Design and Control of Modular Magnetic Millirobots for Multimodal Locomotion and Shape Reconfiguration

TL;DR

This paper introduces a versatile modular magnetic millirobot platform with three functional modules, capable of multimodal locomotion, shape reconfiguration, and autonomous control in constrained environments, advancing biomedical and adaptive robotic applications.

Contribution

It presents a novel modular magnetic robot design with distinct functional modules and programmable magnetic actuation for reliable reconfiguration and locomotion, including real-time closed-loop control.

Findings

Achieved 90% success in chain-to-gripper transformations.

Demonstrated multimodal locomotion and shape reconfiguration at low magnetic field strengths.

Established robust single-module control with real-time vision feedback.

Abstract

Modular small-scale robots offer the potential for on-demand assembly and disassembly, enabling task-specific adaptation in dynamic and constrained environments. However, existing modular magnetic platforms often depend on workspace collisions for reconfiguration, employ bulky three-dimensional electromagnetic systems, and lack robust single-module control, which limits their applicability in biomedical settings. In this work, we present a modular magnetic millirobotic platform comprising three cube-shaped modules with embedded permanent magnets, each designed for a distinct functional role: a free module that supports self-assembly and reconfiguration, a fixed module that enables flip-and-walk locomotion, and a gripper module for cargo manipulation. Locomotion and reconfiguration are actuated by programmable combinations of time-varying two-dimensional uniform and gradient magnetic field inputs. Experiments demonstrate closed-loop navigation using real-time vision feedback and A* path planning, establishing robust single-module control capabilities. Beyond locomotion, the system achieves self-assembly, multimodal transformations, and disassembly at low field strengths. Chain-to-gripper transformations succeeded in 90% of trials, while chain-to-square transformations were less consistent, underscoring the role of module geometry in reconfiguration reliability. These results establish a versatile modular robotic platform capable of multimodal behavior and robust control, suggesting a promising pathway toward scalable and adaptive task execution in confined environments.