ElectroVoxel: Electromagnetically Actuated Pivoting for Scalable Modular Self-Reconfigurable Robots

TL;DR

ElectroVoxel introduces a scalable, cube-based self-reconfigurable robot using electromagnetically actuated pivoting, enabling 3D reconfiguration with simplified hardware suitable for various environments.

Contribution

This work presents a novel electromagnet-based actuation mechanism for scalable, modular self-reconfigurable robots, allowing 3D reconfiguration through pivoting with reduced complexity.

Findings

Successfully demonstrated 2D and 3D reconfiguration maneuvers.

Developed hardware and software for untethered, self-reconfigurable robots.

Evaluated electrical and dynamical characteristics to inform scalable design.

Abstract

This paper introduces a cube-based reconfigurable robot that utilizes an electromagnet-based actuation framework to reconfigure in three dimensions via pivoting. While a variety of actuation mechanisms for self-reconfigurable robots have been explored, they often suffer from cost, complexity, assembly and sizing requirements that prevent scaled production of such robots. To address this challenge, we use an actuation mechanism based on electromagnets embedded into the edges of each cube to interchangeably create identically or oppositely polarized electromagnet pairs, resulting in repulsive or attractive forces, respectively. By leveraging attraction for hinge formation, and repulsion to drive pivoting maneuvers, we can reconfigure the robot by voxelizing it and actuating its constituent modules - termed Electrovoxels - via electromagnetically actuated pivoting. To demonstrate this, we develop fully untethered, three-dimensional self-reconfigurable robots and demonstrate 2D and 3D self-reconfiguration using pivot and traversal maneuvers on an air-table and in microgravity on a parabolic flight. This paper describes the hardware design of our robots, its pivoting framework, our reconfiguration planning software, and an evaluation of the dynamical and electrical characteristics of our system to inform the design of scalable self-reconfigurable robots.