TR2024-050

Leveraging Computational Fluid Dynamics in UAV Motion Planning


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.

 

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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, Robotics
      Brief
      • 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.
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