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Model Predictive Path Integral Control: Theoretical Foundations and Applications to Autonomous Driving

Authors: Grady Williams

Published: 2019 (PhD Dissertation)

Source: Georgia Institute of Technology

Algorithm: MPPI

Summary

PhD dissertation by Grady Williams at Georgia Tech establishing the theoretical foundations of Model Predictive Path Integral (MPPI) control and demonstrating its application to autonomous vehicle navigation.

Abstract

This thesis presents a new approach for stochastic model predictive (optimal) control: model predictive path integral control, which is based on massive parallel sampling of control trajectories. We first show the theoretical foundations of model predictive path integral control, which are based on a combination of path integral control theory and an information theoretic interpretation of stochastic optimal control. We then apply the method to high speed autonomous driving on a 1/5 scale vehicle and analyze the performance and robustness of the method. Extensive experimental results are used to identify and solve key problems relating to robustness of the approach, which leads to a robust stochastic model predictive control algorithm capable of consistently pushing the limits of performance on the 1/5 scale vehicle.

Primary

Paper repository.gatech.edu

Tags

  • Model predictive path integral control

  • MPPI

  • Trajectory optimization

  • Autonomous driving

  • Stochastic optimal control

  • Path integral control