The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Investigation of Air Transportation Technology at Princeton University, 1989-1990

The Air Transportation Technology Program at Princeton University proceeded along six avenues during the past year: microburst hazards to aircraft; machine-intelligent, fault tolerant flight control; computer aided heuristics for piloted flight; stochastic robustness for flight control systems; neural networks for flight control; and computer aided control system design. These topics are briefly discussed, and an annotated bibliography of publications that appeared between January 1989 and June 1990 is given

NASA space station automation: AI-based technology review

Research and Development projects in automation for the Space Station are discussed. Artificial Intelligence (AI) based automation technologies are planned to enhance crew safety through reduced need for EVA, increase crew productivity through the reduction of routine operations, increase space station autonomy, and augment space station capability through the use of teleoperation and robotics. AI technology will also be developed for the servicing of satellites at the Space Station, system monitoring and diagnosis, space manufacturing, and the assembly of large space structures

The 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies

This publication comprises the papers presented at the 1995 Goddard Conference on Space Applications of Artificial Intelligence and Emerging Information Technologies held at the NASA/Goddard Space Flight Center, Greenbelt, Maryland, on May 9-11, 1995. The purpose of this annual conference is to provide a forum in which current research and development directed at space applications of artificial intelligence can be presented and discussed

Onboard Mission- and Contingency Management based on Behavior Trees for Unmanned Aerial Vehicles

Unmanned Aerial Vehicles (UAVs) have gained significant attention for their potential in various sectors, including surveillance, logistics, and disaster management. This thesis focuses on developing a novel onboard mission and contingency management system based on Behavior Trees for UAVs. The study aims to assert if behavior trees can be effectively applied to this domain and how they perform with respect to other modelling architectures. Furthermore, this document explores which tree structures are more efficient, good-design practices and behavior tree limitations. Overall, this thesis addresses the challenge of autonomous onboard decision-making of UAVs in complex and dynamic environments, particularly in the context of delivery missions in off-shore wind farms. The developed architecture is tested in a simulated environment. The research integrates a Skill Manager, a Mission Planner, and a Mission and Contingency Manager. The architecture leverages Behavior Trees to facilitate both mission execution and contingency management. The thesis also presents a quantitative analysis of key performance indicators, providing a comparative evaluation against traditional architectures like Finite State Machines. The results indicate that the proposed system is efficient in mission execution and effective in handling contingencies. This study offers a comprehensive structure targeting onboard planning, contingency management and concurrent actions execution. It also presents a quantitative analysis of Behavior Trees' performance in UAV mission execution and reactivity to contingent situations. It contributes to the ongoing discourse on UAV autonomy, offering insights beneficial for the broader deployment of UAVs in various industrial applications

On the Implementation of Behavior Trees in Robotics

There is a growing interest in Behavior Trees (BTs) as a tool to describe and implement robot behaviors. BTs were devised in the video game industry and their adoption in robotics resulted in the development of ad-hoc libraries to design and execute BTs that fit complex robotics software architectures. While there is broad consensus on how BTs work, some characteristics rely on the implementation choices done in the specific software library used. In this letter, we outline practical aspects in the adoption of BTs and the solutions devised by the robotics community to fully exploit the advantages of BTs in real robots. We also overview the solutions proposed in open-source libraries used in robotics, we show how BTs fit in robotic software architecture, and we present a use case example