Vision-based control of near-obstacle flight

Abstract

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 re-

Science, technology and the future of small autonomous drones

“…For example, some authors [55][56][57] have resorted to using the multiple optic flow sensors found in computer optical mice. Whereas others 68,69 have developed neuromorphic chips that not only extract optic flow, but also adapt to the large variety of light intensities that can be experienced when flying in confined spaces.…”

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

“…These two limits (θ < 90°and θ > 0°) suggest that the area of interest lies aroundθ = 45°. Experimental and theoretical work has shown that 45°is indeed optimal in a variety of situations [20], [18], [13]. In most practical cases, objects will not appear above the aircraft (except if it is flying down a tunnel or inverted over ground), which allows to further reduce the number of required viewing direction.…”

“…Since obstacles in the ventral region (ψ = 180°) are avoided by pitching only, the weights in this region should be set to zero. For more details on this control strategy, possible variations of the weight distributions, viewing directions and the impact of the wind, the reader is invited to consult either [13] or [19].…”

“…In particular, we recently proposed a control strategy called optiPilot [13] that maps optic flow as sampled in many divergent viewing directions and map them into steering commands. OptiPilot requires neither GPS nor IMU to control the flight and avoid collisions.…”

Bio-inspired visual ego-rotation sensor for MAVs

“…In fact, over the past decades the insect visual system has inspired many studies towards visually guided autonomous vehicles. Much emphasis has been put on the implementation of collision avoidance strategies (e.g., Harrison 2005; Bermudez i Badia et al 2007) and local navigation (e.g., Zufferey and Floreano 2006;Srinivasan et al 2009;Conroy et al 2009;Moeckel and Liu 2009;Beyeler et al 2009;Hyslop et al 2010). Moreover, considerable work has been put forth on autonomous height control (e.g., Netter and Franceschini 2002;Valette et al 2010).…”