Navigation Functionalities for an Autonomous UAV Helicopter

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106 APPENDIX A. Fig. 7. Position and heading estimates from the vision system vs. estimates based on YAS and precision RTK GPS observations. altitude [m] 1 0.8 0.6 0.4 0.2 pattern occluded by the shade of the helicopter last valid vision measurement filter raw vision altitude touch down 0 418 419 420 421 422 time [s] 423 424 425 Fig. 8. Altitude estimates from the navigation filter when losing vision. Fig. 9. Time histories and control modes during autonomous landing. The dashed lines show readings from a standard INS/DGPS unit. uncertainty (1σ). We found good agreement between measured and simulated errors. Fig. 7 shows timeplots for distance, altitude, and pitch angle at typical starting points for autonomous landing. Position and attitude estimates were provided with an average rate of 20 Hz (using 384×288 pixels images) and an average latency of 110 ms (including delays from capturing PAL signals). Some tens of autonomous landings were conducted from different relative positions to the landing pad within the specified envelope, on grass and snow fields, with different wind and illumination conditions. A sample of the results is available in Fig. 10 . The vertical velocity at touch down ranged between 18 and 35 cm/s, this corresponds to load factors of about 1.4 g on grass fields. The horizontal velocity at touch down was in the order of magnitude of 15 cm/s. The average touch down point precision was about 42 cm (13 % of rotor diameter). Thanks to the pan/tilt camera and a robust controller, considerable wind levels can be handled. Successful landings have been performed with wind levels on ground up to 30 km/h (2 min average), with gusts of 45 km/h.
A.3. PAPER III 107 Fig. 10. Flight test results from several autonomous landings. 6 Acknowledgements We thank Piotr Rudol and Mariusz Wzorek for providing the pan-tilt controller and Anke Knöppler for work on camera calibration. This work was supported by the Wallenberg Foundation, Sweden. References 1. P. Doherty. Advanced research with autonomous unmanned aerial vehicles. In Proc. of the 9th International Conference on the Principles of Knowledge Representation and Reasoning, pages 731–732, June 2004. 2. P. Doherty, P. Haslum, F. Heintz, T. Merz, T. Persson, and B. Wingman. A distributed architecture for autonomous unmanned aerial vehicle experimentation. In Proc. of the 7th International Symposium on Distributed Autonomous Robotic Systems, pages 221–230, June 2004. 3. Jean-Yves Bouguet. Camera Calibration Toolbox for Matlab . http://www.vision.caltech.edu/bouguetj/calib doc. 4. K. Kanatani. Geometric Computation for Machine Vision. Oxford University Press, 1995. 5. P.S. Maybeck. Stochastic models, estimation, and control. In Mathematics in Science and Engineering, volume 141-1, pages 289–367. Academic Press, 1979. 6. W.H. Press, S.A. Teukolsky, W.T. Vetterling, and B.P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, 1992. 7. S. Saripalli, J.F. Montgomery, and G. Sukhatme. Visually-guided landing of an unmanned aerial vehicle. IEEE Transactions on Robotics and Automation, 19(3):371–380, June 2003.
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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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4.5. CONCLUSIONS 53 1 + s 1 C(s) =
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Navigation Functionalities for an Autonomous UAV Helicopter

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