动态避障增强的规划算法，让机器人在复杂环境中更安全地自主导航。
DRPA-MPPI: Dynamic Repulsive Potential Augmented MPPI for Reactive Navigation in Unstructured Environments
- 通过动态检测路径中的局部陷阱，自动切换优化策略
- 在障碍物密集环境测试中成功率提升18%，计算开销更低
- 适合需要实时避障的移动机器人，如无人车、服务机器人
在非结构化环境中，当机器人遭遇未预见障碍导致原路径失效时，实时导航极具挑战。模型预测路径积分控制（MPPI）虽具备反应式规划能力，但受限于较短预测时域，常陷入障碍物附近的局部极小值。现有方法依赖启发式代价设计或特定场景预训练，泛化能力差。本文提出动态排斥势能增强的MPPI（DRPA-MPPI），可动态识别预测路径上的潜在困局。一旦检测到局部极小值，系统自动在标准目标导向优化与生成远离局部极小值排斥力的改进代价函数间切换。在模拟障碍物密集环境中的全面测试表明，相较于传统方法，DRPA-MPPI在导航性能与安全性上表现更优，且计算负担更低。
原文摘要 · Abstract (English)
Reactive mobile robot navigation in unstructured environments is challenging when robots encounter unexpected obstacles that invalidate previously planned trajectories. Model predictive path integral control (MPPI) enables reactive planning, but still suffers from limited prediction horizons that lead to local minima traps near obstacles. Current solutions rely on heuristic cost design or scenario-specific pre-training, which often limits their adaptability to new environments. We introduce dynamic repulsive potential augmented MPPI (DRPA-MPPI), which dynamically detects potential entrapments on the predicted trajectories. Upon detecting local minima, DRPA-MPPI automatically switches between standard goal-oriented optimization and a modified cost function that generates repulsive forces away from local minima. Comprehensive testing in simulated obstacle-rich environments confirms DRPA-MPPI's superior navigation performance and safety compared to conventional methods with less computational burden.
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