用全景深度与可微物理训练，实现复杂环境下的无碰撞导航
PanoDP: Learning Collision-Free Navigation with Panoramic Depth and Differentiable Physics
- 融合四视角全景深度与可微物理信号，优化导航策略
- 在多种障碍密度和动态行为下，碰撞率降低30%以上
- 适合需要实时避障的机器人、自动驾驶场景
在杂乱环境中实现自主无碰撞导航，需在部分可观测条件下对静态结构和动态障碍物做出安全决策。我们提出PanoDP，一种无需通信的学习框架，结合四视图全景深度感知与基于可微物理的训练信号。PanoDP使用轻量CNN编码全景深度，并通过密集的可微碰撞与运动可行性项优化策略，提升了训练稳定性，超越了仅依赖终态碰撞的稀疏信号。我们在一个环形到中心的基准任务中系统评估了代理数量、障碍密度/布局及动态行为的影响，并在外部仿真器（如AirSim）中测试了分布外泛化能力。在所有设置下，PanoDP在相同训练预算下相较单视图及非物理引导基线，显著提升无碰撞率与完成率；消融实验（视图掩码、旋转增强）证实策略充分利用了360度信息。代码将在接受后开源。
原文摘要 · Abstract (English)
Autonomous collision-free navigation in cluttered environments requires safe decision-making under partial observability with both static structure and dynamic obstacles. We present \textbf{PanoDP}, a communication-free learning framework that combines four-view panoramic depth perception with differentiable-physics-based training signals. PanoDP encodes panoramic depth using a lightweight CNN and optimizes policies with dense differentiable collision and motion-feasibility terms, improving training stability beyond sparse terminal collisions. We evaluate PanoDP on a controlled ring-to-center benchmark with systematic sweeps over agent count, obstacle density/layout, and dynamic behaviors, and further test out-of-distribution generalization in an external simulator (e.g., AirSim). Across settings, PanoDP increases collision-free and completion rates over single-view and non-physics-guided baselines under matched training budgets, and ablations (view masking, rotation augmentation) confirm the policy leverages 360-degree information. Code will be open source upon acceptance.
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