The initial premise of the ITE approach is that for future autonomous flight, especially in the potentially difficult indoor environment of search and rescue (SAR) such as in a building fire, GNSS signal reception may be little to none. But most UAVs are equipped with GNSS and inertial, so aiding the inertial solution with a back-up system is preferred. ITE chose to use a monocular camera and a 2D laser rangefinder combined into a hybrid laser-camera sensor for navigation aiding.<\/p>\n
The camera and laser-range finder were initially calibrated by focusing from multiple different adjacent locations on one object, and so determining the attitude and translation between the two sensors. Basic navigation sans GNSS is established using the acceleration and angular rate information provided by the IMU, but inertial drift rapidly decreases accuracy, so aiding is essential.<\/p>\n The aiding solution has several components which are first integrated together. The camera sensor provides an initial \u201ckeyframe\u201d from which relative motion can be derived.<\/p>\n The next phase was to verify the initial performance of the inertial\/hybrid solution, by flying the UAV down a corridor towards a wall. Horizontal position began to degrade around 67 seconds.<\/p>\n The next more challenging demonstration involved transit down the corridor then into an adjacent room and leaving via a different exit. In addition, solutions using hybrid aiding and laser scanning aiding were evaluated.<\/p>\n The hybrid approach appeared to satisfy the anticipated test constraints very accurately with a deviation of about 0.8 during the 274 second flight, while the laser scanning approach had a horizontal error between start and end point of about 3.7. It was felt that the structured environment in the test rooms presented challenges for laser scanning and resulted in vertical variations coming from the dependence on the UAV\u2019s attitude, while the hybrid solution overcame these problems.<\/p>\n The conclusion from the testing was that the hybrid sensor performance was not limited by the structured test environment. So missions in more challenging environments could be better navigated in future with the hybrid system, compared to those where existing laser-scan-matching approaches would be used. The researchers intend to now focus on better perception of the test environment. For exploration missions, not only is accurate positioning crucial but also an accurate representation of the environment is necessary, for which the hybrid sensor is a promising tool.<\/p>\n Both research projects covered here were presented at ION ITM 2017 in Monterey, California.<\/span><\/p>\n Jamal Atman<\/strong>\u00a0and\u00a0Manuel Popp<\/strong>, Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Germany.\u00a0Gert F. Trommer<\/strong>, Institute of Systems Optimization (ITE), Karlsruhe Institute of Technology (KIT), Germany & ITMO University, St. Petersburg, Russia<\/p>\n Another project ITE has undertaken has been to increase the level of control of quadrotor drones by adding tiltable rotors and associated control systems. The object is to maintain a certain orientation of the UAV and its payload without altering platform attitude, to manage maneuvering more effectively and to compensate for disturbances faster and possibly enlarge the area of operation for rescue forces.<\/p>\n For fire disaster recovery, hovering multi-rotor UAVs can provide invaluable information within buildings, rather than risking the lives of first responders. Locating survivors or difficult to find fire sources using video transmitted by drones may save time and reduce exposure for critical personnel.<\/p>\n A two-part nonlinear control system has been implemented by ITE \u2014\u00a0the first part takes the measurements of the vehicle dynamics and connects these measurements to a back-stepping controller to generate the desired forces and torque to change vehicle motion.<\/p>\n At first the commanded signals have to be fed through a filter in order to provide smooth and continuous command signals and to produce the derivatives required by the control algorithm. The smoothed command signal is then used by an arbitrary controller to create vectors of required forces and torque to control the attitude and velocity of the vehicle.<\/p>\n Desired force and torque is fed into an adaptive and dynamic control allocation algorithm to generate the values for the actuators \u2013 there are four propulsion motor commands and four servo motor commands. The control allocation algorithm is an adaptive algorithm \u2013 used in order to adjust for changing situations and environments. For example, when flying in a hallway and near walls, ceiling or floor, flight characteristics change significantly due to different aerodynamic effects. On the other hand, outdoors flight behavior is usually much easier to manage as the only nonlinear behavior occurs relatively close to the ground.<\/p>\n![\"ITE-UAV\"](\"http:\/\/gpsworld.com\/wp-content\/uploads\/2017\/04\/ITE-UAV.jpg\")
<\/a><\/p>\n![\"Using](\"http:\/\/gpsworld.com\/wp-content\/uploads\/2017\/04\/Keyframe.jpg\")
<\/a><\/p>\n![\"Corridor](\"http:\/\/gpsworld.com\/wp-content\/uploads\/2017\/04\/Corridor-test.png\")
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\nAcknowledgments<\/h4>\n
\nImproved maneuverability<\/h3>\n
![\"Modified](\"http:\/\/gpsworld.com\/wp-content\/uploads\/2017\/04\/ModifiedUAV.jpg\")
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![\"3D](\"http:\/\/gpsworld.com\/wp-content\/uploads\/2017\/04\/3D-performance-1-300x204.png\")
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![\"3D](\"http:\/\/gpsworld.com\/wp-content\/uploads\/2017\/04\/3D-performance-2-300x204.png\")
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