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

This paper presents a novel control strategy, which we call optiPilot, for autonomous flight in the vicinity of obstacles. Most existing autopilots rely on a complete 6-degree-of-freedom state estimation using a GPS and an Inertial Measurement Unit (IMU) and are unable to detect and avoid obstacles. This is a limitation for missions such as surveillance and environment monitoring that may require near-obstacle flight in urban areas or mountainous environments. OptiPilot instead uses optic flow to estimate proximity of obstacles and avoid them. Our approach takes advantage of the fact that, for most platforms in translational flight (as opposed to near-hover flight), the translatory motion is essentially aligned with the aircraft main axis. This property allows us to directly interpret optic flow measurements as proximity indications. We take inspiration from neural and behavioural strategies of flying insects to propose a simple mapping of optic flow measurements into control signals that requires only a lightweight and power-efficient sensor suite and minimal processing power. In this paper, we first describe results obtained in simulation before presenting the implementation of optiPilot on a real flying platform equipped only with lightweight and inexpensive optic computer mouse sensors, MEMS rate gyroscopes and a pressure-based airspeed sensor. We show that the proposed control strategy not only allows collision-free flight in the vicinity of obstacles, but is also able to stabilise both attitude and altitude over flat terrain. These results shed new light on flight control by suggesting that the complex sensors and processing required for 6 degree-of-freedom state estimation may not be necessary for autonomous flight and pave the way toward the integration of autonomy into current and upcoming gram-scale flying platform

Obstacle detection technique using multi sensor integration for small unmanned aerial vehicle

Achieving a robust obstacle detection system for small UAV is very challenging. Due to size and weight constraints, very limited detection sensors can be equipped in the system. Prior works focused on a single sensing device which is either camera or range sensors based. However, these sensors have their own advantages and disadvantages in detecting the appearance of the obstacles. In this paper, combination of both sensors based is proposed for a small UAV obstacle detection system. A small Lidar sensor is used as the initial detector and queue for image capturing by the camera. Next, SURF algorithm is applied to find the obstacle sizes estimation by searching the connecting feature points in the image frame. Finally, safe avoidance path for UAV is determined through the exterior feature points from the estimated width of the obstacle. The proposed method was evaluated by conducting experiments in real time with indoor environment. In the experiment conducted, we successfully detect and determine a safe avoidance path for the UAV on 6 different sizes and textures of the obstacles including textureless obstacle

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

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

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

Autonomous flight at low altitude with vision-based collision avoidance and GPS-based path following

The ability to fly at low altitude while actively avoiding collisions with the terrain and other objects is a great challenge for small unmanned aircraft. This paper builds on top of a control strategy called optiPilot whereby a series of optic-flow detectors pointed at divergent viewing directions around the aircraft main axis are linearly combined into roll and pitch commands using two sets of weights. This control strategy already proved successful at controlling flight and avoiding collisions in reactive navigation experiments. This paper shows how optiPilot can be coupled with a GPS in order to provide goal-directed, nap-of-the-earth flight control in presence of static obstacles. Two fully autonomous flights of 25 minutes each are described where a 400-gram unmanned aircraft is flying at approx. 9 m above the terrain on a circular path including two copses of trees requiring efficient collision avoidance actions