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

2025-01-15

AI Summary

Safe robot navigation in dynamic uncertain environments—characterized by non-stationary obstacles and modeling uncertainties—remains challenging due to the difficulty of enforcing safety constraints under stochastic dynamics. Method: This paper proposes C2U-MPPI, a novel framework that integrates non-convex nonlinear chance constraints directly into sampling-based Model Predictive Control (MPC) for the first time. It employs unscented transform sampling and hierarchical dynamic obstacle modeling to achieve deterministic constraint reconstruction, and introduces a risk-sensitive trajectory evaluation mechanism. Contribution/Results: C2U-MPPI ensures dynamical feasibility for nonlinear systems while significantly improving obstacle avoidance robustness and real-time performance. Extensive simulations and real-world human–robot coexistence experiments demonstrate that C2U-MPPI outperforms mainstream baseline methods in trajectory safety, computational efficiency, and capability to navigate complex multi-obstacle dynamic scenarios.

Technology Category

• Intelligent Robots: State Estimation
• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Navigating safely in dynamic and uncertain environments is challenging due to uncertainties in perception and motion. This letter presents C2U-MPPI, a robust sampling-based Model Predictive Control (MPC) framework that addresses these challenges by leveraging the Unscented Model Predictive Path Integral (U-MPPI) control strategy with integrated probabilistic chance constraints, ensuring more reliable and efficient navigation under uncertainty. Unlike gradient-based MPC methods, our approach (i) avoids linearization of system dynamics and directly applies non-convex and nonlinear chance constraints, enabling more accurate and flexible optimization, and (ii) enhances computational efficiency by reformulating probabilistic constraints into a deterministic form 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

Problem

• Robot Navigation
• Dynamic Obstacle Avoidance
• Environmental Uncertainty

Innovation

• C2U-MPPI
• probabilistic rules
• dynamic obstacle handling