Development and Test of Highly Autonomous Unmanned Aerial Vehicles

Published in Journal of Aerospace Computing, Information, and Communication, Vol. 1, Issue 12, December 2004.This paper describes the design, development, and testing of Unmanned Aerial Vehicles (UAV) with highly automated search capabilities. Here, systems are able to respond on their own in the presence of considerable uncertainty utilizing an image processor, tracker/mapper, mission manager, and trajectory generation; and are used to complete a realistic benchmark reconnaissance mission. Subsequent to the selection of the search area, all functions are automated and human operator assistance is not required. The applications of these capabilities include reduction of operator workload in operational UAV systems, new UAV or guided-munition missions conducted without the assistance or availability of human operators, or the enhancement/augmentation of human search capabilities. The resulting system was able to search the 15-building village automatically with speed comparable to a human operator searching on foot or with a conventional remotely piloted vehicle. It was successful in 6 of 7 actual flights over the McKenna Military Operations in Urban Terrain test site over two different days and a variety of lighting conditions and choice of desired building

Sense and Avoid Characterization of the Independent Configurable Architecture for Reliable Operations of Unmanned Systems

AbstractIndependent Configurable Architecture for Reliable Operations of Unmanned Systems (ICAROUS) is a distributed software architecture developed by NASA Langley Research Center to enable safe autonomous UAS operations. ICAROUS consists of a collection formally verified core algorithms for path planning, traffic avoidance, geofence handling, and decision making that interface with an autopilot system through a publisher-subscriber middleware. The ICAROUS Sense and Avoid Characterization (ISAAC) test was designed to evaluate the performance of the onboard Sense and Avoid (SAA) capability to detect potential conflicts with other aircraft and autonomously maneuver to avoid collisions, while remaining within the airspace boundaries of the mission. The ISAAC tests evaluated the impact of separation distances and alerting times on SAA performance. A preliminary analysis of the effects of each parameter on key measures of performance is conducted, informing the choice of appropriate parameter values for different small Unmanned Aircraft Systems (sUAS) applications. Furthermore, low-power Automatic Dependent Surveillance Broadcast (ADS-B) is evaluated for potential use to enable autonomous sUAS to sUAS deconflictions as well as to provide usable warnings for manned aircraft without saturating the frequency spectrum

Vehicle to Vehicle (V2V) Communication for Collision Avoidance for Multi-Copters Flying in UTM -TCL4

NASAs UAS Traffic management (UTM) research initiative is aimed at identifying requirements for safe autonomous operations of UAS operating in dense urban environments. For complete autonomous operations vehicle to vehicle (V2V) communications has been identified as an essential tool. In this paper we simulate a complete urban operations in an high fidelity simulation environment. We design a V2V communication protocol and all the vehicles participating communicate over this system. We show how V2V communication can be used for finding feasible, collision-free paths for multi agent systems. Different collision avoidance schemes are explored and an end to end simulation study shows the use of V2V communication for UTM TCL4 deployment

GA tuning of pitch controller for small scale MAVs

The paper presents the application of intelligent tuning methods for the control of a prototype MAV in order to address problems associated with bandwidth limited actuators and gust alleviation. Specifically, as a proof of concept, the investigation is focused on the pitch control of a MAV. The work is supported by experimental results from wind tunnel testing that shows the merits of the use of Genetic Algorithm (GA) tuning techniques compared to classical, empirical tuning methodologies. To provide a measure of relative merit, the controller responses are evaluated using the ITAE performance index. In this way, the proposed method is shown to induce far superior dynamic performance compared to traditional approaches