Chance-Constrained Sampling-Based MPC for Collision Avoidance in Uncertain Dynamic Environments

TL;DR

This paper introduces a robust sampling-based MPC framework that effectively handles uncertainties in dynamic environments for collision avoidance, improving safety and efficiency in real-time navigation.

Contribution

It presents the C2U-MPPI framework that integrates probabilistic chance constraints with unscented sampling, avoiding linearization and enabling real-time, reliable obstacle avoidance.

Findings

Successfully navigates dynamic environments with multiple obstacles

Outperforms baseline methods in safety and efficiency

Validates effectiveness through simulated and real-world experiments

Abstract

Navigating safely in dynamic and uncertain environments is challenging due to uncertainties in perception and motion. This letter presents the Chance-Constrained Unscented Model Predictive Path Integral (C2U-MPPI) framework, a robust sampling-based Model Predictive Control (MPC) algorithm that addresses these challenges by leveraging the U-MPPI control strategy with integrated probabilistic chance constraints, enabling more reliable and efficient navigation under uncertainty. Unlike gradient-based MPC methods, our approach (i) avoids linearization of system dynamics by directly applying non-convex and nonlinear chance constraints, enabling more accurate and flexible optimization, and (ii) enhances computational efficiency by leveraging a deterministic form of probabilistic constraints and employing a layered dynamic obstacle representation, enabling real-time handling of multiple obstacles. Extensive experiments in simulated and real-world human-shared environments validate the effectiveness of our algorithm against baseline methods, showcasing its capability to generate feasible trajectories and control inputs that adhere to system dynamics and constraints in dynamic settings, enabled by unscented-based sampling strategy and risk-sensitive trajectory evaluation. A supplementary video is available at: https://youtu.be/FptAhvJlQm8.