Visually guided micro-aerial vehicle: automatic take off, terrain following, landing and wind reaction

We have developed a visually based autopilot which is able to make a micro air vehicle (MAV) automatically take off, cruise and land, while reacting adequately to wind disturbances. We built a proof-of-concept, tethered rotorcraft that can travel indoors over an environment composed of contrasting features randomly arranged on the floor. Here we show the feasibility of a visuomotor control loop that acts upon the thrust so as to maintain the optic flow (OF) estimated in the downward direction to a reference value. The sensor involved in this OF regulator is an elementary motion detector (EMD). The functional structure of the EMD was inspired by that of the housefly, which was previously investigated at our laboratory by performing electrophysiological recordings while applying optical microstimuli to single photoreceptor cells of the compound eye. The vision based autopilot, which we have called OCTAVE (optic flow control system for aerospace vehicles) solves complex problems such as terrain following, controls risky maneuvers such as take off and landing and responds appropriately to wind disturbances. All these reputedly demanding tasks are performed with one and the same visuomotor control loop. The non-emissive sensor and simple processing system are particularly suitable for use with MAV, since the tolerated avionic payload of these micro-aircraft is only a few grams. OCTAVE autopilot could also contribute to relieve a remote operator from the lowly and difficult task of continuously piloting and guiding an UAV. It could also provide guiding assistance to pilots of manned aircraft.

[1]  Heinrich H. Bülthoff,et al.  Insect Inspired Visual Control of Translatory Flight , 2001, ECAL.

[2]  Fabrizio Mura,et al.  Visual control of altitude and speed in a flying agent , 1994 .

[3]  Nicolas H. Franceschini,et al.  Neuromorphic optical flow sensing for Nap-of-the-Earth flight , 1999, Optics East.

[4]  Zhang,et al.  Honeybee navigation en route to the goal: visual flight control and odometry , 1996, The Journal of experimental biology.

[5]  Werner Reichardt,et al.  Processing of optical data by organisms and by machines , 1969 .

[6]  Stéphane Viollet,et al.  Super-accurate Visual Control of an Aerial Minirobot , 2001 .

[7]  Stéphane Viollet,et al.  Visual control of two aerial micro-robots by insect-based autopilots , 2004, Adv. Robotics.

[8]  Rahul Sarpeshkar,et al.  Pulse-Based Analog VLSI Velocity Sensors , 1997 .

[9]  Franck Ruffier,et al.  OCTAVE: a bioinspired visuo-motor control system for the guidance of micro-air-vehicles , 2003, SPIE Microtechnologies.

[10]  W. Reichardt Movement perception in insects , 1969 .

[11]  Stefan Werner,et al.  Landmark navigation and autonomous landing approach with obstacle detection for aircraft , 1997, Defense, Security, and Sensing.

[12]  J. Kennedy,et al.  The migration of the Desert Locust (Schistocerca gregaria Forsk.) I. The behaviour of swarms. II. A theory of long-range migrations , 1951, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[13]  Robert J. Wood,et al.  Biomimetic sensor suite for flight control of a micromechanical flying insect: design and experimental results , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[14]  Mandyam V. Srinivasan,et al.  Landing Strategies in Honeybees and Applications to Uninhabited Airborne Vehicles , 2004, Int. J. Robotics Res..

[15]  Nicolas Franceschini,et al.  Visual Guidance Of A Mobile Robot Equipped With A Network Of Self-Motion Sensors , 1990, Other Conferences.

[16]  Hitoshi Yamada,et al.  Flying Robot with Biologically Inspired Vision , 2001, J. Robotics Mechatronics.

[17]  Martin Egelhaaf,et al.  Neural Mechanisms of Visual Course Control in Insects , 1989 .

[18]  Svetha Venkatesh,et al.  How honeybees make grazing landings on flat surfaces , 2000, Biological Cybernetics.

[19]  N. Franceschini,et al.  From insect vision to robot vision , 1992 .

[20]  Stéphane Viollet,et al.  Bio-inspired optical flow circuits for the visual guidance of micro air vehicles , 2003, Proceedings of the 2003 International Symposium on Circuits and Systems, 2003. ISCAS '03..

[21]  Alexa Riehle,et al.  Directionally Selective Motion Detection by Insect Neurons , 1989 .

[22]  H. Nalbach,et al.  Visual stabilization in arthropods. , 1993, Reviews of oculomotor research.

[23]  Jean-Marc Pichon Guidage visuel d'un robot mobile autonome d'inspiration bionique , 1991 .

[24]  S. Shankar Sastry,et al.  A vision system for landing an unmanned aerial vehicle , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[25]  Bruno Jouvencel,et al.  Avoidance of underwater cliffs for autonomous underwater vehicles , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[26]  Takeo Kanade,et al.  Vision-Based Autonomous Helicopter Research at Carnegie Mellon Robotics Institute 1991-1997 , 1998 .

[27]  C. David The relationship between body angle and flight speed in free‐flying Drosophila , 1978 .

[28]  Thomas Netter,et al.  A robotic aircraft that follows terrain using a neuromorphic eye , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Thomas J. Mueller,et al.  Optic Flow Sensors for MAV Navigation , 2001 .

[30]  Thomas J. Mueller,et al.  Fixed and Flapping Wing Aerodynamics for Micro Air Vehicle Applications , 2001 .