Distributed prediction of unsafe reconfiguration scenarios of modular robotic Programmable Matter

TL;DR

This paper introduces a distributed algorithm that predicts mechanical overloads during modular robot reconfiguration, enhancing safety by enabling the robot to assess stability in real-time using a simplified mechanical model.

Contribution

It presents a novel distributed prediction framework for modular robots that estimates overload risks during reconfiguration, validated through simulation and real-world experiments.

Findings

Algorithm accurately predicts overload scenarios in simulations.

Real-life experiments confirm the effectiveness of the prediction method.

Framework enables safer reconfiguration by early detection of instability.

Abstract

We present a distributed framework for predicting whether a planned reconfiguration step of a modular robot will mechanically overload the structure, causing it to break or lose stability under its own weight. The algorithm is executed by the modular robot itself and based on a distributed iterative solution of mechanical equilibrium equations derived from a simplified model of the robot. The model treats inter-modular connections as beams and assumes no-sliding contact between the modules and the ground. We also provide a procedure for simplified instability detection. The algorithm is verified in the Programmable Matter simulator VisibleSim, and in real-life experiments on the modular robotic system Blinky Blocks.