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Meeting	ACGS Committee Meeting 98 - Williamsburg - October 2006
Agenda Location	7 SUBCOMMITTEE C - AVIONICS AND SYSTEM INTEGRATION 7.1 Vision-Based GNC and Decision Making for an Unmanned Helicopter
Title	Vision-Based GNC and Decision Making for an Unmanned Helicopter
Presenter	Frank Thielecke
Affiliation	DLR
Available Downloads*	presentation
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Abstract	The research presented in this talk intends to contribute towards the utilization of small UAVs in uncertain environments and towards the increase of vehicle autonomy to operate even under systems failures. For the demonstration of the developed concepts in the fields of vision-based GNC and on-board decision making, DLR’s VTOL UAV demonstrator ARTIS is used. Due to the poor quality of MEMS inertial sensors typically used in small UAVs, periods without GPS aiding cannot be bridged using unaided strapdown solution. Therefore, small, inexpensive and drift-free sensor alternatives are desired to compensate for GPS-failure. The first part of the presentation addresses the implementation and evaluation of a vision-based navigation filter for the Unmanned Aerial Vehicle ARTIS (Autonomous Rotorcraft Testbed for Intelligent Systems). The navigation filter uses vision data from ground feature tracking based on a Lucas-Kanade algorithm in order to compensate GPS-failure. An Extended Kalman Filter (EKF) is used for sensor data fusion. The developed algorithm handles data synchronisation and latency compensation. The filter is evaluated in Software in the Loop (SITL) simulation, in Hardware in the Loop (HITL) simulation and in flight test. Despite the inherent error accumulation the relative navigation approach allows the helicopter a notable area of operation. ARTIS performs stable flight in hover domain using the vision-based navigation approach. The results show the capability of the navigation algorithm to converge and to compensate GPS-failure. To handle more complex systems failures and to move the UAV in a partially known and dynamic environment, it is a key topic to increase the level of vehicle autonomy by onboard decision making capabilities. They allow the vehicle to perform its mission even when the system’s performance is degraded and the initial plan prepared offline is no more valid. Decision capabilities, which guaranty the adaptation of the vehicle behaviour, are implemented in cognitive architectures in order to close the loop of perception, situation evaluation, decision, and action. The capability to integrate environmental information via sensors and to evaluate the current state is indeed essential for the vehicle to assure its own safety and a minimum level of autonomy. The second part for this presentation will focus on system architectures for decision making and a design metaphor for the man-machine interface. In this context, some aspects of automatic sense and avoid will be discussed.