987 research outputs found
A Traffic Control Framework for Uncrewed Aircraft Systems
The exponential growth of Advanced Air Mobility (AAM) services demands
assurances of safety in the airspace. This research a Traffic Control Framework
(TCF) for developing digital flight rules for Uncrewed Aircraft System (UAS)
flying in designated air corridors. The proposed TCF helps model, deploy, and
test UAS control, agents, regardless of their hardware configurations. This
paper investigates the importance of digital flight rules in preventing
collisions in the context of AAM. TCF is introduced as a platform for
developing strategies for managing traffic towards enhanced autonomy in the
airspace. It allows for assessment and evaluation of autonomous navigation,
route planning, obstacle avoidance, and adaptive decision making for UAS. It
also allows for the introduction and evaluation of advance technologies
Artificial Intelligence (AI) and Machine Learning (ML) in a simulation
environment before deploying them in the real world. TCF can be used as a tool
for comprehensive UAS traffic analysis, including KPI measurements. It offers
flexibility for further testing and deployment laying the foundation for
improved airspace safety - a vital aspect of UAS technological advancement.
Finally, this papers demonstrates the capabilities of the proposed TCF in
managing UAS traffic at intersections and its impact on overall traffic flow in
air corridors, noting the bottlenecks and the inverse relationship safety and
traffic volume.Comment: 6 pages, 7 figure
An information theoretic approach for generating an aircraft avoidance Markov decision process
Developing a collision avoidance system that can meet safety standards required of commercial aviation is challenging. A dynamic programming approach to collision avoidance has been developed to optimize and generate logics that are robust to the complex dynamics of the national airspace. The current approach represents the aircraft avoidance problem as Markov Decision Processes and independently optimizes a horizontal and vertical maneuver avoidance logics. This is a result of the current memory requirements for each logic, simply combining the logics will result in a significantly larger representation. The "curse of dimensionality" makes it computationally inefficient and unfeasible to optimize this larger representation. However, existing and future collision avoidance systems have mostly defined the decision process by hand.
In response, a simulation-based framework was built to better understand how each potential state quantifies the aircraft avoidance problem with regards to safety and operational components. The framework leverages recent advances in signals processing and database, while enabling the highest fidelity analysis of Monte Carlo aircraft encounter simulations to date. This framework enabled the calculation of how well each state of the decision process quantifies the collision risk and the associated memory requirements. Using this analysis, a collision avoidance logic that leverages both horizontal and vertical actions was built and optimized using this simulation based approach
- …