用离散屏障状态提升机器人轨迹优化的安全性与效率
DBaS-Log-MPPI: Efficient and Safe Trajectory Optimization via Barrier States
- 引入离散屏障状态机制，实时保障复杂环境中的导航安全
- 在仿真与真实场景中均实现更高成功率和更低跟踪误差
- 适合需兼顾安全与探索能力的自主导航系统研究者
非线性控制系统轨迹优化仍是重大挑战。基于采样的模型预测控制（如MPPI）虽借助并行计算高效评估多条轨迹，但在受限环境中难以兼顾安全与探索，导致在密集障碍物场景中不可行。为此，我们提出DBaS-Log-MPPI算法，通过引入离散屏障状态（DBaS）确保安全的同时，实现自适应探索与更高可行性。该方法在三个仿真任务及一次真实实验中验证，涵盖2D四旋翼与地面车辆穿越复杂障碍物场景。结果表明，该算法优于原始MPPI与Log-MPPI，成功率达更高，跟踪误差更小，平均速度更保守但更安全。
原文摘要 · Abstract (English)
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.
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