Luigi Graziosi
Control Algorithms and Hardware Accelerations for Unmanned Aerial Vehicles: Implementation and Numerical Precision Analysis for MPPI Acceleration.
Rel. Paolo Bernardi, Marcello Traiola, Marco Tognon. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2025
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Abstract
Control Algorithms and Hardware Accelerations for Unmanned Aerial Vehicles: Implementation and Numerical Precision Analysis for MPPI Acceleration This thesis describes the development and optimization of an advanced drone control system based on the Model Predictive Path Integral (MPPI) approach, with a focus on analyzing numerical precision for real-time applications on accelerated hardware. The work was conducted at the INRIA research center in France, in collaboration with the Rainbow team, which specializes in robotics, and the Taran team, focused on hardware research. Real-time control of autonomous systems represents one of the main challenges in modern robotics, as it requires both high computational efficiency and precision.
The primary objective of this work was to implement an MPPI controller on a quadrotor drone equipped with an NVIDIA Jetson Orin platform, leveraging GPU acceleration via CUDA and the JAX framework, to evaluate the impact of numerical precision on computational performance and control quality
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