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Generic Urban Air Mobility Simulation

This research presents a simulation framework for autonomous research for a UAM vehicle using the NASA Revolutionary Vertical Lift Technology Lift+Cruise concept vehicle. Our research results were produced using the open-source, six degree of freedom, rigid-body, nonlinear generic urban air mobility (GUAM) simulation. The intent of this paper is to demonstrate the GUAM simulation and a series of Challenge Problems that our researchers have posed to the broader autonomous vehicle research community. Our team has developed the GUAM simulation for the express purpose of providing a high-fidelity transition vehicle dynamics model to foster collaboration and algorithm performance comparison across research teams. In this paper, we demonstrate some of the autonomous flight research challenges and some of our current approaches to tackling basic autonomous flight tasks (e.g., trajectory following, stationary and moving obstacle avoidance). Additionally, we propose some flight metrics to assess autonomous algorithm performance while accomplishing these basic autonomous tasks.

autonomous flight

A Systems Approach to AI Model Integration and Performance Evaluation for the Generic UAM Simulation Framework

This paper introduces py-guam, an open-source experimentation framework developed for the NASA Generic Urban Air Mobility simulation (GUAM) environment, facilitating the integration and evaluation of advanced artificial intelligence (AI) algorithms. We present a systems approach which enables the seamless incorporation of data-driven models, including off-nominal and failure state detection, into the GUAM’s Cognitive Architecture (CA). The framework supports customizable experimentation parameters, derives Safety Performance Indicators (SPIs) from UL 4600 safety case analyses, and employs rapid UAM simulations to assess AI impacts on flight performance across diverse scenarios. Through comprehensive testing and validation experiments, we demonstrate GUAM’s capability to enhance safety and efficiency in urban air mobility operations. Additionally, the open-source nature of py-guam fosters community collaboration, ensuring continuous improvement and adaptability to evolving technological advancements. This work establishes a robust tool for developing and testing AI-driven urban air mobility (UAM) systems, advancing the safety and reliability of autonomous urban air vehicles.

Artificial Intelligence