Navigating a nonholonomic robot in a cluttered environment requires extremely accurate perception and locomotion for collision avoidance. This paper presents NeuPAN: a real-time, highly-accurate, map-free, robot-agnostic, and environment-invariant robot navigation solution. Leveraging a tightly-coupled perception-locomotion framework, NeuPAN has two key innovations compared to existing approaches: 1) it directly maps raw points to a learned multi-frame distance space, avoiding error propagation from perception to control; 2) it is interpretable from an end-to-end model-based learning perspective, enabling provable convergence. The crux of NeuPAN is to solve a high-dimensional end-to-end mathematical model with various point-level constraints using the plug-and-play (PnP) proximal alternating-minimization network (PAN) with neurons in the loop. This allows NeuPAN to generate real-time, end-to-end, physically-interpretable motions directly from point clouds, which seamlessly integrates data- and knowledge-engines, where its network parameters are adjusted via back propagation. We evaluate NeuPAN on car-like robot, wheel-legged robot, and passenger autonomous vehicle, in both simulated and real-world environments. Experiments demonstrate that NeuPAN outperforms various benchmarks, in terms of accuracy, efficiency, robustness, and generalization capability across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unstructured environments with arbitrary-shape undetectable objects, making impassable ways passable.

翻译：摘要：在杂乱环境中引导非完整约束机器人实现避障，需要极其精确的感知与运动控制能力。本文提出NeuPAN：一种实时、高精度、无需地图、机器人无关且环境鲁棒的导航解决方案。基于紧耦合的感知-运动框架，NeuPAN与现有方法相比具有两大核心创新：1）直接将原始点云映射至学习得到的多帧距离空间，避免从感知到控制的误差传播；2）从端到端模型学习角度具备可解释性，可实现可证明的收敛性。NeuPAN的关键在于利用含神经元的即插即用(PnP)近端交替最小化网络(PAN)求解带有多类点级约束的高维端到端数学模型，从而直接从点云生成实时、端到端且具备物理可解释性的运动轨迹，实现数据引擎与知识引擎的无缝融合，其中网络参数通过反向传播进行调节。我们在类车机器人、轮腿式机器人及乘用自动驾驶车辆上，于仿真与现实环境中进行性能评估。实验表明，NeuPAN在复杂沙盘、办公室、走廊及停车场等多种场景下，其精度、效率、鲁棒性及泛化能力均优于各类基准方法。研究证实，NeuPAN能在存在任意形状不可检测物体的非结构化环境中稳定运行，使原本不可通行的路径变为可行。