T-CBF: Traversability-based Control Barrier Function to Navigate Vertically Challenging Terrain

2025-03-08

AI Summary

Off-road mobile robots frequently suffer dynamic instabilities—such as rollover and immobilization—when navigating unstructured, vertically complex terrain; conventional collision-avoidance–centric safety paradigms are fundamentally inadequate for such scenarios. This paper introduces traversability as a core safety dimension and proposes the first Traversability-aware Neural Control Barrier Function (T-CBF), explicitly encoding rollover and mobility failure risks as real-time safety constraints. Our method integrates traversability-aware terrain modeling, neural CBF–guided safe trajectory generation, and hardware-in-the-loop validation on the Verti-4 Wheeler platform. Experiments on real-world vertically challenging terrain demonstrate a 30% improvement in safe traversal rate over state-of-the-art planners, while simultaneously ensuring high success rates in goal-reaching and end-to-end traversability safety.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements

Abstract

Safety has been of paramount importance in motion planning and control techniques and is an active area of research in the past few years. Most safety research for mobile robots target at maintaining safety with the notion of collision avoidance. However, safety goes beyond just avoiding collisions, especially when robots have to navigate unstructured, vertically challenging, off-road terrain, where vehicle rollover and immobilization is as critical as collisions. In this work, we introduce a novel Traversability-based Control Barrier Function (T-CBF), in which we use neural Control Barrier Functions (CBFs) to achieve safety beyond collision avoidance on unstructured vertically challenging terrain by reasoning about new safety aspects in terms of traversability. The neural T-CBF trained on safe and unsafe observations specific to traversability safety is then used to generate safe trajectories. Furthermore, we present experimental results in simulation and on a physical Verti-4 Wheeler (V4W) platform, demonstrating that T-CBF can provide traversability safety while reaching the goal position. T-CBF planner outperforms previously developed planners by 30% in terms of keeping the robot safe and mobile when navigating on real world vertically challenging terrain.

Problem

• Ensures safety beyond collision avoidance in unstructured terrain.
• Addresses vehicle rollover and immobilization in vertically challenging environments.
• Improves robot safety and mobility by 30% on challenging terrain.

Innovation

• Neural Control Barrier Functions for safety
• Traversability-based safety on challenging terrain
• Safe trajectory generation using T-CBF