端到端规划器让机器人在森林地形中无需调参直接实战
YOPO-Rally: A Sim-to-Real Single-Stage Planner for Off-Road Terrain
- 用统一神经网络融合路径规划与可通行性分析，输入深度图和目标向量生成多条候选轨迹
- 在模拟森林环境中训练后零样本迁移至真实场景，无需微调即可运行
- 基于Unity的模拟器支持多传感器数据生成，适合野外自主导航研究者
由于恶劣地形和密集障碍物，非结构化环境中的自主导航仍具挑战。本文将端到端导航框架YOPO扩展至非结构化地形，聚焦于森林环境，构建了高性能、多传感器支持的离线模拟器YOPO-Sim、零样本迁移的模拟到现实规划器YOPO-Rally以及一个模型预测控制（MPC）控制器。该模拟器基于Unity引擎，可生成随机森林环境，并输出深度图像和点云地图用于专家示范，性能媲美主流模拟器。地形可通行性分析（TTA）处理代价图，生成以非均匀三次埃尔米特曲线表示的专家轨迹。规划器将TTA与路径规划集成于单一神经网络中，输入包括深度图像、当前速度和目标向量，输出多条带成本的轨迹候选。规划器在模拟环境中通过行为克隆训练，并直接部署至真实世界而无需微调。一系列仿真与真实实验验证了该框架的有效性。
原文摘要 · Abstract (English)
Off-road navigation remains challenging for autonomous robots due to the harsh terrain and clustered obstacles. In this letter, we extend the YOPO (You Only Plan Once) end-to-end navigation framework to off-road environments, explicitly focusing on forest terrains, consisting of a high-performance, multi-sensor supported off-road simulator YOPO-Sim, a zero-shot transfer sim-to-real planner YOPO-Rally, and an MPC controller. Built on the Unity engine, the simulator can generate randomized forest environments and export depth images and point cloud maps for expert demonstrations, providing competitive performance with mainstream simulators. Terrain Traversability Analysis (TTA) processes cost maps, generating expert trajectories represented as non-uniform cubic Hermite curves. The planner integrates TTA and the pathfinding into a single neural network that inputs the depth image, current velocity, and the goal vector, and outputs multiple trajectory candidates with costs. The planner is trained by behavior cloning in the simulator and deployed directly into the real-world without fine-tuning. Finally, a series of simulated and real-world experiments is conducted to validate the performance of the proposed framework.
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