4 citations · 5 across the 5 of their papers we have counts for
5 papers · 1 filter
Transformer-based Heuristic for Advanced Air Mobility Planning
Jun Xiang, Jun Chen
Safety is extremely important for urban flights of autonomous Unmanned Aerial Vehicles (UAVs). Risk-aware path planning is one of the most effective methods to guarantee the safety…
Landing Trajectory Prediction for UAS Based on Generative Adversarial Network
Jun Xiang, Drake Essick, Luiz Gonzalez Bautista +2
Models for trajectory prediction are an essential component of many advanced air mobility studies. These models help aircraft detect conflict and plan avoidance maneuvers, which is…
Learning Probabilistic Obstacle Spaces from Data-driven Uncertainty using Neural Networks
Jun Xiang, Jun Chen
Identifying the obstacle space is crucial for path planning. However, generating an accurate obstacle space remains a significant challenge due to various sources of uncertainty, i…
Data-driven Probabilistic Trajectory Learning with High Temporal Resolution in Terminal Airspace
Jun Xiang, Jun Chen
Predicting flight trajectories is a research area that holds significant merit. In this paper, we propose a data-driven learning framework, that leverages the predictive and featur…
Learning-accelerated A* Search for Risk-aware Path Planning
Jun Xiang, Junfei Xie, Jun Chen
Safety is a critical concern for urban flights of autonomous Unmanned Aerial Vehicles. In populated environments, risk should be accounted for to produce an effective and safe path…