Bio-inspired optical flow circuits for the visual guidance of micro air vehicles

Ruffier, Franck; Viollet, Stéphane; Amic, S.; Franceschini, Nicolas

doi:10.1109/iscas.2003.1205152

Cited by 73 publications

(63 citation statements)

References 17 publications

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“…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. Similarly, specialized micro optic flow sensors have been used for altitude regulation in sub-1 g flying robots 70 .…”

Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,049

552

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Drones, a popular nickname for unmanned aerial vehicles and micro aerial vehicles, often conjure up images of unmanned aeroplanes that fly thousands of miles for espionage and to deploy munitions. However, over the past few years, an increasing number of public and private research laboratories have been working on small, human-friendly drones that one day may autonomously fly in confined spaces and in close proximity to people. The development of these small drones, which is the main focus of this Review, has been supported by the miniaturization and cost reduction of electronic components (microprocessors, sensors, batteries and wireless communication units), largely driven by the portable electronic device industry. These improvements have enabled the prototyping and commercialization of small (typically less than 1 kg) drones at smartphone prices.Small drones will have important socio-economic impacts (Fig. 1). Images from drones that are capable of flying a few metres above the ground will fill a gap between expensive, weather-dependent and lowresolution images provided by satellites and car-based images limited to human-level perspectives and the availability of accessible roads. Specialized flying cameras and cloud-based data analytics will allow farmers to continuously monitor the quality of crop growth. Such platforms will enable construction companies to measure work progress in real time. Drones will let mining companies obtain precise volumetric data of excavations. Energy and infrastructure companies will be able to exhaustively survey pipelines, roads and cables. Humanitarian organizations could immediately assess and adapt aid efforts in continuously changing refugee camps. Transportation drones that are capable of safely taking off and landing in the proximity of buildings and humans will allow developing countries -without a suitable road network -to rapidly deliver goods and to finally unleash the full potential of their e-commerce telecommunication infrastructure. Transportation drones will also help developed countries to improve the quality of service in congested or remote areas, and will enable rescue organizations to quickly deliver medical supplies in the field and on demand. Inspection drones that are capable of flying in confined spaces will help fire-fighting and emergency units to assess dangers faster and more safely, logistic companies to detect cracks in the inner and outer shells of ships, road maintenance companies to measure signs of wear and tear in bridges and tunnels, security companies to improve building safety by monitoring areas outside the range of surveillance cameras, and disaster mitigation agencies to inspect partially collapsed buildings where ground clutter is an obstacle for terrestrial robots. Coordinated teams of autonomous drones will enable missions that last longer than the flight time of a single drone by allowing some drones to temporarily leave the team for battery replacement. Drone teams will permit rescue organizations to quickly deploy dedicated commu...

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Section: Review Insightmentioning

confidence: 99%

Science, technology and the future of small autonomous drones

Floreano

Wood

2015

Nature

1,049

552

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“…This fly-inspired 2-pixel local motion sensor assesses the optic flow on the basis of the inverse of the delay between the detection of the same contrasting feature by two adjacent photoreceptors [24,43]. This 1D optic flow estimation method is largely invariant to contrast and spatial frequency [1].…”

Section: Optic Flow-based Visual Guidance Above An Unsteady Platformmentioning

confidence: 99%

“…During the last decade, some fixedand rotary-wing aerial robots have been equipped with similar means of detecting and processing the OF to those providing insects such as flies and bees with the vital cues they need to take off, follow a terrain and land (blimp: [19], fixed-wings: [8,[20][21][22], rotary-wings: [1,2,9,[23][24][25]), to hover (rotary-wings: [10,26,27]), and to track a moving target (rotary-wings: [27][28][29]). In aerial robots, OF processing methods were used in several ways as ameans of:…”

Section: Introductionmentioning

confidence: 99%

Optic Flow Regulation in Unsteady Environments: A Tethered MAV Achieves Terrain Following and Targeted Landing Over a Moving Platform

Ruffier

Franceschini

2014

J Intell Robot Syst

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The present study deals with the risky and daunting tasks of flying and landing in nonstationary environments. Using a two Degree-OfFreedom (DOF) tethered micro-air vehicle (MAV), we show the benefits of an autopilot dealing with a variable -the optic flow -which depends directly on two relative variables, the groundspeed and the groundheight. The micro-helicopter was shown to follow the ups and downs of a rotating platform that was also oscillated vertically. At no time did the MAV know in terms of ground height whether it was approaching the moving ground or whether the ground itself was rising to it dangerously. Nor did it know whether its current groundspeed was caused only by its forward thrust or whether it was partly due to the ground moving backwards or forwards. Furthermore, the MAV was shown to land safely on a platform set into motion along two directions, vertical and horizontal. This paper extends to non-stationary environments a former approach that introduced the principle of "optic flow regulation" for altitude control. Whereas in the former approach no requirement was set on the robot's landing target, the target's elevation angle was used here in a second feedback loop that gradually altered the robot's pitch and therefore its airspeed, leading to smooth landing in the vicinity of the target. Whether dealing with terrain following or landing, the MAV followed appropriately the unpredictable changes in the environment although it had no explicit knowledge of groundheight and groundspeed. The MAV did not make use of any rangefinders or velocimeters and was simply equipped with a 2-gram vision-based autopilot.

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“…Therefore, a second order fixed-point low pass filter was used to enhance the signal to noise ratio by removing the noise remaining at frequencies of more than 10 Hz. The algorithm called the "Time of travel scheme" implemented here consists mainly of a hysteresis thresholding process with separate ON and OFF pathways [5,39,40,42,45] followed by the ∆t computation, the result of which is fed into a corresponding table. Lastly, the five simultaneously computed optic flows ω m i are combined by the median operator in order to increase the robustness and the refresh rate of the output [40].…”

Section: % (Rmentioning

confidence: 99%

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

Sabiron

Chavent

Burlion

et al. 2013

Advances in Aerospace Guidance, Navigation and Control

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For the last few decades, growing interest has returned to the quite challenging task of the autonomous lunar landing. Soft landing of payloads on the lunar 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 autopilot 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 accurately 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. Secondly 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 preliminary results show that the optic flow measured despite the complex disturbances encountered closely matched the ground-truth optic flow.

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Bio-inspired optical flow circuits for the visual guidance of micro air vehicles

Cited by 73 publications

References 17 publications

Science, technology and the future of small autonomous drones

Science, technology and the future of small autonomous drones

Optic Flow Regulation in Unsteady Environments: A Tethered MAV Achieves Terrain Following and Targeted Landing Over a Moving Platform

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

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