A 10-gram Vision-based Flying Robot

We aim at developing ultralight autonomous microflyers capable of freely flying within houses or small built environments while avoiding collisions. Our latest prototype is a fixed-wing aircraft weighing a mere 10 g, flying around 1.5 m/s and carrying the necessary electronics for airspeed regulation and lateral collision avoidance. This microflyer is equipped with two tiny camera modules, two rate gyroscopes, an anemometer, a small microcontroller, and a Bluetooth radio module. Inflight tests are carried out in a new experimentation room specifically designed for easy changing of surrounding textures

Vision-based control of near-obstacle flight

Lightweight micro unmanned aerial vehicles (micro-UAVs) capable of autonomous flight in natural and urban environments have a large potential for civil and commercial applications, including environmental monitoring, forest fire monitoring, homeland security, traffic monitoring, aerial imagery, mapping and search and rescue. Smaller micro-UAVs capable of flying inside houses or small indoor environments have further applications in the domain of surveillance, search and rescue and entertainment. These applications require the capability to fly near to the ground and amongst obstacles. Existing UAVs rely on GPS and AHRS (attitude heading reference system) to control their flight and are unable to detect and avoid obstacles. Active distance sensors such as radars or laser range finders could be used to measure distances to obstacles, but are typically too heavy and power-consuming to be embedded on lightweight systems. In this thesis, we draw inspiration from biology and explore alternative approaches to flight control that allow aircraft to fly near obstacles. We show that optic flow can be used on flying platforms to estimate the proximity of obstacles and propose a novel control strategy, called optiPilot, for vision-based near-obstacle flight. Thanks to optiPilot, we demonstrate for the first time autonomous near-obstacle flight of micro-UAVs, both indoor and outdoor, without relying on an AHRS nor external beacons such as GPS. The control strategy only requires a small series of optic flow sensors, two rate gyroscopes and an airspeed sensor. It can run on a tiny embedded microcontroller in realtime. Despite its simplicity, optiPilot is able to fully control the aircraft, including altitude regulation, attitude stabilisation, obstacle avoidance, landing and take-off. This parsimony, inherited from the biology of flying insects, contrasts with the complexity of the systems used so far for flight control while offering more capabilities. The results presented in this thesis contribute to a better understanding of the minimal requirements, in terms of sensing and control architecture, that enable animals and artificial systems to fly and bring closer to reality the perspective of using lightweight and inexpensive micro-UAV for civilian purposes

Optic flow to control small UAVs

Autonomous flight in confined or cluttered environments such as houses or urban canyons requires high manoeuvrability, fast mapping from sensors to actuators and very limited overall system weight. Although flying animals are well capable of coping with such situations, roboticists still have difficulties at reproducing such capabilities. This paper describes how we took inspiration from flying insects to progress toward the goal of developing small UAVs able to dynamically fly in cluttered environments. This endeavour allowed us to demonstrate a 10-gram microflyer capable of fully autonomous operation in an office-sized room using fly-inspired vision, inertial and airspeed sensors. This encouraging result is now being ported to outdoor scenarios such as low-altitude flight in urban or mountainous environments. Important is that these autonomous capabilities are achieved without the help of GPS nor active range finders, which allows to develop very lightweight autopilots

Aerial Locomotion in Cluttered Environments

Many environments where robots are expected to operate are cluttered with objects, walls, debris, and different horizontal and vertical structures. In this chapter, we present four design features that allow small robots to rapidly and safely move in 3 dimensions through cluttered environments: a perceptual system capable of detecting obstacles in the robot’s surroundings, including the ground, with minimal computation, mass, and energy requirements; a flexible and protective framework capable of withstanding collisions and even using collisions to learn about the properties of the surroundings when light is not available; a mechanism for temporarily perching to vertical structures in order to monitor the environment or communicate with other robots before taking off again; and a self-deployment mechanism for getting in the air and perform repetitive jumps or glided flight. We conclude the chapter by suggesting future avenues for integration of multiple features within the same robotic platform

Rotary-wing MAV Modeling & Control for indoor scenarios

This paper is about modeling and control of Miniature Aerial Vehicles ¿MAVs for indoor scenarios, specially using, micro coaxial and quadrotor systems. Mathematical models for simulation and control are introduced and subsequently applied to the commercial aircraft: the DraganFlyer quadrotor and the Micro-Mosquito coaxial flying vehicle. The MAVs have been hardware-modified in order to perform experimental autonomous flight. A novel approach for control based on Hybrid Backstepping and the Frenet-Serret theory is used for attitude stabilization (Backstepping+FST), introducing a desired attitude angle acceleration function dependent on aircraft velocity. Results of autonomous hovering and tracking are presented based on the scheme we propose for control and attitude stabilization when MAV is maneuvering at moderate speeds

Mini-quadrotor Attitude Control based on Hybrid Backstepping & Frenet-Serret Theory

This paper is about modeling and control of miniature quadrotors, with a special emphasis on attitude control. Mathematical models for simulation and nonlinear control approaches are introduced and subsequently applied to commercial aircraft: the DraganFlyer quadrotor, which has been hardware-modified in order to perform experimental autonomous flying. Hybrid Backstepping control and the Frenet-Serret theory is used for attitude stabilization, introducing a desired attitude angle acceleration function dependent on aircraft velocity. Finally, improvements on disturbance rejection and attitude tracking at moderate aircraft speeds are validated through various simulation scenarios (indoor navigation based on camera tracking), and flight experiments conducted on the DraganFlyer quadroto

Insect inspired visual motion sensing and flying robots

International audienceFlying insects excellently master visual motion sensing techniques. They use dedicated motion processing circuits at a low energy and computational costs. Thanks to observations obtained on insect visual guidance, we developed visual motion sensors and bio-inspired autopilots dedicated to flying robots. Optic flow-based visuomotor control systems have been implemented on an increasingly large number of sighted autonomous robots. In this chapter, we present how we designed and constructed local motion sensors and how we implemented bio-inspired visual guidance scheme on-board several micro-aerial vehicles. An hyperacurate sensor in which retinal micro-scanning movements are performed via a small piezo-bender actuator was mounted onto a miniature aerial robot. The OSCAR II robot is able to track a moving target accurately by exploiting the microscan-ning movement imposed to its eye's retina. We also present two interdependent control schemes driving the eye in robot angular position and the robot's body angular position with respect to a visual target but without any knowledge of the robot's orientation in the global frame. This "steering-by-gazing" control strategy, which is implemented on this lightweight (100 g) miniature sighted aerial robot, demonstrates the effectiveness of this biomimetic visual/inertial heading control strategy

Flying over the reality gap: From simulated to real indoor airships

Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approac