CUDA Spotlight: GPU-Accelerated Guidance and Control for Robotic Systems

This week’s CUDA Spotlight is on Jon Rogers of Texas A&M University. Jon is director of the Helicopter and Unmanned Systems Lab, where he works on new technologies for autonomous systems.

He is currently exploring new algorithms and sensing technologies to increase task complexity of robotic devices. His research encompasses the fields of non-linear dynamics, robust control, and high-performance computing.

NVIDIA: What problems has CUDA helped you solve?

Jon-Rogers-PhotoJon: CUDA has provided an entry point to GPU programming and execution that is highly compatible with our current guidance and control software. As we search for new ways to incorporate uncertainty quantification in real-time guidance laws, we are naturally drawn to GPU-based Monte Carlo due to its flexibility in handling nonlinear dynamics and non-Gaussian behavior.

We leverage CUDA primarily for parallel trajectory simulation, which means we have developed dynamic models for several vehicles (mostly aircraft) that run within a GPU kernel. Launching thousands of threads means we can run numerous dynamic simulations at once.

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