Navigation Functionalities for an Autonomous UAV Helicopter

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104 APPENDIX A. Fig. 5. Mode sequence leading to touch down. Note that all transitions are onedirectional: once a mode is exited it can not be re-entered. throttle command is ramped down, inducing loss of lift and touch down. The control laws of the helicopter consist of an inner loop (pitch, roll and yaw angle control, and vertical velocity control) and an outer loop (position and velocity control). The inner loop consists of the Yamaha Attitude Control System (YACS), the properties of which have been identified with a dedicated system identification session. The control equations of the outer loop can be summarized as following: θC = KpxδX + KpvxδVX + KivxδVXsum ∆φC = KpyδY + KpvyδVY + KivyδVY sum VZC = KivzδVZsum + Kpvz(VZtarget − VZ) (6) VZtarget = limit(0.75δZ, VZmin, VZmax) ωC = limit(Kpwδψ, −26 deg/s, 26 deg/s) where the subscripted K are control gains, the δ are control errors, the subscript sum indicates the integral terms, θC is the commanded pitch angle, ∆φC is the commanded roll angle variation, ωC is the commanded yaw rate and VZC is the commanded vertical velocity 2 . 5 Experimental Results The helicopter used for experimentation is a slightly modified Yamaha RMAX (Fig. 1). It has a total length of 3.6 m (incl. main rotor) and a take-off weight 2 During the descent and touch down phases, the gains of the velocity terms (Kpvx and Kpvx) are increased by one fifth and the integral terms in the horizontal control are activated, for faster and more precise position control.
A.3. PAPER III 105 altitude [m] 20 18 16 14 12 10 8 6 4 2 0 0 2 4 6 8 10 12 14 16 18 20 distance to pattern [m] [mm] 400 350 300 250 200 150 100 50 altitude [m] 20 18 16 14 12 10 8 6 4 2 0 0 2 4 6 8 10 12 14 16 18 20 distance to pattern [m] Fig. 6. RMS error in horizontal (left) and vertical position (right) from simulation. of 95 kg, including 30 kg available for payload. The vision navigation system consists of two PC104 stacks with PIII 700 MHz processors, the inertial sensors of the YAS, and a single standard CCD camera with approx. 45 degrees horizontal angle of view which is mounted on an off-the-shelf pan/tilt unit (PTU). One of the two computers is dedicated to sensor management and low level control of the helicopter, the other one for image processing and control of the camera and the PTU. The two computers communicate over a RS232C serial link. They are built inside a shock and vibration isolated box, which also includes a precision GPS, a barometric altitude sensor, a compass, a video recorder, a video transmitter, and a wireless Ethernet bridge. The PTU is mechanically limited to 111 degrees tilt and ±180 degrees pan, the max. angular rate is 300 degrees/s and the resolution 0.051 degrees. It is mounted on a vibration isolating platform on the underside of the helicopter body. We estimated the RMS error of the vision system in position and attitude depending on the relative position to the pattern in leveled flight. For each position 1000 samples were generated using a feature noise model that included noise from image formation, digitization, and segmentation. We developed a method to analyze noise in ellipse center position and semi-axis length. Errors introduced by the transformation from the camera frame into the body frame were not considered in simulation. Fig. 6 shows the RMS errors (1σ) in horizontal and vertical position. The error doesn’t change much in the simulated envelope (a 20 m radius hemisphere, with a ”blind” sector from azimuth 0 to 15 degrees) due to different triplet sizes and sub-pixel feature extraction. For pitch and roll the error behaves similar to the horizontal error, with a maximum RMS value of ≈1 degree, the error in heading is negligible. The actual accuracy of the vision system was evaluated through an inflight comparison with a navigation solution based on the YAS and a precision RTK GPS which supplied horizontal/vertical position with 10 mm/15 mm [mm] 30 25 20 15 10 5
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Linköping Studies in Science and T
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Acknowledgements This work would ha
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Contents 1 Introduction 1 2 Overvie
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Chapter 1 Introduction An Unmanned
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of the environment in order to avoi
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Chapter 2 Overview This chapter pre
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2.1. UAV SOFTWARE ARCHITECTURE 7 wh
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2.2. THE UAV HELICOPTER PLATFORM 9
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2.2. THE UAV HELICOPTER PLATFORM 11
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Chapter 3 Simulation 3.1 Introducti
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3.2. HARDWARE-IN-THE-LOOP SIMULATIO
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3.3. REFERENCE FRAMES 17 3.3 Refere
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3.4. THE AUGMENTED RMAX DYNAMIC MOD
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3.4. THE AUGMENTED RMAX DYNAMIC MOD
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3.5. SIMULATION RESULTS 23 3.5 Simu
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3.5. SIMULATION RESULTS 25 Figure 3
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3.5. SIMULATION RESULTS 27 Figure 3
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Chapter 4 Path Following Control Mo
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4.2. TRAJECTORY GENERATOR 31 Figure
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4.2. TRAJECTORY GENERATOR 33 Fig. 4
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4.2. TRAJECTORY GENERATOR 35 be use
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4.2. TRAJECTORY GENERATOR 37 4.2.3
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4.2. TRAJECTORY GENERATOR 39 radius
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4.2. TRAJECTORY GENERATOR 41 Calcul
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4.2. TRAJECTORY GENERATOR 45 by the
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4.3. OUTER LOOP CONTROL EQUATIONS 4
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4.4. EXPERIMENTAL RESULTS 51 Up [m]
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Navigation Functionalities for an Autonomous UAV Helicopter

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