Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Small Unmanned Aircraft Systems for Project-Based Engineering Education

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143092/1/6.2017-1377.pd

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Path Planning and Control of an Autonomous Quadrotor Testbed in a Cluttered Environment

A classical problem for robotic navigation is how to efficiently navigate from one point to another and what to do if obstacles are encountered along the way. Many map based path planning algorithms attempt to solve this problem, all with varying levels of optimality and complexity. This work shows a review of selected algorithms, and two of these are selected for simulation and testing using a quadrotor unmanned aerial vehicle (UAV) in a dynamic indoor environment which requires replanning capabilities. The Dynamic A* algorithm, or simply D*, and the Probabilistic Roadmap method (PRM) are used in a scenario designed to test their respective functionality and usefulness with the goal of determining the better algorithm for flight testing given a partially known or changing environment.;The development of the quadrotor platform hardware is discussed as well as the associated software and capabilities. Both algorithms are redesigned to fit this specific application and display their respective planned and replanned paths in an intuitive and comparable manner. Simulation is performed and an obstacle is added to the map during the quadrotor motion, requiring a replanned path. Results are compared for both computed path length and computational intensity. Flight testing is performed in an indoor environment, and during the flight an obstacle is inserted into the flight path, requiring detection and replanning. Results are compared for computed path length and intuitively analyzed to compare optimality and complexity

Continuous Autonomous UAV Inspection for FPSO vessels

This Master's thesis represents the preliminary design study and proposes the unmanned aerial vehicle (UAV) -based inspection framework, comprising several multirotors with automatic charging and deployment for 24/7 integrity inspection tasks. This project has three main topics. First one describes the operational environment and existing regulations that cover use of UAVs. It forms the basis for proposal of the relevant use-case scenarios. Third part comprises two chapters, where design of concept and framework is being based on the previous factors. It shows that before implementation of fully autonomous inspection system, there is a need to cover both regulatory and technical gaps. It can be explained by the fact that there does not exist any autonomous inspection system today. Thus, this project can be seen as a base for future development of the UAV-based inspection system, as it focuses on creation of a general framework

Design and control of quadrotors with application to autonomous flying

This thesis is about modelling, design and control of Miniature Flying Robots (MFR) with a focus on Vertical Take-Off and Landing (VTOL) systems and specifically, micro quadrotors. It introduces a mathematical model for simulation and control of such systems. It then describes a design methodology for a miniature rotorcraft. The methodology is subsequently applied to design an autonomous quadrotor named OS4. Based on the mathematical model, linear and nonlinear control techniques are used to design and simulate various controllers along this work. The dynamic model and the simulator evolved from a simple set of equations, valid only for hovering, to a complex mathematical model with more realistic aerodynamic coefficients and sensor and actuator models. Two platforms were developed during this thesis. The first one is a quadrotor-like test-bench with off-board data processing and power supply. It was used to safely and easily test control strategies. The second one, OS4, is a highly integrated quadrotor with on-board data processing and power supply. It has all the necessary sensors for autonomous operation. Five different controllers were developed. The first one, based on Lyapunov theory, was applied for attitude control. The second and the third controllers are based on PID and LQ techniques. These were compared for attitude control. The fourth and the fifth approaches use backstepping and sliding-mode concepts. They are applied to control attitude. Finally, backstepping is augmented with integral action and proposed as a single tool to design attitude, altitude and position controllers. This approach is validated through various flight experiments conducted on the OS4

A Survey on Aerial Swarm Robotics

The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional space and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of this paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas

PIXHAWK: A micro aerial vehicle design for autonomous flight using onboard computer vision

We describe a novel quadrotor Micro Air Vehicle (MAV) system that is designed to use computer vision algorithms within the flight control loop. The main contribution is a MAV system that is able to run both the vision-based flight control and stereo-vision-based obstacle detection parallelly on an embedded computer onboard the MAV. The system design features the integration of a powerful onboard computer and the synchronization of IMU-Vision measurements by hardware timestamping which allows tight integration of IMU measurements into the computer vision pipeline. We evaluate the accuracy of marker-based visual pose estimation for flight control and demonstrate marker-based autonomous flight including obstacle detection using stereo vision. We also show the benefits of our IMU-Vision synchronization for egomotion estimation in additional experiments where we use the synchronized measurements for pose estimation using the 2pt+gravity formulation of the PnP proble

Model Predictive Control (MPC) of quadrotors using LPV techniques

This Thesis objective was to apply Model predictive control (MPC) on a quadcopters using linear parameter varying (LPV) techniques. In order to do so the non-linear mathematical model of the quadcopter was put in the Linear Parameter Varying (LPV) form in order to be able to use the most basic Model Predictive Control (MPC) strategy, developed for linear systems. After applying the MPC strategy, the aim was to make the quadcopter track a given trajectory. Different trajectories were tested and validated. Initially, the most important step was to define the coordinate frames that are used to control the quadcopter and to establish the mathematical model of a quadcopter. Once the mathematical model of the quadcopter is developed, the next step was to design the controller. The controller was split into two sub controllers. One controller is responsible for the position variables x, y, z. This position controller controls the position variables by using the state feedback linearization method. Moreover, the attitude controller was used to control the angles using the LPV-MPC control strategy. The proposed strategy turned out to be a success in controlling the quadcopter. All the Quadcopter’s six degrees of freedom are tracked with very small errors. In tracking the x, y, z reference velocity values, one can observe strong overshoot at the beginning of the test period. That can be explained by the fact that the quadcopter starts its journey from quite a long distance away from the trajectory. However, once it reaches the path that it needs to follow, the velocities of the quadcopter stabilize and track the reference values very smoothly. Everything was done keeping in mind that the LPV-MPC controller needs time to push the state angles towards its reference values. Therefore, the attitude controller must work at a higher frequency compared to the position controlle