DBaS-Log-MPPI: Efficient and Safe Trajectory Optimization via Barrier States

Authors: Fanxin Wang, Haolong Jiang, Chuyuan Tao, Wenbin Wan, Yikun Cheng

Published: 2025 (Conference Paper)

Source: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Algorithm: DBaS-Log-MPPI

arXiv: 2504.06437

DOI: 10.1109/IROS60139.2025.11245991

Summary

Integrates logarithmic barrier states (DBaS) into the dynamics, providing smooth cost shaping that discourages safety constraint violations.

Abstract

Optimizing trajectory costs for nonlinear control systems remains a significant challenge. Model Predictive Control (MPC), particularly sampling-based approaches such as the Model Predictive Path Integral (MPPI) method, has recently demonstrated considerable success by leveraging parallel computing to efficiently evaluate numerous trajectories. However, MPPI often struggles to balance safe navigation in constrained environments with effective exploration in open spaces, leading to infeasibility in cluttered conditions. To address these limitations, we propose DBaS-Log-MPPI, a novel algorithm that integrates Discrete Barrier States (DBaS) to ensure safety while enabling adaptive exploration with enhanced feasibility. Our method is efficiently validated through three simulation missions and one real-world experiment, involving a 2D quadrotor and a ground vehicle navigating through cluttered obstacles. We demonstrate that our algorithm surpasses both Vanilla MPPI and Log-MPPI, achieving higher success rates, lower tracking errors, and a conservative average speed.

Links

Primary

Paper arxiv.org

Standard

arXiv Abstract 2504.06437 arXiv PDF 2504.06437 arXiv HTML 2504.06437 DOI 10.1109/IROS60139.2025.11245991

Alternate

ieeexplore.ieee.org ieeexplore.ieee.org scholar.xjtlu.edu.cn scholar.xjtlu.edu.cn

Tags

• MPPI

• Trajectory optimization

• Safety

• Barrier states

• Constraint satisfaction