Global Path Planner with Multi-Model Switching

2026-09-11

AI Summary

本文通过多模型切换的纯追踪控制器增强全局路径规划，利用自适应运动学建模实时调整以适应不同地形，提高路径效率和能源使用。

Abstract

This work enhances global path planning via a pure-pursuit controller with multi-model kinematic switching that sustains plan fidelity across diverse terrains. The system includes a traversability graph for terrain analysis, a Heading-Aware A* algorithm for generating feasible paths, and a multi-model Pure Pursuit controller for dynamic tracking. A core innovation is adaptive kinematic modeling, enabling real-time switching between kinematic models based on terrain features and robot states. This adaptability optimizes path efficiency and energy use in challenging scenarios. We validate the approach in simulation on different platforms, namely the Artaban quadruped and the X3 quadrotor drone, showcasing improved performance, robustness, and adaptability over standard baselines.

Problem

• global path planning
• terrain analysis
• adaptive kinematic modeling

Innovation

• adaptive kinematic modeling
• multi-model switching
• terrain analysis