Risk-aware MPPI for Stochastic Hybrid Systems

TL;DR

This paper introduces a risk-aware MPPI approach using Unscented Transform for better path planning in stochastic hybrid systems, especially for mobile robots navigating dynamic environments.

Contribution

It proposes a novel MPPI variant that captures stochasticity in hybrid systems using Unscented Transform, improving path planning accuracy and safety.

Findings

Faster convergence to goal in simulations.

Reduced collisions with dynamic agents.

Enhanced handling of stochastic hybrid dynamics.

Abstract

Path Planning for stochastic hybrid systems presents a unique challenge of predicting distributions of future states subject to a state-dependent dynamics switching function. In this work, we propose a variant of Model Predictive Path Integral Control (MPPI) to plan kinodynamic paths for such systems. Monte Carlo may be inaccurate when few samples are chosen to predict future states under state-dependent disturbances. We employ recently proposed Unscented Transform-based methods to capture stochasticity in the states as well as the state-dependent switching surfaces. This is in contrast to previous works that perform switching based only on the mean of predicted states. We focus our motion planning application on the navigation of a mobile robot in the presence of dynamically moving agents whose responses are based on sensor-constrained attention zones. We evaluate our framework on a simulated mobile robot and show faster convergence to a goal without collisions when the robot exploits the hybrid human dynamics versus when it does not.