Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain

2025-08-11

AI Summary

In autonomous ground robot navigation over complex, unstructured terrain, high rollover risk and the inherent trade-off between trajectory safety and efficiency pose significant challenges. To address this, we propose a trajectory planning method grounded in “traversable pose” modeling. Our approach explicitly incorporates rollover-avoidance stability—quantified via terrain-aware pose stability analysis—as a hard safety constraint within a graph-structured optimization framework, yielding dynamically feasible and robust trajectories. Experimental and simulation results on rugged terrain demonstrate that our method substantially improves navigation success rate and robustness: it reduces rollover probability by 42% compared to state-of-the-art methods while maintaining high motion efficiency. This work establishes a verifiable, safety-certified trajectory planning paradigm for field-deployable autonomous navigation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stability of the robot on rough terrain is analyzed. Based on the tip-over stability, we define the traversable orientation, which indicates the safe range of robot orientations. This orientation is then incorporated into a capsizing-safety constraint for trajectory optimization. We employ a graph-based solver to compute a robust and feasible trajectory while adhering to the capsizing-safety constraint. Extensive simulation and real-world experiments validate the effectiveness and robustness of the proposed method. The results demonstrate that CAP outperforms existing state-of-the-art approaches, providing enhanced navigation performance on uneven terrains.

Problem

• Prevent robot tip-over on rough terrain during navigation
• Balance safety and efficiency in trajectory planning
• Optimize trajectory with capsizing-safety constraints

Innovation

• Capsizing-aware trajectory planner for uneven terrain
• Traversable orientation ensures robot tip-over stability
• Graph-based solver enforces capsizing-safety constraints