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

Coordinated Turn Trajectory Generation and Tracking Control for Multi-Rotors Operating in Urban Environment

The paper presents an efficient trajectory generation and tracking approach for multi-rotor air vehicles operating in urban environment, which takes into account uncertainties in the urban wind field and in the vehicle's parameters. Generated trajectories are sufficiently smooth, based on the differential flatness of the vehicle's dynamics and optimal in the sense of minimum agility and time. They pass through given set of way points, guarantee flight without a side-slip, and satisfy vehicle's dynamics and actuators constraints. In addition, an algorithm is presented to compute the required power to traverse the generated trajectory. Presented algorithms are implementable in real time using on-board computers. They do not take into account the vehicle's existing flight controller, hence there is no guarantee that the controller will be able to provide acceptable tracking of the generated trajectory, especially in the presence of atmospheric disturbances. To this end, we propose an adaptive augmentation algorithm to improve vehicle's performance by taking into account the effects of disturbances and on-line estimates of vehicle's existing flight controller's gains. The algorithms have been verified by simulations using DJI S1000 octocopter's model

Coordinated Turn Trajectory Generation and Tracking Control for Multi-rotors Operating in Urban Environment

The paper presents an efficient trajectory generation and tracking approach for multi-rotor air vehicles operating in urban environment, which takes into account uncertainties in the urban wind field and in the vehicle's parameters. Generated trajectories are sufficiently smooth, based on the differential flatness of the vehicle's dynamics and optimal in the sense of minimum agility and time. They pass through given set of way points, guarantee flight without a side-slip, and satisfy vehicle's dynamics and actuator constraints. In addition, an algorithm is presented to compute the required power to traverse the generated trajectory. Presented algorithms are implementable in real time using on-board computers. They do not take into account the vehicle's existing flight controller, hence there is no guarantee that the controller will be able to provide acceptable tracking of the generated trajectory, especially in the presence of atmospheric disturbances. To this end, we propose an adaptive augmentation algorithm to improve vehicle's performance by taking into account the effects of disturbances and on-line estimates of vehicle's existing flight controller's gains. The algorithms have been verified by simulations using DJI S1000 octocopter's model

Automated multi-rotor draft survey of large vessels

In maritime sector draft survey has a significant importance as it is used to determine many important factors used in maritime transportation. Draft is the vertical displacement from the bottom of the keel (the bottom-most element of a vessel) to the water line (the line of meeting point of hull and the water surface). It is used to measure the minimum water depth for safe navigation of vessel and to evaluate mass of cargo in the vessel by the change in displacement on the draft scale after loading of the cargo in the vessel. Draft measurement of a vessel has a vital role in maritime sector to ensure a safe equilibrium between maximum and minimum cargo that can be loaded in the vessel. Draft survey performed at the time of loading and unloading of cargo (Iron Ore) at the Narvik port to read out draft markings traditionally involved a round trip around the vessel in a small crew boat and it is a time consuming and challenging task specially in darkness (during night), shadows and when difficult to safely reach the crew boat close enough due to anchors and buoys. The goal of this study is to develop an autonomous multi-rotor system that can survey the large vessel to capture all the necessary draft measurements by reaching close enough even in challenging environments like nighttime and in presence of obstacles. This involves developing the solution for path planning to perform flight operation autonomously, developing guidance and control algorithm for the flight operation to enable the multi-rotor to follow the designated path and perform the inspection while avoiding all the hurdles using collision avoidance system. Along with developing the specifications for a multi-rotor that can perform the inspection and suggest necessary system components including multi-rotor itself and additional components such as sensors, lights and camera, and necessities for on-board data handling

Quadcopter Trajectory Prediction and Wind Estimation Using Machine Learning

Small unmanned aerial systems are heavily impacted by wind disturbances. Wind causes deviations from desired trajectories, potentially leading to crashes. In this thesis, we consider two inherently related problems: predicting quadcopter trajectory deviations due to wind disturbances and estimating wind velocity based on quadcopter trajectory deviations. The former is addressed using linear difference equation identification as well as neural network (NN) modeling. Simulations validate the use of linear difference equation identification as a tool to predict trajectory deviations in crosswinds and machine learning (specifically, long short-term memory (LSTM) NNs) as an approach to predict trajectory deviations in multidimensional wind. We approach the wind estimation problem from a machine learning perspective due to easier generalization of the NN to multidimensional winds. As in the trajectory prediction case, we use LSTM NNs to identify a model. The trained NN is deployed to estimate the turbulent winds as generated by the Dryden gust model as well as a realistic large eddy simulation of a near-neutral atmospheric boundary layer over flat terrain. The resulting NN predictions are compared to a wind triangle approach that uses tilt angle as an approximation of airspeed. Results from this study indicate that the LSTM NN based approach results in lower errors in both the mean and variance of the local wind field as compared to the wind triangle approach

NAVIGATION AND AUTONOMOUS CONTROL OF MAVS IN GPS-DENIED ENVIRONMENTS

Ph.DDOCTOR OF PHILOSOPH

Towards MAV Autonomous Flight: A Modeling and Control Approach

This thesis is about modeling and control of miniature rotary-wing flying vehicles, with a special emphasis on quadrotor and coaxial systems. Mathematical models for simulation and nonlinear control approaches are introduced and subsequently applied to commercial aircrafts: the DraganFlyer and the Hummingbird quadrotors, which have been hardware-modified in order to perform experimental autonomous flying. Furthermore, a first-ever approach for modeling commercial micro coaxial mechanism is presented using a flying-toy called the Micro-mosquito

Estimation, Navigation and Control of Multi-Rotor Drones in an Urban Wind Field

The paper presents an on-board estimation, navigation and control architecture for multi-rotor drones flying in urban environment. It consists of adaptive algorithms to estimate vehicle's aerodynamic drag coefficients with respect to still air and the urban wind components along the flight trajectory, with guaranteed fast and reliable convergence to the true values; navigation algorithms to generate feasible trajectories between given way-points that take into account the estimated wind; and of control algorithms to track the generated trajectories as long as the vehicle retains sufficient number of functioning rotors capable of compensating for the estimated wind. All components of this on-board system are computationally effective and are intended for a real time implementation. The algorithms were tested in simulations