VIP: Variation-based Iterative-learning Planning for Robotic Navigation

2026-08-25

AI Summary

本文提出一种基于变分的迭代学习规划框架VIP，用于解决机器人在复杂环境下的高效运动规划问题，通过直接在无限维函数空间中更新规划命令，避免了传统方法的高计算成本。

Abstract

Over the past decade, autonomous robotic systems have been increasingly deployed in applications such as surveying, search and rescue, and last-mile delivery. These applications require robots to generate safe and efficient motion plans in large, complex, and obstacle-dense environments, often under limited onboard computing resources. However, conventional planning methods commonly rely on finite-dimensional trajectory parameterization or increasingly long prediction horizons, leading to rapidly growing computational costs, particularly in multi-robot scenarios. This paper presents a novel variation-based iterative-learning planning (VIP) framework for efficient motion planning of both single robots and robotic swarms. Instead of optimizing a large number of discrete trajectory variables, VIP directly updates the planning command as a continuous function in an infinite-dimensional function space. The same variation-based update can be implemented in a model-in-the-loop manner for offline planning or in a robot-in-the-loop manner between online physical executions. By avoiding the computational burden associated with horizon expansion and high-dimensional trajectory discretization, VIP maintains a per-iteration computational complexity of $\mathcal{O}(n)$, where $n$ denotes the number of spatial discretization points. Extensive simulations and real-world experiments demonstrate that the proposed framework can efficiently generate and iteratively improve motion plans for different planning objectives, robotic platforms, and swarm configurations, highlighting its effectiveness, computational efficiency, and scalability as a general planning methodology.

Problem

• autonomous robotic systems
• motion planning
• computational costs
• multi-robot scenarios
• trajectory parameterization

Innovation

• variation-based iterative-learning planning
• infinite-dimensional function space
• computational efficiency
• motion planning