Quadrotor Aggressive Deployment, Using a Quaternion-based Spherical Chattering-free Sliding-mode Controller

International audienceThis paper introduces a non-conventional approach for autonomous multi-rotor UAV deployment, in which a quadro-tor is aggressively launched through the air with its motors turned off. A continuous quaternion attitude trajectory is proposed to safely recover the vehicle into hover mode. Then, an operator then could take the command or continue a desired mission in autonomous mode. The controller is a chattering-free sliding mode algorithm based on the geometrical properties of quaternions and axis-angle rotations. Lyapunov theory is used to analyze the system stability. The proposed methodology is validated in real world indoor and outdoor experiments

Automatic control of a multirotor

Objective of this thesis is to describe the design and realisation phases of a multirotor to be used for low risk and cost aerial observation. Starting point of this activity was a wide literature study related to the technological evolution of multirotors design and to the state of the art. Firstly the most common multirotor configurations were defined and, according to a size and performance based evaluation, the most suitable one was chosen. A detailed computer aided design model was drawn as basis for the realisation of two prototypes. The realised multirotors were “X-shaped” octorotors with eight coaxially coupled motors. The mathematical model of the multirotor dynamics was studied. “Proportional Integral Derivative” and “Linear Quadratic” algorithms were chosen as techniques to regulate the attitude dynamics of the multirotor. These methods were tested with a nonlinear model simulation developed in the Matlab Simulink environment. In the meanwhile the Arduino board was selected as the best compromise between costs and performance and the above mentioned algorithms were implemented using this platform thanks to its main characteristic of being completely “open source”. Indeed the multirotor was conceived to be a serviceable tool for the public utility and, at the same time, to be an accessible device for research and studies. The behaviour of the physical multirotor was evaluated with a test bench designed to isolate the rotation about one single body axis at a time. The data of the experimental tests were gathered in real time using a custom Matlab code and several indoor tests allowed the “fine tuning” of the controllers gains. Afterwards a portable “ground station” was conceived and realised in adherence with the real scenarios users needs. Several outdoor experimental flights were executed with successful results and the data gathered during the outdoor tests were used to evaluate some key performance indicators as the endurance and the maximum allowable payload mass. Then the fault tolerance of the control system was evaluated simulating and experimenting the loss of one motor; even in this critical condition the system exhibited an acceptable behaviour. The reached project readiness allowed to meet some potential users as the “Turin Fire Department” and to cooperate with them in a simulated emergency. During this event the multirotor was used to gather and transmit real time aerial images for an improved “situation awareness”. Finally the study was extended to more innovative control techniques like the neural networks based ones. Simulations results demonstrated their effectiveness; nevertheless the inherent complexity and the unreliability outside the training ranges could have a catastrophic impact on the airworthiness. This is a factor that cannot be neglected especially in the applications related to flying platforms. Summarising, this research work was addressed mainly to the operating procedures for implementing automatic control algorithms to real platforms. All the design aspects, from the preliminary multirotor configuration choice to the tests in possible real scenarios, were covered obtaining performances comparable with other commercial of-the-shelf platforms

Science, technology and the future of small autonomous drones

We are witnessing the advent of a new era of robots — drones — that can autonomously fly in natural and man-made environments. These robots, often associated with defence applications, could have a major impact on civilian tasks, including transportation, communication, agriculture, disaster mitigation and environment preservation. Autonomous flight in confined spaces presents great scientific and technical challenges owing to the energetic cost of staying airborne and to the perceptual intelligence required to negotiate complex environments. We identify scientific and technological advances that are expected to translate, within appropriate regulatory frameworks, into pervasive use of autonomous drones for civilian applications

A Continuous-Time Nonlinear Observer for Estimating Structure from Motion from Omnidirectional Optic Flow

Various insect species utilize certain types of self-motion to perceive structure in their local environment, a process known as active vision. This dissertation presents the development of a continuous-time formulated observer for estimating structure from motion that emulates the biological phenomenon of active vision. In an attempt to emulate the wide-field of view of compound eyes and neurophysiology of insects, the observer utilizes an omni-directional optic flow field. Exponential stability of the observer is assured provided the persistency of excitation condition is met. Persistency of excitation is assured by altering the direction of motion sufficiently quickly. An equal convergence rate on the entire viewable area can be achieved by executing certain prototypical maneuvers. Practical implementation of the observer is accomplished both in simulation and via an actual flying quadrotor testbed vehicle. Furthermore, this dissertation presents the vehicular implementation of a complimentary navigation methodology known as wide-field integration of the optic flow field. The implementation of the developed insect-inspired navigation methodologies on physical testbed vehicles utilized in this research required the development of many subsystems that comprise a control and navigation suite, including avionics development and state sensing, model development via system identification, feedback controller design, and state estimation strategies. These requisite subsystems and their development are discussed

Use of Unmanned Aerial Systems in Civil Applications

Interest in drones has been exponentially growing in the last ten years and these machines are often presented as the optimal solution in a huge number of civil applications (monitoring, agriculture, emergency management etc). However the promises still do not match the data coming from the consumer market, suggesting that the only big field in which the use of small unmanned aerial vehicles is actually profitable is the video-makers’ one. This may be explained partly with the strong limits imposed by existing (and often "obsolete") national regulations, but also - and pheraps mainly - with the lack of real autonomy. The vast majority of vehicles on the market nowadays are infact autonomous only in the sense that they are able to follow a pre-determined list of latitude-longitude-altitude coordinates. The aim of this thesis is to demonstrate that complete autonomy for UAVs can be achieved only with a performing control, reliable and flexible planning platforms and strong perception capabilities; these topics are introduced and discussed by presenting the results of the main research activities performed by the candidate in the last three years which have resulted in 1) the design, integration and control of a test bed for validating and benchmarking visual-based algorithm for space applications; 2) the implementation of a cloud-based platform for multi-agent mission planning; 3) the on-board use of a multi-sensor fusion framework based on an Extended Kalman Filter architecture

Towards an autonomous landing system in presence of uncertain obstacles in indoor environments

The landing task is fundamental to Micro air vehicles (MAVs) when attempting to land in an unpredictable environment (e.g., presence of static obstacles or moving obstacles). The MAV should immediately detect the environment through its sensors and decide its actions for landing. This paper addresses the problem of the autonomous landing approach of a commercial AR. Drone 2.0 in presence of uncertain obstacles in an indoor environment. A localization methodology to estimate the drone's pose based on the sensor fusion techniques which fuses IMU and Poxyz signals is proposed. In addition, a vision-based approach to detect and estimate the velocity, position of the moving obstacle in the drone's working environment is presented. To control the drone landing accurately, a cascade control based on an Accelerated Particle Swarm Optimization algorithm (APSO) is designed. The simulation and experimental results demonstrate that the obtained model is appropriate for the measured data