially with the number of robots in the swarm. This thesis examines two different aspects of the formation control problem. On the one hand, we investigate how formation could be performed by swarm robots with limited communication and perception (e.g., Crazyflie nano quadrotor). On the other hand, we explore human-swarm interaction (HSI) and different shared-control mechanisms between human and swarm robots (e.g., BristleBot) for artistic creation. In particular, we combine bio-inspired (i.e., flocking, foraging) techniques with learning-based control strategies (using artificial neural networks) for adaptive control of multi- robots. We first review how learning-based control and networked dynamical systems can be used to assign distributed and decentralized policies to individual robots such that the desired formation emerges from their collective behavior. We proceed by presenting a novel flocking control for UAV swarm using deep reinforcement learning. We formulate the flocking formation problem as a partially observable Markov decision process (POMDP), and consider a leader-follower configuration, where consensus among all UAVs is used to train a shared control policy, and each UAV performs actions based on the local information it collects. In addition, to avoid collision among UAVs and guarantee flocking and navigation, a reward function is added with the global flocking maintenance, mutual reward, and a collision penalty. We adapt deep deterministic policy gradient (DDPG) with centralized training and decentralized execution to obtain the flocking control policy using actor-critic networks and a global state space matrix. In the context of swarm robotics in arts, we investigate how the formation paradigm can serve as an interaction modality for artists to aesthetically utilize swarms. In particular, we explore particle swarm optimization (PSO) and random walk to control the communication between a team of robots with swarming behavior for musical creation

Eyes in the sky: multi-drones surveillance technology

Neste projeto pretende-se desenvolver uma rede de segurança baseada no trabalho cooperativo entre vários UAVs. Sabendo que os UAVs podem variar na sua autonomia, velocidade de voo, estabilidade e muitos outros fatores, será feito um estudo onde tentaremos potenciar as melhores características para a rede de segurança a desenvolver. Em simultâneo com este estudo serão aplicados algoritmos de controlo de distribuição aos vários agentes para que a cobertura da área seja máxima. O resultado final esperado deste projeto é conseguir criar um miniprograma capaz de comunicar com vários agentes de patrulha, receber as suas localizações, calcular as suas posições ideais ou, no caso de não conseguirem cobrir por completo a área, calcular uma rota de patrulha e, enviar as informações calculadas. Esperamos também que este programa possa ser usado em simulação e se possível no terreno.In this project, we will develop a security network based on the cooperation between several UAVs. Knowing that UAV's autonomy, speed, stability and many other factors, a study will be made where we will leverage the best characteristics for our goals. Simultaneously, we will design and apply a coverage algorithm to control the distribution of the agents in the area to maximize their coverage. As result of this project we wish to have a mini-program capable of communicate with several agents, read their locations, calculate their optimal positions or patrolling routes, if they can't cover all the area with their sensor range, and send them the information needed. We also want this program to be at least simulated and if possible on the field

Real-time control architecture for a multi UAV test bed

The purpose of this thesis is to develop a control architecture running at real-time for a multi unmanned aerial vehicle test bed formed by three AscTec Hummingbird mini quadrotors. The reliable and reconfigurable architecture presented here has a FPGA-based embedded system as main controller. Under the implemented control system, different practical applications have been performed in the MARHES Lab at the University of New Mexico as part of its research in cooperative control of mobile aerial agents. This thesis also covers the quadrotor modeling, the design of a position controller, the real-time architecture implementation and the experimental flight tests. A hybrid approach combining first-principles with system identification techniques is used for modeling the quadrotor due to the lack of information around the structure of the onboard controller designed by AscTec. The complete quadrotor model structure is formed by a black-box subsystem and a point-mass submodel. Experimental data have been gathered for system identification and black-box submodel validation purposes; while the point-mass submodel is found applying rigid-body dynamics. Using the dynamical model, a position control block based in lead-lag and PI compensators is developed and simulated. Improvements in trajectory tracking performance are achieved estimating the linear velocity of the aerial robot and incorporating velocity lead-lag compensators to the control approach. The velocity of the aerial robot is computed by numerical differentiation of position data. Simulation results to a variety of input signals of the control block in cascade with the complete dynamic model of the quadrotor are included. The control block together with the velocity estimation is fully programmed in the embedded controller. A graphical user interface, GUI, as part of the architecture is designed to display real-time data of position and orientation streamed from the motion tracking system as well as to contain useful user controllers. This GUI facilitates that a single operator conducts and oversees all aspects of the different applications where one or multiple quadrotors are used. Experimental tests have helped to tune the control parameters determined by simulation. The performance of the whole architecture has been validated through a variety of practical applications. Autonomous take off, hovering and landing, target surveillance, trajectory tracking and suspended payload transportation are just some of the applications carried out employing the real-time control architecture proposed in this thesis

