A mouse sensor and a 2-pixel motion sensor exposed to continuous illuminance changes

International audienceConsiderable attention has been paid during the last decade to navigation systems based on the use of visual optic flow cues, especially for guiding autonomous robots designed to travel under specific lighting conditions. In the present study, the performances of two visual motion sensors used to measure a local 1-D angular speed, namely (i) a bio-inspired 2-pixel motion sensor and (ii) an off-the-shelf mouse sensor, were tested for the first time in a wide range of illuminance levels. The sensors' characteristics were determined here by recording their responses to a purely rotational optic flow generated by rotating the sensors mechanically and comparing their responses with an accurate rate gyro output signal. The refresh rate, a key parameter for future optic flow-based robotic applications, was also defined and tested in these two sensors. The bio-inspired 2-pixel motion sensor was found to be more accurate indoors whereas the mouse sensor was found to be more efficient outdoors

A two-directional 1-gram visual motion sensor inspired by the fly's eye

International audienceOptic flow based autopilots for Micro-Aerial Vehicles (MAVs) need lightweight, low-power sensors to be able to fly safely through unknown environments. The new tiny 6-pixel visual motion sensor presented here meets these demanding requirements in term of its mass, size and power consumption. This 1-gram, low-power, fly-inspired sensor accurately gauges the visual motion using only this 6-pixel array with two different panoramas and illuminance conditions. The new visual motion sensor's output results from a smart combination of the information collected by several 2-pixel Local Motion Sensors (LMSs), based on the \enquote{time of travel} scheme originally inspired by the common housefly's Elementary Motion Detector (EMD) neurons. The proposed sensory fusion method enables the new visual sensor to measure the visual angular speed and determine the main direction of the visual motion without any prior knowledge. By computing the median value of the output from several LMSs, we also ended up with a more robust, more accurate and more frequently refreshed measurement of the 1-D angular speed

Toward an Autonomous Lunar Landing Based on Low-Speed Optic Flow Sensors

International audienceFor the last few decades, growing interest has returned to the quite chal-lenging task of the autonomous lunar landing. Soft landing of payloads on the lu-nar surface requires the development of new means of ensuring safe descent with strong final conditions and aerospace-related constraints in terms of mass, cost and computational resources. In this paper, a two-phase approach is presented: first a biomimetic method inspired from the neuronal and sensory system of flying insects is presented as a solution to perform safe lunar landing. In order to design an au-topilot relying only on optic flow (OF) and inertial measurements, an estimation method based on a two-sensor setup is introduced: these sensors allow us to accu-rately estimate the orientation of the velocity vector which is mandatory to control the lander's pitch in a quasi-optimal way with respect to the fuel consumption. Sec-ondly a new low-speed Visual Motion Sensor (VMS) inspired by insects' visual systems performing local angular 1-D speed measurements ranging from 1.5 • /s to 25 • /s and weighing only 2.8 g is presented. It was tested under free-flying outdoor conditions over various fields onboard an 80 kg unmanned helicopter. These pre-liminary results show that the optic flow measured despite the complex disturbances encountered closely matched the ground-truth optic flow

Controlling docking, altitude and speed in a circular high-roofed tunnel thanks to the optic flow

International audienceThe new robot called BeeRotor we have developed is a tandem rotorcraft that mimicks optic flow-based behaviors previously observed in flies and bees. This tethered miniature robot (80g), which is autonomous in terms of its computational power requirements, is equipped with a 13.5-g quasi-panoramic visual system consisting of 4 individual visual motion sensors responding to the optic flow generated by photographs of natural scenes, thanks to the bio-inspired "time of travel" scheme. Based on recent findings on insects' sensing abilities and control strategies, the BeeRotor robot was designed to use optic flow to perform complex tasks such as ground and ceiling following while also automatically driving its forward speed on the basis of the ventral or dorsal optic flow. In addition, the BeeRotor robot can perform tricky manoeuvers such as automatic ceiling docking by simply regulating its dorsal or ventral optic flow in high-roofed tunnel depicting natural scenes. Although it was built as a proof of concept, the BeeRotor robot is one step further towards achieving a fully- autonomous micro-helicopter which is capable of navigating mainly on the basis of the optic flow

Bio-inspired Landing Approaches and Their Potential Use On Extraterrestrial Bodies

