Puzzle Piece Robots: Inverse-Designed Shape-Morphing Docking for Spherically Reconfigurable Soft Robots
Justin Conzola, Vishesh Vikas
AI summary
Problem
Docking in spherically reconfigurable soft robots is difficult because conventional coupling methods compromise compliance, interfere with body deformation, or lack sufficient holding strength across flat and spherical states.
Approach
The authors designed module edges that interlock like puzzle pieces and used inverse optimization to create an internal auxetic metamaterial structure that buckles under localized SMA actuation to dock, while passively maintaining coupling under distributed loads.
Key results
- Jigsaw-puzzle-like geometric interlocks enable reliable docking in both planar and spherical configurations
- Constraint-aware inverse design optimizes auxetic metamaterial cell dimensions for reversible buckling
- Simulation model validated with under 0.3 mm average displacement error against physical prototypes
- Two docked modules successfully completed 120 locomotive actuation cycles without disconnecting
Why it matters
Provides a compliant, high-strength coupling solution that advances untethered, multi-modal locomotion for modular soft robots in unstructured environments.
Abstract
Modularity in robots enhances versatility, enabling shape morphing and reconfiguration. In modular soft robots, the use of soft materials allows dimensional transformations across different architectures - from chains (1D) to lattices (2D) and spheres (3D). All this enables a swarm of robots to exhibit multi- modal locomotion - such as millipede-like, starfish-like, and soccer-ball-like movement patterns. However, achieving such reconfiguration remains challenging, especially in soft robots, where docking is difficult to realize without compromising com- pliance. Conventional approaches - such as rigid inserts, mag- netic actuators, and adhesives - face challenges due to rigid–soft fabrication mismatch, interference with body compliance and limited holding strength. To address these challenges, this work proposes a geometric, active shape-morphing docking mecha- nism for spherically reconfigurable soft robots, that combines concepts of topology design and mechanical metamaterials. The robot module edges are designed to create geometric interlocks between adjacent edges (similar to jigsaw puzzle pieces) with an internal structure that deforms under actuation by inlaid shape memory alloy (SMA) wires. The metamaterial internal structure is obtained through inverse design optimization of a computational deformation model created in Abaqus CAE. The constraint-aware optimization strategy blends random search and genetic algorithm features to handle a large number of bounded variables and nonlinear objective function, driving convergence toward a global minimum via geometric decay of the search space. The resulting optimal geometry is designed to buckle under high localized forces, enabling docking and undocking, while remaining minimally deformed under dis- tributed forces, thereby passively maintaining coupling during operation. The docking mechanism is experimentally validated by confirming that the deformation achieved under actuation can facilitate the docking operation and through locomotive testing where two docked modules completed 120 locomotive actuation cycles without disconnecting.