hanruihua/NeuPAN

Health v2 · maintenance only

Adoption not part of the score

1090 stars · 105 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NeuPAN is a real-time, map-free robot motion planner that maps raw obstacle point data directly to control actions using an end-to-end MPC-based neural proximal alternating-minimization network. It is a Python library for point-robot navigation with collision avoidance, deployable on mobile robots and vehicles, with ROS/ROS2 wrappers available.

Use cases

• navigate a mobile robot through cluttered unknown environments
• real-time collision avoidance from raw point cloud data
• local motion planning for nonholonomic robots
• deploy a model-based learning planner on a real robot
• plan paths for wheel-legged robots and autonomous vehicles
• integrate a planner into ROS or ROS2 navigation stacks

When to choose

• you need real-time, map-free local planning with point-level collision avoidance
• you want an interpretable end-to-end planner combining learning and optimization
• you deploy on ground robots, wheel-legged robots, or autonomous vehicles
• you want a planner that integrates with ROS/ROS2

When to avoid

• you need global path planning over large known maps
• you work outside Python or need non-GPL-licensed code
• you need planners for aerial or manipulator robots
• you require a fully mature production-grade navigation stack

Facets

library · maturity active

machine-learning simulation robotics robotics autonomous-vehicles machine-learning simulation python cross-platform motion-planning collision-avoidance mpc model-based-learning local-planner mobile-robot point-cloud ros end-to-end-learning navigation linux

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