Is this project an undergraduate, graduate, or faculty project?
Graduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Edison Alberto Martinez Samaniego, Graduate student Natalie Alexander, Graduate student Kaelyn Weddle, Graduate student
Lead Presenter's Name
Edison Alberto Martinez Samaniego
Lead Presenter's College
DB College of Engineering
Faculty Mentor Name
Dr. Gamage Dumindu Samaraweera
Abstract
Learning Motion Primitive Selection and Environment Abstraction Advanced Air Mobility (AAM) is emerging as a transformative solution for short and medium range transportation; however, it introduces an operational model that differs significantly from conventional aviation. AAM vehicles are expected to operate closer to populated areas, with increased autonomy, in dense urban and suburban environments. These settings present constrained maneuvering conditions which highlights the importance of maintaining safe operation under degraded flight conditions. Abnormal conditions may endanger onboard passengers, people on the ground, and surrounding infrastructure, making rapid detection and mitigation essential to prevent loss of control. Recent research has explored frameworks that enhance operational safety by explicitly integrating flight envelope limits in the real-time trajectory replanning. Classical sampling-based planners (e.g., RRT/RRT*) typically construct trees using straight line connections, followed by post processing techniques such as smoothing the path with Dubins airplane motion primitives to ensure dynamic feasibility. On the other hand, using motion primitives directly during planning ensures feasibility but introduces significant computational cost. Computing a Dubins airplane motion primitive requires evaluating approximately 30 candidate trajectories and selecting the solution with the lowest cost. This project proposes a data-driven framework to improve planning efficiency while preserving trajectory quality. Supervised learning models, including Random Forests, Neural Networks, and Naive Bayes classifiers, will predict the most promising motion primitives between aircraft states, reducing the number of evaluations. Additionally, clustering techniques such as DBSCAN will be applied to spatial obstacle data to reduce collision-checking complexity in dense environments. Using datasets generated from an existing trajectory planner, the expected outcomes include reduced planning time, decreased number of motion primitive evaluations, and minimal deviation in trajectory cost compared to baseline methods. This work investigates how data mining methods can enable real-time, dynamically feasible trajectory generation for AAM operations under constrained flight environments.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Learning Motion Primitive Selection and Environment Abstraction
Learning Motion Primitive Selection and Environment Abstraction Advanced Air Mobility (AAM) is emerging as a transformative solution for short and medium range transportation; however, it introduces an operational model that differs significantly from conventional aviation. AAM vehicles are expected to operate closer to populated areas, with increased autonomy, in dense urban and suburban environments. These settings present constrained maneuvering conditions which highlights the importance of maintaining safe operation under degraded flight conditions. Abnormal conditions may endanger onboard passengers, people on the ground, and surrounding infrastructure, making rapid detection and mitigation essential to prevent loss of control. Recent research has explored frameworks that enhance operational safety by explicitly integrating flight envelope limits in the real-time trajectory replanning. Classical sampling-based planners (e.g., RRT/RRT*) typically construct trees using straight line connections, followed by post processing techniques such as smoothing the path with Dubins airplane motion primitives to ensure dynamic feasibility. On the other hand, using motion primitives directly during planning ensures feasibility but introduces significant computational cost. Computing a Dubins airplane motion primitive requires evaluating approximately 30 candidate trajectories and selecting the solution with the lowest cost. This project proposes a data-driven framework to improve planning efficiency while preserving trajectory quality. Supervised learning models, including Random Forests, Neural Networks, and Naive Bayes classifiers, will predict the most promising motion primitives between aircraft states, reducing the number of evaluations. Additionally, clustering techniques such as DBSCAN will be applied to spatial obstacle data to reduce collision-checking complexity in dense environments. Using datasets generated from an existing trajectory planner, the expected outcomes include reduced planning time, decreased number of motion primitive evaluations, and minimal deviation in trajectory cost compared to baseline methods. This work investigates how data mining methods can enable real-time, dynamically feasible trajectory generation for AAM operations under constrained flight environments.