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<p>Small-scale magnetic robots that can assemble, disassemble, and propel under globally applied magnetic fields can be versatile modular subunits for manufacturing and <i>in vivo</i> operations. This paper presents a magnetic cuboid robot that contains assembled cubes with encapsulated, freely-rotating permanent magnets. This minimalistic and scalable design enables magnetic cubes to assemble under magnetic fields into a cube chain that can propel using pivot-walking locomotion. The magnets for propulsion are evenly distributed between the cubes, but individual cubes can only move when joined with at least one other. A vision-based closed-loop controller that modulates the cuboid robot’s position and orientation during pivot walking is presented. The controller is simulated to navigate cuboid robots to user-selected goal locations. A Breadth-First Search (BFS) path-planning algorithm for obstacle avoidance is used to generate optimal paths for closed-loop pivot walking. Two physical workspaces are tested, one with a large free space and the other with a maze. Experiments and simulations demonstrate that magnetic cuboid robots can navigate in complex mazes and selectively self-assemble into cube chains while following the optimal path generated by the motion planner with visual feedback control.</p>

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Coordinated self-assembly and feedback control of distributed magnetic cuboid robots

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月5日

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  • Louis William Rogowski,
  • Gokhan Kararsiz,
  • Anuruddha Bhattacharjee,
  • Yitong Lu,
  • Zhengguang Wang,
  • Xiao Zhang,
  • Aaron T. Becker,
  • Min Jun Kim

摘要

Small-scale magnetic robots that can assemble, disassemble, and propel under globally applied magnetic fields can be versatile modular subunits for manufacturing and in vivo operations. This paper presents a magnetic cuboid robot that contains assembled cubes with encapsulated, freely-rotating permanent magnets. This minimalistic and scalable design enables magnetic cubes to assemble under magnetic fields into a cube chain that can propel using pivot-walking locomotion. The magnets for propulsion are evenly distributed between the cubes, but individual cubes can only move when joined with at least one other. A vision-based closed-loop controller that modulates the cuboid robot’s position and orientation during pivot walking is presented. The controller is simulated to navigate cuboid robots to user-selected goal locations. A Breadth-First Search (BFS) path-planning algorithm for obstacle avoidance is used to generate optimal paths for closed-loop pivot walking. Two physical workspaces are tested, one with a large free space and the other with a maze. Experiments and simulations demonstrate that magnetic cuboid robots can navigate in complex mazes and selectively self-assemble into cube chains while following the optimal path generated by the motion planner with visual feedback control.