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

2025-06-26

AI Summary

Mobile robot navigation in crowded environments faces significant challenges due to high uncertainty in dynamic obstacle trajectories and stringent real-time requirements. Method: This paper proposes a dynamic risk-aware Model Predictive Path Integral (MPPI) control framework. Its core innovation integrates non-Gaussian stochastic trajectory prediction and joint multi-obstacle collision probability modeling into the MPPI framework, enabling millisecond-scale risk evaluation via efficient Monte Carlo approximation. A dual-mode decision mechanism—combining risk-threshold filtering and cost-weighted risk penalization—is introduced to mitigate the “freezing” problem. Contribution/Results: Extensive simulation and real-world experiments demonstrate that the proposed method significantly improves safety and efficiency over state-of-the-art baselines (S-MPC, Frenet-based planning, and standard MPPI): collision rate is reduced by 42%, and average traversal time is shortened by 28%.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Ensuring safe robot navigation among uncertain human trajectories
• Handling non-Gaussian predictions in real-time motion planning
• Reducing collision risks with dynamic obstacles efficiently

Innovation

• Dynamic Risk-Aware MPPI for safe navigation
• Monte Carlo approximation for collision probability
• Real-time non-Gaussian trajectory predictions