TY - GEN
T1 - Self-Assembly Planning for Modular Robots via Multi-Agent Path Finding on Time-Expanded Networks
AU - Huang, Zhen
AU - Cheng, Yajie
AU - Shi, Lingling
AU - Shan, Minghe
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Self-assembly planning for modular robots is critical for constructing functional structures, yet existing methods often suffer from inefficiency, poor scalability, or collision risks. This paper presents an innovative framework that formulates modular robot self-assembly as a time-varying online Multi-Agent Path Finding (MAPF) problem and resolves it through an enhanced Time-Expanded Network (TEN). Key modifications are introduced to handle the dynamic nature of the self-assembly process, including the varying number of agents and evolving target configurations. Simulations conducted with hexagonal modular robots demonstrate that the proposed algorithm significantly outperforms the benchmark A*-based approach in terms of both assembly efficiency and success rate across various target configurations. The proposed framework establishes a scalable planning framework for modular robot self-assembly, with future extensions toward real-world validation.
AB - Self-assembly planning for modular robots is critical for constructing functional structures, yet existing methods often suffer from inefficiency, poor scalability, or collision risks. This paper presents an innovative framework that formulates modular robot self-assembly as a time-varying online Multi-Agent Path Finding (MAPF) problem and resolves it through an enhanced Time-Expanded Network (TEN). Key modifications are introduced to handle the dynamic nature of the self-assembly process, including the varying number of agents and evolving target configurations. Simulations conducted with hexagonal modular robots demonstrate that the proposed algorithm significantly outperforms the benchmark A*-based approach in terms of both assembly efficiency and success rate across various target configurations. The proposed framework establishes a scalable planning framework for modular robot self-assembly, with future extensions toward real-world validation.
UR - https://www.scopus.com/pages/publications/105029963775
U2 - 10.1109/IROS60139.2025.11246674
DO - 10.1109/IROS60139.2025.11246674
M3 - Conference contribution
AN - SCOPUS:105029963775
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 8660
EP - 8666
BT - IROS 2025 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, Conference Proceedings
A2 - Laugier, Christian
A2 - Renzaglia, Alessandro
A2 - Atanasov, Nikolay
A2 - Birchfield, Stan
A2 - Cielniak, Grzegorz
A2 - De Mattos, Leonardo
A2 - Fiorini, Laura
A2 - Giguere, Philippe
A2 - Hashimoto, Kenji
A2 - Ibanez-Guzman, Javier
A2 - Kamegawa, Tetsushi
A2 - Lee, Jinoh
A2 - Loianno, Giuseppe
A2 - Luck, Kevin
A2 - Maruyama, Hisataka
A2 - Martinet, Philippe
A2 - Moradi, Hadi
A2 - Nunes, Urbano
A2 - Pettre, Julien
A2 - Pretto, Alberto
A2 - Ranzani, Tommaso
A2 - Ronnau, Arne
A2 - Rossi, Silvia
A2 - Rouse, Elliott
A2 - Ruggiero, Fabio
A2 - Simonin, Olivier
A2 - Wang, Danwei
A2 - Yang, Ming
A2 - Yoshida, Eiichi
A2 - Zhao, Huijing
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2025
Y2 - 19 October 2025 through 25 October 2025
ER -