Trailblazer: Learning offroad costmaps for long range planning

2025-05-14

AI Summary

In long-range autonomous navigation of unmanned ground vehicles (UGVs) within unstructured野外 environments, conventional cost map generation relies heavily on hand-crafted features and manual parameter tuning, resulting in poor generalization across diverse terrains. Method: This paper proposes an end-to-end learnable cost map generation framework that fuses satellite imagery, onboard LiDAR, and real-time perception data into a multimodal input. It innovatively integrates imitation learning with a differentiable A* planner, enabling gradient-based optimization guided by expert path priors to automatically produce terrain-adaptive cost maps. Contribution/Results: The framework eliminates the need for manual cost function design or hyperparameter tuning, significantly improving cross-terrain generalization. Extensive real-world field experiments demonstrate substantial gains in path planning robustness and adaptability under dynamic, complex off-road conditions. This work establishes a scalable, joint perception-planning learning paradigm for野外 autonomous navigation.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• 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

Abstract

Autonomous navigation in off-road environments remains a significant challenge in field robotics, particularly for Unmanned Ground Vehicles (UGVs) tasked with search and rescue, exploration, and surveillance. Effective long-range planning relies on the integration of onboard perception systems with prior environmental knowledge, such as satellite imagery and LiDAR data. This work introduces Trailblazer, a novel framework that automates the conversion of multi-modal sensor data into costmaps, enabling efficient path planning without manual tuning. Unlike traditional approaches, Trailblazer leverages imitation learning and a differentiable A* planner to learn costmaps directly from expert demonstrations, enhancing adaptability across diverse terrains. The proposed methodology was validated through extensive real-world testing, achieving robust performance in dynamic and complex environments, demonstrating Trailblazer's potential for scalable, efficient autonomous navigation.

Problem

• Autonomous off-road navigation for UGVs in challenging environments
• Automated costmap generation from multi-modal sensor data
• Learning costmaps via imitation learning for diverse terrains

Innovation

• Automates multi-modal sensor data into costmaps
• Uses imitation learning for costmap generation
• Integrates differentiable A* planner for adaptability