Connected Reconfiguration of Polyominoes Amid Obstacles using RRT*

TL;DR

This paper presents a sampling-based RRT* approach for reconfiguring connected polyomino tiles in obstacle-rich environments, optimizing build sequences for automated cellular structure assembly.

Contribution

It introduces a novel application of RRT* for reconfiguring connected tiles with obstacle constraints, improving efficiency over existing algorithms.

Findings

RRT* finds more efficient build sequences with fewer samples.

The approach effectively handles environments with varying obstacle densities.

Compared to global and local planners, RRT* demonstrates superior performance.

Abstract

This paper investigates the use of a sampling-based approach, the RRT*, to reconfigure a 2D set of connected tiles in complex environments, where multiple obstacles might be present. Since the target application is automated building of discrete, cellular structures using mobile robots, there are constraints that determine what tiles can be picked up and where they can be dropped off during reconfiguration. We compare our approach to two algorithms as global and local planners, and show that we are able to find more efficient build sequences using a reasonable number of samples, in environments with varying densities of obstacles.