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

Reinforcement Learning to Control Lift Coefficient Using Distributed Sensors on a Wind Tunnel Model

Arrays of sensors distributed on the wing of fixed-wing vehicles can provide information not directly available to conventional sensor suites. These arrays of sensors have the potential to improve flight control and overall flight performance of small fixed-wing uninhabited aerial vehicles (UAVs). This work investigated the feasibility of estimating and controlling aerodynamic coefficients using the experimental readings of distributed pressure and strain sensors across a wing. The study was performed on a one degree-of-freedom model about pitch of a fixed-wing platform instrumented with the distributed sensing system. A series of reinforcement learning (RL) agents were trained in simulation for lift coefficient control, then validated in wind tunnel experiments. The performance of RL-based controllers with different sets of inputs in the observation space were compared with each other and with that of a manually tuned PID controller. Results showed that hybrid RL agents that used both distributed sensing data and conventional sensors performed best across the different tests.</p

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Development, modeling, and simulation of a nano aerial vehicle using empirical data

The ultimate goals of this study were to use experimental data to estimate the flight capabilities of a flapping wing nano aerial vehicle (NAV), estimate the power required to provide such flight, and develop a controller approach for future use in the design of this aircraft. The experimental data is a collection of measurements of the normal force on a flapping wing taken in stationary water, and was used to develop empirical coefficient derivatives for use in the dynamic modeling of the NAV --Abstract, page iii

Modeling and Characterization of Bioinspired Hybrid Flapping/Gliding Flight for Flapping Wing Air Vehicles

Unmanned Aerial Vehicles (UAVs) are increasingly being used for applications that require longer, reliable flight duration and distances. The greatest limitation to achieving these desired flights is the current on board battery technology which, restricted by internal chemistry and external size, can only provide a finite amount of power over time. Efforts to increase the battery’s efficiency and energy storage tend to rely on cumbersome methods that add weight and/or complexity to the system. However natural flyers, though also limited by a finite amount of internal energy gained through food consumption, are able to extend their flights through techniques that either utilize their inherent aerodynamic advantages or advantageously employ atmospheric phenomena. Flapping-Wing UAVs (FWUAVs) are as limited by their onboard battery as any other type of UAV, but because of their bio-inspired functionality are uniquely suited to utilize natural flight extension methods. Therefore, this PhD presents an analysis of the exploration of bio-inspired, hybrid flapping/gliding, also known as intermittent gliding, techniques to improve the flight performance of a FWUAV. Robo Raven is the FWUAV that was chosen as the research platform for this work. It was developed by researchers at the University of Maryland to perform prolonged, untethered flights and exhibit a flight proficiency that combined the maneuverability of rotary-wing flight with the efficiency of fixed-wing flight. The technique to improve FWUAV flight time, presented in this work incorporates (1) the modeling of Robo Raven’s flapping/gliding potential through the development of a state-space representation directly linking Robo Raven’s onboard battery dynamics with its aerodynamic performance, (2) the use of the state-space model to characterize the effect of intermittent gliding techniques on flight performance through simulation, (3) the real-world characterization of the simulation and of intermittent gliding techniques through flight demonstrations, and (4) the development of a design space by which the effect of wing design on gliding performance might be explored and lead to the potential tailoring of wing design to desired flight performance. The expected outcome of this technique is scientific analysis of the extension of Robo Raven’s flight time without added complexity of weight of the battery system

IMPROVED PREDICTION OF FLAPPING WING AERIAL VEHICLE PERFORMANCE THROUGH COMPONENT INTERACTION MODELING

Flapping wing aerial vehicles offer the promise of versatile performance, however prediction of flapping wing aerial vehicle performance is a challenging task because of complex interconnectedness in vehicle functionality. To address this challenge, performance is estimated by using component-level modeling as a foundation. Experimental characterization of the drive motors, battery, and wings is performed to identify important functional characteristics and enable selection of appropriate modeling techniques. Component-level models are then generated that capture the performance of each vehicle component. Validation of each component-level model shows where errors are eliminated by capturing important dynamic functionality. System-level modeling is then performed by creating linkages between component-level models that have already been individually validated through experimental testing, leading to real-world functional constraints that are realized and correctly modeled at the system level. The result of this methodology is a system-level performance prediction that offers the ability to explore the effects of changing vehicle components as well as changing functional properties, while maintaining computational tractability. Simulated results are compared to experimental flight test data collected with an instrumented flapping wing aerial vehicle, and are shown to offer good accuracy in estimation of system-level performance properties

Autonomous Vehicles

This edited volume, Autonomous Vehicles, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of vehicle autonomy. The book comprises nine chapters authored by various researchers and edited by an expert active in the field of study. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts by international authors, open new possible research paths for further novel developments, and to inspire the younger generations into pursuing relevant academic studies and professional careers within the autonomous vehicle field