用动态贝叶斯过滤预测局部陷阱，提升无人车在复杂环境中的导航能力
Local Minima Prediction using Dynamic Bayesian Filtering for UGV Navigation in Unstructured Environments
- 基于局部障碍物与全局目标，用动态贝叶斯过滤预测局部极小点
- 可提前识别可能被困区域，避免车辆陷入死循环
- 适合需高鲁棒性导航的无人地面车辆系统
路径规划对自主车辆导航至关重要，但在复杂真实环境中，尽管可提供全局视图，却常因过时而失效，导致无人地面车辆（UGVs）必须依赖实时局部信息。这种仅依赖局部信息、忽略全局上下文的做法，易使UGV陷入局部极小点。本文提出一种基于动态贝叶斯滤波的方法，利用局部视野中检测到的障碍物与全局目标，主动预测潜在的局部极小点。该方法旨在提升自动驾驶车辆的自主导航能力，使其能在被困前预判风险，选择求助人类或重新规划替代路径。
原文摘要 · Abstract (English)
Path planning is crucial for the navigation of autonomous vehicles, yet these vehicles face challenges in complex and real-world environments. Although a global view may be provided, it is often outdated, necessitating the reliance of Unmanned Ground Vehicles (UGVs) on real-time local information. This reliance on partial information, without considering the global context, can lead to UGVs getting stuck in local minima. This paper develops a method to proactively predict local minima using Dynamic Bayesian filtering, based on the detected obstacles in the local view and the global goal. This approach aims to enhance the autonomous navigation of self-driving vehicles by allowing them to predict potential pitfalls before they get stuck, and either ask for help from a human, or re-plan an alternate trajectory.
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