Navigation Functionalities for an Autonomous UAV Helicopter

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8 CHAPTER 2. OVERVIEW provides a powerful way to quickly test the correct functioning of all the components on the field, including the interface to the helicopter. The path following control mode (PFCM) enables the helicopter to follow a 3D path. It receives a geometric description of a path via a number of parameters from the path planner. From these parameters it generates a continuous description of the path, then it adds velocity and acceleration constraints in order to calculate the inputs to be sent to the helicopter. An algorithm which generates the proper control inputs at each control cycle has been developed. The PFCM also generates a number of events and other outputs which are necessary for monitoring purposes in flight. The two sensor fusion algorithms implemented are based on a Kalman filter and produce estimates of the helicopter state in terms of position, velocity and attitude. They are used to fuse the inertial sensors with the GPS, and the inertial sensors with the video camera during autonomous landing. Since they are based on the same dynamic model, only the results of the integration between inertial sensors and video camera will be presented. It will be shown how the integration of the video camera with inertial sensors can effectively improve the robustness of the helicopter state estimate compared to a solution obtained from image processing alone. 2.2 The UAV helicopter platform The Yamaha RMAX helicopter used for the experimentation (Fig. 2.2) has a total length of 3.6 m (including main rotor). It is powered by a 21 hp two-stroke engine and has a maximum takeoff weight of 95 kg. The RMAX rotor head is equipped with a Bell-Hiller stabilizer bar (see Fig.2.3). The effect of the Bell-Hiller mechanism is to reduce the response of the helicopter to wind gusts. Moreover, the stabilizer bar is used to generate a control augmentation to the main rotor cyclic input. The Bell-Hiller mechanism is very common in small-scale helicopters but quite uncommon in full-scale helicopters. The reason for this is that a small-scale helicopter experiences less rotor-induced damping compared to full-scale helicopters. Consequently, it is more difficult to control for a human pilot. It should be pointed out though that an electronic control system can stabilize a small-scale helicopter without a stabilizer bar quite
2.2. THE UAV HELICOPTER PLATFORM 9 Figure 2.2: The RMAX helicopter. efficiently. For this reason the trend for small-scale autonomous helicopters is to remove the stabilizer bar and let the digital control system stabilize the helicopter. The advantage in this case is reduced mechanical complexity in the system. Figure 2.3: The RMAX rotor head with Bell-Hiller mechanism.
Page 1: Linköping Studies in Science and T
Page 5: Acknowledgements This work would ha
Page 9 and 10: Contents 1 Introduction 1 2 Overvie
Page 11 and 12: Chapter 1 Introduction An Unmanned
Page 13 and 14: of the environment in order to avoi
Page 15 and 16: Chapter 2 Overview This chapter pre
Page 17: 2.1. UAV SOFTWARE ARCHITECTURE 7 wh
Page 21 and 22: 2.2. THE UAV HELICOPTER PLATFORM 11
Page 23 and 24: Chapter 3 Simulation 3.1 Introducti
Page 25 and 26: 3.2. HARDWARE-IN-THE-LOOP SIMULATIO
Page 27 and 28: 3.3. REFERENCE FRAMES 17 3.3 Refere
Page 29 and 30: 3.4. THE AUGMENTED RMAX DYNAMIC MOD
Page 31 and 32: 3.4. THE AUGMENTED RMAX DYNAMIC MOD
Page 33 and 34: 3.5. SIMULATION RESULTS 23 3.5 Simu
Page 35 and 36: 3.5. SIMULATION RESULTS 25 Figure 3
Page 37 and 38: 3.5. SIMULATION RESULTS 27 Figure 3
Page 39 and 40: Chapter 4 Path Following Control Mo
Page 41 and 42: 4.2. TRAJECTORY GENERATOR 31 Figure
Page 43 and 44: 4.2. TRAJECTORY GENERATOR 33 Fig. 4
Page 45 and 46: 4.2. TRAJECTORY GENERATOR 35 be use
Page 47 and 48: 4.2. TRAJECTORY GENERATOR 37 4.2.3
Page 49 and 50: 4.2. TRAJECTORY GENERATOR 39 radius
Page 51 and 52: 4.2. TRAJECTORY GENERATOR 41 Calcul
Page 55 and 56: 4.2. TRAJECTORY GENERATOR 45 by the
Page 59 and 60: 4.3. OUTER LOOP CONTROL EQUATIONS 4
Page 61 and 62: 4.4. EXPERIMENTAL RESULTS 51 Up [m]
Page 63 and 64: 4.5. CONCLUSIONS 53 1 + s 1 C(s) =
Page 65 and 66: 4.5. CONCLUSIONS 55 Vel [m/s] 20 18
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composed of three accelerometers an
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5.1. FILTER ARCHITECTURE 61 filter
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5.1. FILTER ARCHITECTURE 63 north,
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5.2. EXPERIMENTAL RESULTS 65 Figure
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5.2. EXPERIMENTAL RESULTS 67 Figure
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5.2. EXPERIMENTAL RESULTS 69 by the
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5.3. CONCLUSION 71 vision system st
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BIBLIOGRAPHY 73 [9] E. Frazzoli. Ro
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Appendix A 75
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A.1. PAPER I 77 is a three-dimensio
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A.1. PAPER I 79 The reference point
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A.1. PAPER I 81 Path Av Err Max Err
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A.2. PAPER II 83 From Motion Planni
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A.3. PAPER III 97 A.3 Paper III Aut
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A.3. PAPER III 99 the projection of
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A.3. PAPER III 101 yes Tbo > Tlim2
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A.3. PAPER III 103 immediately afte
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A.3. PAPER III 105 altitude [m] 20
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A.3. PAPER III 107 Fig. 10. Flight
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Department of Computer and Informat
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No 598 Rego Granlund: C 3 Fire - A
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No 1024 Aleksandra Tešanovic: Towa
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Navigation Functionalities for an Autonomous UAV Helicopter

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