  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.