Multi-robot coordination and safe learning using barrier certificates

The objective of this research is to develop a formal safety framework for collision-free and connectivity sustained motion in multi-robot coordination and learning based control. This safety framework is designed with barrier certificates, which provably guarantee the safety of dynamical systems based on the set invariance principle. The barrier certificates are enforced on the system using an online optimization-based controller such that minimal changes to the existing control strategies are required to guarantee safety. The proposed safety barrier certificates are validated on real multi-robot systems consisting of multiple Khepera robots, Magellan Pro robot, GRITS-Bots, and Crazyflie quadrotors.Ph.D

Distributed formation control for autonomous robots

This thesis addresses several theoretical and practical problems related to formation-control of autonomous robots. Formation-control aims to simultaneously accomplish the tasks of forming a desired shape by the robots and controlling their coordinated collective motion. This kind of robot performance is a cornerstone in the emerging field of swarm robotics, in particular with applications in precision agriculture, coverage of sport/art events, communication networks, area surveillance or vehicle platooning for energy efficiency and many others. One of the most important outcomes of this thesis is that the provided algorithms are completely distributed. This means that there is no central unit commanding the robots, but they have their own intelligence which allows them to make their own decisions based only on the local information. A distributed scheme entails a striking feature about the scalability and maintenance of a team of robots. Moreover, we also address the scenario of having wrongly calibrated sensors, which has a profound impact in the performance of the robots. The provided algorithms make the robots robust against such a practical and very common problem in real applications

An investigation of change in drone practices in broadacre farming environments

The application of drones in broadacre farming is influenced by novel and emergent factors. Drone technology is subject to legal, financial, social, and technical constraints that affect the Agri-tech sector. This research showed that emerging improvements to drone technology influence the analysis of precision data resulting in disparate and asymmetrically flawed Ag-tech outputs. The novelty of this thesis is that it examines the changes in drone technology through the lens of entropic decay. It considers the planning and controlling of an organisation’s resources to minimise harmful effects through systems change. The rapid advances in drone technology have outpaced the systematic approaches that precision agriculture insists is the backbone of reliable ongoing decision-making. Different models and brands take data from different heights, at different times of the day, and with flight of differing velocities. Drone data is in a state of decay, no longer equally comparable to past years’ harvest and crop data and are now mixed into a blended environment of brand-specific variations in height, image resolution, air speed, and optics. This thesis investigates the problem of the rapid emergence of image-capture technology in drones and the corresponding shift away from the established measurements and comparisons used in precision agriculture. New capabilities are applied in an ad hoc manner as different features are rushed to market. At the same time existing practices are subtly changed to suit individual technology capability. The result is a loose collection of technically superior drone imagery, with a corresponding mismatch of year-to-year agricultural data. The challenge is to understand and identify the difference between uniformly accepted technological advance, and market-driven changes that demonstrate entropic decay. The goal of this research is to identify best practice approaches for UAV deployment for broadacre farming. This study investigated the benefits of a range of characteristics to optimise data collection technologies. It identified widespread discrepancies demonstrating broadening decay on precision agriculture and productivity. The pace of drone development is so rapidly different from mainstream agricultural practices that the once reliable reliance upon yearly crop data no longer shares statistically comparable metrics. Whilst farmers have relied upon decades of satellite data that has used the same optics, time of day and flight paths for many years, the innovations that drive increasingly smarter drone technologies are also highly problematic since they render each successive past year’s crop metrics as outdated in terms of sophistication, detail, and accuracy. In five years, the standardised height for recording crop data has changed four times. New innovations, coupled with new rules and regulations have altered the once reliable practice of recording crop data. In addition, the cost of entry in adopting new drone technology is sufficiently varied that agriculturalists are acquiring multiple versions of different drone UAVs with variable camera and sensor settings, and vastly different approaches in terms of flight records, data management, and recorded indices. Without addressing this problem, the true benefits of optimization through machine learning are prevented from improving harvest outcomes for broadacre farming. The key findings of this research reveal a complex, constantly morphing environment that is seeking to build digital trust and reliability in an evolving global market in the face of rapidly changing technology, regulations, standards, networks, and knowledge. The once reliable discipline of precision agriculture is now a fractured melting pot of “first to market” innovations and highly competitive sellers. The future of drone technology is destined for further uncertainty as it struggles to establish a level of maturity that can return broadacre farming to consistent global outcomes

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection