NeuPAN: Direct Point Robot Navigation with End-to-End Model-based Learning

2024-03-11

arXiv.org

AI Summary

To address the challenge of real-time, map-free navigation for non-holonomic robots in unknown, cluttered environments, this paper proposes an end-to-end, point-cloud-driven motion planning method. Our approach directly maps raw 3D point clouds to distance-field features—bypassing conventional perception-control cascades and eliminating error propagation. Technically, we introduce a novel knowledge- and data-fused learning framework grounded in a plug-and-play proximal alternating minimization network (PnP-PAN), which embeds neuron-level closed-loop optimization and supports end-to-end differentiable fine-tuning. Evaluated in diverse simulated and real-world settings—including sandbox, office, corridor, and parking lot environments—our method consistently outperforms state-of-the-art baselines. It demonstrates strong robustness, cross-environment generalizability, and reliable map-free navigation around arbitrarily shaped obstacles.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Navigating a nonholonomic robot in a cluttered, unknown environment requires accurate perception and precise motion control for real-time collision avoidance. This paper presents NeuPAN: a real-time, highly accurate, map-free, easy-to-deploy, and environment-invariant robot motion planner. Leveraging a tightly coupled perception-to-control framework, NeuPAN has two key innovations compared to existing approaches: 1) it directly maps raw point cloud data to a latent distance feature space for collision-free motion generation, avoiding error propagation from the perception to control pipeline; 2) it is interpretable from an end-to-end model-based learning perspective. The crux of NeuPAN is solving an end-to-end mathematical model with numerous point-level constraints using a plug-and-play (PnP) proximal alternating-minimization network (PAN), incorporating neurons in the loop. This allows NeuPAN to generate real-time, physically interpretable motions. It seamlessly integrates data and knowledge engines, and its network parameters can be fine-tuned via backpropagation. We evaluate NeuPAN on a ground mobile robot, a wheel-legged robot, and an autonomous vehicle, in extensive simulated and real-world environments. Results demonstrate that NeuPAN outperforms existing baselines in terms of accuracy, efficiency, robustness, and generalization capabilities across various environments, including the cluttered sandbox, office, corridor, and parking lot. We show that NeuPAN works well in unknown and unstructured environments with arbitrarily shaped objects, transforming impassable paths into passable ones.

Problem

• Real-time navigation for nonholonomic robots
• End-to-end model-based collision-free motion planning
• Environment-invariant, accurate, and easy-to-deploy solution

Innovation

• Direct point cloud mapping
• End-to-end model-based learning
• Plug-and-play PAN network