Fast Motion Planning for Non-Holonomic Mobile Robots via a Rectangular Corridor Representation of Structured Environments

TL;DR

This paper introduces a fast, scalable motion planning framework for non-holonomic mobile robots in structured environments, utilizing a corridor-based decomposition to reduce search complexity and generate near-optimal trajectories.

Contribution

It proposes a novel corridor-based environment decomposition method that significantly reduces search space for efficient motion planning of non-holonomic robots.

Findings

Achieves faster planning times compared to traditional grid-based methods.

Demonstrates successful real-world robot navigation in complex environments.

Provides open-source implementation for broader adoption.

Abstract

We present a complete framework for fast motion planning of non-holonomic autonomous mobile robots in highly complex but structured environments. Conventional grid-based planners struggle with scalability, while many kinematically-feasible planners impose a significant computational burden due to their search space complexity. To overcome these limitations, our approach introduces a deterministic free-space decomposition that creates a compact graph of overlapping rectangular corridors. This method enables a significant reduction in the search space, without sacrificing path resolution. The framework then performs online motion planning by finding a sequence of rectangles and generating a near-time-optimal, kinematically-feasible trajectory using an analytical planner. The result is a highly efficient solution for large-scale navigation. We validate our framework through extensive simulations and on a physical robot. The implementation is publicly available as open-source software.