Navigation Functionalities for an Autonomous UAV Helicopter

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94 APPENDIX A. From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle Figure 13: Mission 1. Images captured during the mission. Clockwise, starting from the upper left corner of the figure: the North, West, South and Top view of the building. on-board. As the helicopter was reaching successive hovering positions in front of each facade and over the building roof, the camera was controlled autonomously to keep the object of interest in the center of the image. This kind of mission was performed several times with different buildings chosen as observation targets. The logged flight-test data of one of the missions is plotted on the map in Fig. 12. Fig. 13 presents several frames taken from video footage from the mission demonstrations. The second mission demonstrates the use of the dynamic replanning capability of the framework. The flight started with autonomous take off, and the helicopter began executing the planned path towards the designated waypoints. After arriving at the first one, the direction of flight changed to south and the ground operator added a no-fly zone intersecting the flight path. 21 th Bristol UAV Systems Conference — April 2006 The information was sent to the helicopter and the on-board system activated the replanning mechanism. A new path was planned, and the flight continued avoiding the no-fly zone. After the helicopter arrived at the last waypoint, it was commanded to return to home base and land. Fig. 14 shows the logged flight-test data superimposed on the map of the area. 9 Conclusions and Future Work A distributed hardware/software architecture has been described which includes a framework for integrating path planning techniques, a path following control mode, and a path execution mechanism which allow for UAV operation in obstacle-cluttered environments in addition to dynamic replanning of flight paths. The
A.2. PAPER II 95 From Motion Planning to Control - A Navigation Framework for an Autonomous Unmanned Aerial Vehicle Figure 14: Mission 2. White solid arrow designates take off and landing position. Solid line represents the actual flight path executed counter-clockwise. Gray polygonal area marks the no-fly zone added during the flight by the ground operator. Black dotted line shows invalidated part of the path. path planning algorithms are based on the use of sample-based probabilistic techniques which sacrifice completeness in plan generation for tractability. Details of the path following controller are provided in addition to experimental results using the framework. These results show that the controller keeps the UAV within one meter of the desired path during flights with a velocity of 10 m/s. The proposed method for 3D trajectory execution views flight paths as sequences of segments where each segment is incrementally fed to the path following controller. This scheme supports dynamic replanning and replacement of flight path segments (e.g. because of adding no-fly zones). It also enhances the safety of UAV operations. The self-contained PFC computer’s role is to request subsequent segments as a mission flight path is being flown. In cases where the new segments do not arrive on time, the PFC automatically switches to a hover control mode. This approach would help to avoid situations where a longer path which is passed to the controller becomes unacceptable as time progresses, but due to communication failure with the rest of the system, is not made aware of the problem. This could lead to potentially catastrophic situations. The systems and techniques described here have been implemented and fully tested on our WITAS UAV systems. The framework proposed allows for the seamless coexistence of the hard real-time Control Kernel and the soft real-time high-level deliberative 21 th Bristol UAV Systems Conference — April 2006 system by taking advantage of timing and other characteristics of both. The Control Kernel is in charge of flight mode switching of the hybrid control system and coordinating the real-time communication among other things. It uses HCSMs to do this. The control kernel is easily extendible and allows for the implementation of additional functionality requiring a rigorous timing regime. The use of CORBA provides a straightforward means of transparently distributing deliberative services such as task- and motion planners across different processors in a single platform, onto other airborne platforms or onto different ground control stations. Task procedures are used in the implementation of reactive behaviors which provide the software and conceptual glue between the lower control layer and the upper deliberative layer in the architecture. Because CORBA is being used, both reactive behaviors and deliberative functionalities can be implemented in many different languages as long as they have supporting IDL mappings. Future work includes extending two of the services used in the dynamic replanning technique, namely, the Strategy Selector and the Strategy Library services. On the hardware side, inclusion of additional sensors necessary for perceiving the environment reactively are planned. In order to enhance the autonomy of the platform, the need for a 3D elevation map requirement must be loosened. This can be achieved by adding sensors enabling mapping and obstacle avoidance in unknown and dynamic environments. A new and enhanced version of the Control Kernel is also under evaluation. Among other features, it introduces data flow support into the state machine concept. 10 Acknowledgements This work is funded by the Wallenberg Project under the WITAS UAV Project. Many members of the WITAS UAV project helped to make this work possible. References 1. MARVIN: TU Berlin. http://pdv.cs.tu-berlin.de/MARVIN/. 2. M. La Civita. Integrated Modeling and Robust Control for Full-Envelope Flight of Robotic
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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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Navigation Functionalities for an Autonomous UAV Helicopter

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