Serialized Red-Green-Gray: Quicker Heuristic Validation of Edges in Dynamic Roadmap Graphs

2026-03-30

AI Summary

This work addresses the inefficiency of traditional motion planning methods in dynamic environments, where frequent roadmap reconstruction or costly collision checks hinder performance. The authors propose the Red-Green-Gray (RGG) framework, which leverages SPITE and conservative geometric approximations to efficiently classify roadmap edges into three states—invalid, valid, or uncertain—thereby drastically reducing the number of edges requiring full verification. They further introduce a serialized variant, SerRGG, that integrates batch serialization with vectorized computation to enable GPU acceleration. Experimental results demonstrate that SerRGG achieves a 2–9× speedup over its sequential counterpart while preserving geometric fidelity and planning accuracy, significantly lowering the computational overhead of roadmap updates in dynamic scenarios.

Abstract

Motion planning in dynamic environments, such as robotic warehouses, requires fast adaptation to frequent changes in obstacle poses. Traditional roadmap-based methods struggle in such settings, relying on inefficient reconstruction of a roadmap or expensive collision detection to update the existing roadmap. To address these challenges we introduce the Red-Green-Gray (RGG) framework, a method that builds on SPITE to quickly classify roadmap edges as invalid (red), valid (green), or uncertain (gray) using conservative geometric approximations. Serial RGG provides a high-performance variant leveraging batch serialization and vectorization to enable efficient GPU acceleration. Empirical results demonstrate that while RGG effectively reduces the number of unknown edges requiring full validation, SerRGG achieves a 2-9x speedup compared to the sequential implementation. This combination of geometric precision and computational speed makes SerRGG highly effective for time-critical robotic applications.

Problem

• motion planning
• dynamic environments
• roadmap graphs
• collision detection
• real-time adaptation

Innovation

• Red-Green-Gray
• dynamic roadmap
• GPU acceleration
• motion planning
• collision detection