TR2024-050
Leveraging Computational Fluid Dynamics in UAV Motion Planning
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- "Leveraging Computational Fluid Dynamics in UAV Motion Planning", American Control Conference (ACC), DOI: 10.23919/ACC60939.2024.10644753, July 2024, pp. 375-381.BibTeX TR2024-050 PDF Video
- @inproceedings{Huang2024jul,
- author = {Huang, Yunshen and Greiff, Marcus and Vinod, Abraham P. and Di Cairano, Stefano}},
- title = {Leveraging Computational Fluid Dynamics in UAV Motion Planning},
- booktitle = {American Control Conference (ACC)},
- year = 2024,
- pages = {375--381},
- month = jul,
- publisher = {IEEE},
- doi = {10.23919/ACC60939.2024.10644753},
- url = {https://www.merl.com/publications/TR2024-050}
- }
,
- "Leveraging Computational Fluid Dynamics in UAV Motion Planning", American Control Conference (ACC), DOI: 10.23919/ACC60939.2024.10644753, July 2024, pp. 375-381.
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MERL Contacts:
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Research Areas:
Abstract:
We propose a motion planner for quadrotor unmanned aerial vehicles (UAVs) in windy environments, where the motion is defined by a sequence of Bézier curves in the flat output space of the UAV. The real-time implementable planner incorporates wind information from high-fidelity computational fluid dynamics simulations performed offline and utilizes convexity properties of Bézier curves to enable real-time implementations. For this purpose, we: (i) identify a model for the UAV-wind interaction; (ii) use the OpenFoam software to compute a model of the wind speeds subject to world geometry and boundary conditions; (iii) describe a method for regressing this wind model into a more compact representation; and finally (iv) demonstrate how this representation is amenable to minimum-snap motion planning of quad-rotor UAVs in realistic environments. We validate our approach using simulations and hardware experiments, and show a significant improvement in the thrust used by the UAV in presence of strong winds.
Related News & Events
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NEWS MERL researchers present 9 papers at ACC 2024 Date: July 10, 2024 - July 12, 2024
Where: Toronto, Canada
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.
As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.
In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
- MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.