Fast Motion Planning for Non-Holonomic Mobile Robots Via a Rectangular Corridor Representation of Structured Environments
Alejandro Gonzalez-Garcia, Sebastiaan Wyns, Sonia De Santis, Jan Swevers, Wilm Decré
AI summary
Problem
Conventional grid-based planners scale poorly with map resolution, while sampling-based and convex-cover methods often lack deterministic guarantees, struggle in narrow passages, or ignore kinematic constraints. This leaves a gap for scalable, real-time motion planning for non-holonomic mobile robots in complex structured environments.
Approach
The framework decomposes the environment offline into a compact graph of overlapping rectangular corridors, then finds a corridor sequence online and generates kinematically feasible trajectories using analytical motion primitives without online optimization.
Key results
- Deterministic free-space decomposition achieving >10,000:1 structural compression
- Real-time planning complexity independent of map resolution
- Near-time-optimal trajectories via analytical motion primitives
- Extensive simulation and physical robot validation with open-source ROS 2 release
Why it matters
Provides a scalable, deterministic navigation solution for industrial autonomous mobile robots operating in complex, corridor-heavy environments like warehouses and factories.
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 rect- angular 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.