SwarmCVT: Centroidal Voronoi Tessellation-Based Path Planning for Very-Large-Scale Robotics

TL;DR

This paper introduces SwarmCVT, a path planning method for very large-scale robotics that uses centroidal Voronoi tessellation to systematically generate Gaussian components, improving performance and reliability.

Contribution

It presents a novel approach employing centroidal Voronoi tessellation for systematic Gaussian component generation in VLSR path planning, enhancing efficiency and consistency.

Findings

Performance improvement over previous methods

Ensures consistency and reliability in path planning

Effective in obstacle-rich environments

Abstract

Swarm robotics, or very large-scale robotics (VLSR), has many meaningful applications for complicated tasks. However, the complexity of motion control and energy costs stack up quickly as the number of robots increases. In addressing this problem, our previous studies have formulated various methods employing macroscopic and microscopic approaches. These methods enable microscopic robots to adhere to a reference Gaussian mixture model (GMM) distribution observed at the macroscopic scale. As a result, optimizing the macroscopic level will result in an optimal overall result. However, all these methods require systematic and global generation of Gaussian components (GCs) within obstacle-free areas to construct the GMM trajectories. This work utilizes centroidal Voronoi tessellation to generate GCs methodically. Consequently, it demonstrates performance improvement while also ensuring consistency and reliability.