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Published on : Jan 08, 2015
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Slide 1 - IST 2.6.1 Advanced Robotics µdrones µDRone autOnomous Navigation for Environment Sensing JM ALEXANDRE CEA List
Slide 2 - Objective of the project : AUTONOMOUS MICRO DRONE Development small size flying robot able to carry outdoor missions (urban environment) autonomously without human command for civil security applications - Autonomous localisation - Autonomous navigation - Robustness to unexpected events Sensors for security Vision Chemical Sensors for autonomy Vision GPS Inertial US/IR USABLE IN HUMAN ENVIRONMENT - Small size and weight Careenage
Slide 3 - µDrones presentation CONSORTIUM CEA List – Technology research institute (FR) Thales Security System – Industry (Fr) University of Tünbingen – Research institute (D) AirRobot – SME (D) Lisippos – SME (GR) Main developments - Algorithms for autonomous localisation and navigation Intuitive MMI for mission preparation and control Flying robot Evaluation in urban environment
Slide 4 - MCS – Mission Control System Man Machine Interface (mission preparation) Screen with (2D or 3D view of the urban environment (GPS navigation map), to define the trajectory that the drone will follow autonomously. Definition of the trajectory by defining way point Automatic planning of the trajectory Man Machine Interface (mission monitoring) Display a feedback of : Estimated position of the drone Data coming from the mission sensors ( video, chemical …) Stop the drone on an interest point and to servo the drone on this point or command with a joystick
Slide 5 - Perception and command for flight autonomy Attitude stabilization - attitude stabilization of the UAV using inertial sensor (Gyrometer and accelerometer MEMS) to counteract wind perturbation. Localization and navigation Performed by different sensors modalities, including GPS, vision, inertial sensors. Redundancy increases the reliability of the system (loss of information from one of the sensors. (GPS loss, bad vision feedback or too poor video contents). Obstacle avoidance Strategy of obstacle avoidance based on optic flow (measurement of time to collision).
Slide 6 - Final scenarios Two scenarios Crisis management scenario (    unknown  urban environment) Search and Rescue Car accidents Fight against terrorism Forest Fire Detection Inspection and monitoring of sensitive site (known environment) Nuclear power plant Chemical factory