Dynamic path planning must remain reliable in the presence of sensing noise, uncertain localization, and incomplete semantic perception. We propose a practical, implementation-friendly planner that operates on occupancy grids and optionally incorporates occupancy-flow predictions to generate ego-centric, kinematically feasible paths that safely navigate through static and dynamic obstacles. The core is a nonlinear program in the spatial domain built on a modified bicycle model with explicit feasibility and collision-avoidance penalties. The formulation naturally handles unknown obstacle classes and heterogeneous agent motion by operating purely in occupancy space. The pipeline runs in real-time (faster than 10 Hz on average), requires minimal tuning, and interfaces cleanly with standard control stacks. We validate our approach in simulation with severe localization and perception noises, and on an F1TENTH platform, demonstrating smooth and safe maneuvering through narrow passages and rough routes. The approach provides a robust foundation for noise-resilient, prediction-aware planning, eliminating the need for handcrafted heuristics. The project website can be accessed at https://honda-research-institute.github.io/onrap/

翻译：动态路径规划必须在存在感知噪声、定位不确定性和不完整语义感知的情况下保持可靠性。我们提出了一种实用且易于实现的规划器，该规划器在占据栅格上运行，并可选择性地结合占据流预测，以生成以自车为中心、运动学可行的路径，从而安全地穿越静态和动态障碍物。其核心是一个基于改进自行车模型构建的空间域非线性优化问题，该模型包含显式的可行性约束和避碰惩罚项。该公式通过在纯粹的占据空间中进行操作，自然地处理未知障碍物类别和异质智能体运动。该流程实时运行（平均快于10 Hz），需要最少的参数调整，并能与标准控制栈清晰对接。我们在仿真中通过严重的定位和感知噪声，以及在F1TENTH平台上验证了我们的方法，展示了其在狭窄通道和崎岖路线中的平滑安全机动能力。该方法为抗噪声、具备预测意识的规划提供了鲁棒的基础，无需依赖人工设计的启发式规则。项目网站可通过 https://honda-research-institute.github.io/onrap/ 访问。