Crystallization-Inspired Design and Modeling of Self-Assembly Lattice-Formation Swarm Robotics

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 10; p. 3081
• Main Authors: Pan, Zebang, Wen, Guilin, Yin, Hanfeng, Yin, Shan, Tan, Zhao
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 12.05.2024
• Subjects: Collaboration; Crystallization; Design; Efficiency; Foraging behavior; macroscopic model; Phase transitions; Robotics; Robotics industry; Robots; self-assembly formation; Simulation methods; swarm robotics; New Jersey; Germany; macroscopic model; crystallization; swarm robotics; self-assembly formation
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24103081

Self-assembly formation is a key research topic for realizing practical applications in swarm robotics. Due to its inherent complexity, designing high-performance self-assembly formation strategies and proposing corresponding macroscopic models remain formidable challenges and present an open research frontier. Taking inspiration from crystallization, this paper introduces a distributed self-assembly formation strategy by defining , , , and states for robots. Robots in these states can spontaneously organize into user-specified two-dimensional shape formations with lattice structures through local interactions and communications. To address the challenges posed by complex spatial structures in modeling a macroscopic model, this work introduces the structural features estimation method. Subsequently, a corresponding non-spatial macroscopic model is developed to predict and analyze the self-assembly behavior, employing the proposed estimation method and a stock and flow diagram. Real-robot experiments and simulations validate the flexibility, scalability, and high efficiency of the proposed self-assembly formation strategy. Moreover, extensive experimental and simulation results demonstrate the model's accuracy in predicting the self-assembly process under different conditions. Model-based analysis indicates that the proposed self-assembly formation strategy can fully utilize the performance of individual robots and exhibits strong self-stability.