用模仿学习自动生成越野路径规划用的成本图
Trailblazer: Learning offroad costmaps for long range planning
- 通过专家示范和可微A*算法学习成本图
- 实测在复杂动态环境中表现稳健
- 适合需要长距离自主导航的机器人
无人地面车辆在非结构化环境中的自主导航仍是野外机器人领域的重要挑战，尤其在搜救、勘探和监视任务中。有效的长距离规划依赖于车载感知系统与先验环境知识（如卫星影像和LiDAR数据）的融合。本文提出Trailblazer框架，可自动将多模态传感器数据转化为成本图，实现无需人工调参的高效路径规划。与传统方法不同，Trailblazer利用模仿学习和可微A*规划器，直接从专家示范中学习成本图，提升在多样地形下的适应性。该方法通过大量真实场景测试验证，在动态复杂环境中表现出鲁棒性能，展现了其在可扩展、高效自主导航中的潜力。
原文摘要 · Abstract (English)
Autonomous navigation in off-road environments remains a significant challenge in field robotics, particularly for Unmanned Ground Vehicles (UGVs) tasked with search and rescue, exploration, and surveillance. Effective long-range planning relies on the integration of onboard perception systems with prior environmental knowledge, such as satellite imagery and LiDAR data. This work introduces Trailblazer, a novel framework that automates the conversion of multi-modal sensor data into costmaps, enabling efficient path planning without manual tuning. Unlike traditional approaches, Trailblazer leverages imitation learning and a differentiable A* planner to learn costmaps directly from expert demonstrations, enhancing adaptability across diverse terrains. The proposed methodology was validated through extensive real-world testing, achieving robust performance in dynamic and complex environments, demonstrating Trailblazer's potential for scalable, efficient autonomous navigation.
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