用动态障碍函数提升复杂环境下的轨迹优化安全性与效率
EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
- 引入渐进式障碍函数，避免采样效率低导致的性能崩溃
- 在2D避障和3D水下机械臂上实现更低代价解，计算耗时少一个数量级
- 无需昂贵投影操作，适合高约束场景的实时安全轨迹规划
我们提出通过引入受内点法启发的动态障碍函数，对基于模型的扩散算法施加约束。研究表明，标准的基于模型的扩散算法在高度约束环境下（即使在简单的2维系统中）也会因分数函数蒙特卡洛近似采样效率低下而导致灾难性性能下降。为此，我们提出新兴障碍模型扩散（EB-MBD），通过逐步引入障碍约束规避此类问题，显著提升解的质量，且无需昂贵的投影操作。我们分析了每轮迭代中样本的存活性，以指导障碍参数调度。实验验证了该方法在2维避障和3维水下机械臂系统中的有效性，结果表明其相比基于模型的扩散获得更低代价解，且计算时间比投影类方法减少一个数量级。
原文摘要 · Abstract (English)
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We demonstrate that the standard Model-Based Diffusion algorithm can lead to catastrophic performance degradation in highly constrained environments, even on simple 2D systems due to sample inefficiency in the Monte Carlo approximation of the score function. We introduce Emerging-Barrier Model-Based Diffusion (EB-MBD) which uses progressively introduced barrier constraints to avoid these problems, significantly improving solution quality, without expensive projection operations such as projections. We analyze the sampling liveliness of samples at each iteration to inform barrier parameter scheduling choice. We demonstrate results for 2D collision avoidance and a 3D underwater manipulator system and show that our method achieves lower cost solutions than Model-Based Diffusion, and requires orders of magnitude less computation time than projection based methods.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。