🤖 AI Summary
This work addresses critical limitations of existing modular magnetically driven microrobots—namely, their reliance on collision-based reconfiguration, dependence on bulky three-dimensional electromagnetic systems, and inability to achieve precise individual module control—which hinder their deployment in confined biomedical environments. The authors propose a reconfigurable platform composed of three functionally distinct cubic magnetic modules that enables collision-free self-assembly and independent actuation of single modules under low-strength two-dimensional time-varying uniform and gradient magnetic fields. By integrating embedded permanent magnets, real-time visual feedback, and A* path planning, the system supports multimodal locomotion and closed-loop navigation. Experiments demonstrate 90% successful reconfiguration from chain-like structures to gripper morphologies, highlighting the platform’s high adaptability, scalability, and efficient reconfiguration capability within constrained spaces, thereby opening new avenues for microrobotic applications in biomedical contexts.
📝 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.