International audienceAutomatic landing on extraterrestrial bodies is still a challenging and hazardous task. Here we propose a new type of autopilot designed to solve landing problems, which is based on neurophysiological, behavioral, and biorobotic findings on flying insects. Flying insects excel in optic flow sensing techniques and cope with highly parallel data at a low energy and computational cost using lightweight dedicated motion processing circuits. In the first part of this paper, we present our biomimetic approach in the context of a lunar landing scenario, assuming a 2-degree-of-freedom spacecraft approaching the moon, which is simulated with the PANGU software. The autopilot we propose relies only on optic flow (OF) and inertial measurements, and aims at regulating the OF generated during the landing approach, by means of a feedback control system whose sensor is an OF sensor. We put forward an estimation method based on a two-sensor setup to accurately estimate the orientation of the lander's velocity vector, which is mandatory to control the lander's pitch in a near optimal way with respect to the fuel consumption. In the second part, we present a lightweight Visual Motion Sensor (VMS) which draws on the results of neurophysiological studies on the insect visual system. The VMS was able to perform local 1-D angular speed measurements in the range 1.5°/s - 25°/s. The sensor was mounted on an 80 kg unmanned helicopter and test-flown outdoors over various fields. The OF measured onboard was shown to match the ground-truth optic flow despite the dramatic disturbances and vibrations experienced by the sensor

Honeybees' Speed Depends on Dorsal as Well as Lateral, Ventral and Frontal Optic Flows

Flying insects use the optic flow to navigate safely in unfamiliar environments, especially by adjusting their speed and their clearance from surrounding objects. It has not yet been established, however, which specific parts of the optical flow field insects use to control their speed. With a view to answering this question, freely flying honeybees were trained to fly along a specially designed tunnel including two successive tapering parts: the first part was tapered in the vertical plane and the second one, in the horizontal plane. The honeybees were found to adjust their speed on the basis of the optic flow they perceived not only in the lateral and ventral parts of their visual field, but also in the dorsal part. More specifically, the honeybees' speed varied monotonically, depending on the minimum cross-section of the tunnel, regardless of whether the narrowing occurred in the horizontal or vertical plane. The honeybees' speed decreased or increased whenever the minimum cross-section decreased or increased. In other words, the larger sum of the two opposite optic flows in the horizontal and vertical planes was kept practically constant thanks to the speed control performed by the honeybees upon encountering a narrowing of the tunnel. The previously described ALIS (“AutopiLot using an Insect-based vision System”) model nicely matches the present behavioral findings. The ALIS model is based on a feedback control scheme that explains how honeybees may keep their speed proportional to the minimum local cross-section of a tunnel, based solely on optic flow processing, without any need for speedometers or rangefinders. The present behavioral findings suggest how flying insects may succeed in adjusting their speed in their complex foraging environments, while at the same time adjusting their distance not only from lateral and ventral objects but also from those located in their dorsal visual field

Taking Inspiration from Flying Insects to Navigate inside Buildings

These days, flying insects are seen as genuinely agile micro air vehicles fitted with smart sensors and also parsimonious in their use of brain resources. They are able to visually navigate in unpredictable and GPS-denied environments. Understanding how such tiny animals work would help engineers to figure out different issues relating to drone miniaturization and navigation inside buildings. To turn a drone of ~1 kg into a robot, miniaturized conventional avionics can be employed; however, this results in a loss of their flight autonomy. On the other hand, to turn a drone of a mass between ~1 g (or less) and ~500 g into a robot requires an innovative approach taking inspiration from flying insects both with regard to their flapping wing propulsion system and their sensory system based mainly on motion vision in order to avoid obstacles in three dimensions or to navigate on the basis of visual cues. This chapter will provide a snapshot of the current state of the art in the field of bioinspired optic flow sensors and optic flow-based direct feedback loops applied to micro air vehicles flying inside buildings

A fully-autonomous hovercraft inspired by bees: wall following and speed control in straight and tapered corridors

International audienceThe small autonomous vehicles of the future will have to navigate close to obstacles in highly unpredictable environments. Risky tasks of this kind may require novel sensors and control methods that differ from conventional approaches. Recent ethological findings have shown that complex navigation tasks such as obstacle avoidance and speed control are performed by flying insects on the basis of optic flow (OF) cues, although insects' compound eyes have a very poor spatial resolution. The present paper deals with the implementation of an optic flow-based autopilot on a fully autonomous hovercraft. Tests were performed on this small (878-gram) innovative robotic platform in straight and tapered corridors lined with natural panoramas. A bilateral OF regulator controls the robot's forward speed (up to 0.8 m/s), while a unilateral OF regulator controls the robot's clearance from the two walls. A micro-gyrometer and a tiny magnetic compass ensure that the hovercraft travels forward in the corridor without yawing. The lateral OFs are measured by two minimalist eyes mounted sideways opposite to each other. For the first time, the hovercraft was found to be capable of adjusting both its forward speed and its clearance from the walls, in both straight and tapered corridors, without requiring any distance or speed measurements}, that is, without any need for on-board rangefinders or tachometers

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