双向滚动聚类增强路径积分，让机器人更稳更快抵达目标
BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization
- 用正反向滚动预测连接起点与终点，提升路径连通性
- 在900次仿真中成功率更高，计算时间与现有方法相当
- 适合需要高可靠路径规划的自主导航系统
本文提出一种新型轨迹优化方法——双向聚类MPPI（BiC-MPPI），旨在提升模型预测路径积分（MPPI）框架中的目标导向能力。该方法引入双向动力学近似和新的引导代价机制，通过前向与后向滚动，有效连接初始与终端状态，引导代价则帮助发现动态可行路径。实验表明，在修改后的BARN数据集上，900次仿真中BiC-MPPI在二维与三维环境中均优于现有MPPI变体，具有更高的任务成功率，且计算耗时保持竞争力。
原文摘要 · Abstract (English)
This paper introduces the Bidirectional Clustered MPPI (BiC-MPPI) algorithm, a novel trajectory optimization method aimed at enhancing goal-directed guidance within the Model Predictive Path Integral (MPPI) framework. BiC-MPPI incorporates bidirectional dynamics approximations and a new guide cost mechanism, improving both trajectory planning and goal-reaching performance. By leveraging forward and backward rollouts, the bidirectional approach ensures effective trajectory connections between initial and terminal states, while the guide cost helps discover dynamically feasible paths. Experimental results demonstrate that BiC-MPPI outperforms existing MPPI variants in both 2D and 3D environments, achieving higher success rates and competitive computation times across 900 simulations on a modified BARN dataset for autonomous navigation. GitHub: https://github.com/i-ASL/BiC-MPPI
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