Time-Efficient Iterative Learning Planning for Safety-Critical Dynamic Obstacle Avoidance

2026-09-17

AI Summary

本文通过结合预测风险混合控制屏障函数(ARB-CBF)扩展迭代学习规划(ILP)，解决了自主移动机器人在动态环境中的高效安全避障问题。

Abstract

Autonomous mobile robots require timeefficient planning and safety-critical dynamic obstacle avoidance under constrained onboard computation. While Iterative Learning Planning (ILP) offers lightweight and efficient traversal planning, it lacks explicit mechanisms for dynamic obstacle perception and avoidance. This article extends ILP to safety-critical navigation in dynamic environments by integrating an anticipatory risk-blended control barrier function (ARB-CBF). The extended ILP learns traversal-speed and steering-bias profiles via a fractionalpower update based on local obstacle risk, generating nominal control commands that ARB-CBF modifies at runtime for real-time safety guarantees. Algorithmic analysis demonstrates that the ILP replanning stage scales at O(kN) for k iterations and N waypoints, while ARB-CBF executes with linear complexity. Comprehensive simulations and real-world experiments validate the framework, demonstrating superior temporal efficiency and safety with lower computational overhead compared to optimizationbased baselines, making it highly suitable for resourceconstrained platforms.

Problem

• Autonomous mobile robots
• Time-efficient planning
• Dynamic obstacle avoidance
• Onboard computation constraints

Innovation

• Iterative Learning Planning
• Anticipatory Risk-Blended Control Barrier Function
• Dynamic Obstacle Avoidance