Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations

TL;DR

This paper introduces DRA-MPPI, a real-time motion planning method for mobile robots that accounts for uncertain obstacle trajectories using efficient Monte Carlo approximations, improving safety and avoiding robot freezing.

Contribution

The paper presents a novel risk-aware MPPI approach that efficiently estimates collision probabilities with non-Gaussian predictions, enabling safer robot navigation in dynamic crowds.

Findings

DRA-MPPI outperforms state-of-the-art methods in safety and efficiency.

Real-time Monte Carlo approximation enables handling hundreds of trajectories.

The approach reduces robot freezing in crowded environments.

Abstract

Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents' predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic predictions. By leveraging MPPI's gradient-free nature, we propose a method that efficiently approximates the joint Collision Probability (CP) among multiple dynamic obstacles for several hundred sampled trajectories in real-time via a Monte Carlo (MC) approach. This enables the rejection of samples exceeding a predefined CP threshold or the integration of CP as a weighted objective within the navigation cost function. Consequently, DRA-MPPI mitigates the freezing robot problem while enhancing safety. Real-world and simulated experiments with multiple dynamic obstacles demonstrate DRA-MPPI's superior performance compared to state-of-the-art approaches, including Scenario-based Model Predictive Control (S-MPC), Frenet planner, and vanilla MPPI.