DRPA-MPPI：动态斥力势增强的MPPI用于非结构化环境中的反应式导航
DRPA-MPPI: Dynamic Repulsive Potential Augmented MPPI for Reactive Navigation in Unstructured Environments
AI总结：
针对非结构化环境中反应式导航易陷入局部极小值的问题，提出DRPA-MPPI方法，动态检测并切换斥力成本函数，在障碍物密集模拟中实现更优导航性能与安全性，且计算负担更小。
摘要：
在非结构化环境中，当移动机器人遇到意外障碍物导致先前规划的轨迹失效时，反应式导航具有挑战性。模型预测路径积分控制（MPPI）能够实现反应式规划，但仍受限于预测视界较短，导致在障碍物附近陷入局部极小值陷阱。现有解决方案依赖启发式成本设计或特定场景的预训练，这往往限制了其对新环境的适应性。我们提出了动态斥力势增强的MPPI（DRPA-MPPI），该方法动态检测预测轨迹上的潜在围困。一旦检测到局部极小值，DRPA-MPPI自动在标准目标导向优化和修改后的成本函数之间切换，该成本函数产生远离局部极小值的斥力。在模拟的障碍物密集环境中的全面测试证实，与传统方法相比，DRPA-MPPI具有更优的导航性能和安全性，且计算负担更小。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
