基于占用栅格的实时路径规划，抗感知噪声，无需手工规则。
ONRAP: Occupancy-driven Noise-Resilient Autonomous Path Planning
- 在占用空间中构建非线性规划模型，结合自行车运动模型与碰撞惩罚。
- 实测平均速度超10 Hz，可在严重噪声下安全通过狭窄通道。
- 适合需要鲁棒感知融合的自动驾驶系统，尤其适用于低资源平台。
动态路径规划需在感知噪声、定位不确定和语义感知不完整的情况下保持可靠性。我们提出一种实用且易部署的规划器，基于占用栅格运行，并可选地融合占用流预测，生成以自身为中心、满足运动学可行性的路径，安全穿越静态与动态障碍物。核心是一个基于改进自行车模型的时空非线性规划，显式包含可行性与避碰惩罚项。该方法仅在占用空间操作，天然适应未知障碍物类别与异构主体运动。整体流程平均运行速度超过10 Hz，调参极少，且能无缝接入标准控制栈。我们在仿真中引入严重定位与感知噪声，并在F1TENTH平台上验证，结果表明其能平稳安全地通过狭窄区域与复杂路况。该方法为抗噪声、具备预测感知的规划提供了稳健基础，避免了手工设计启发式规则。项目主页见：https://honda-research-institute.github.io/onrap/
原文摘要 · Abstract (English)
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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。