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<p>Self-reconfiguration of modular robots is one of the most challenging problems in the robotics field. The objective is to determine how a set of identical modular robots, with local knowledge of the system and limited capacities, can reorganize themselves into a target topology or shape. The problem has received great interest from the research community giving birth to many centralized and distributed algorithms. However, the lack of comparative study of these algorithms makes it difficult to choose one when faced with a given configuration. In this paper, we present a kind of high-level hybridization approach of these algorithms using a neural network technique. The objective is to propose a centralized pre-processing procedure that allows, according to the self-reconfiguration problem, to determine which algorithm is most suitable. We applied the Neural Network technique to two self-reconfiguration algorithms: C2SR and TBSR. The obtained results show that the machine learning tool succeeds 96.67% of the time to determine the suitable algorithm based on the initial and the final shape. Consequently, using machine learning directly leads to the reduction of the required number of moves for the reconfiguration.</p>

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Machine learning for modular robots self-reconfiguration problem

  • 期刊论文

  • 发表日期：

    2025年3月12日

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  • Baptiste Buchi,
  • Hakim Mabed,
  • Frédéric Lassabe,
  • Jaafar Gaber

摘要

Self-reconfiguration of modular robots is one of the most challenging problems in the robotics field. The objective is to determine how a set of identical modular robots, with local knowledge of the system and limited capacities, can reorganize themselves into a target topology or shape. The problem has received great interest from the research community giving birth to many centralized and distributed algorithms. However, the lack of comparative study of these algorithms makes it difficult to choose one when faced with a given configuration. In this paper, we present a kind of high-level hybridization approach of these algorithms using a neural network technique. The objective is to propose a centralized pre-processing procedure that allows, according to the self-reconfiguration problem, to determine which algorithm is most suitable. We applied the Neural Network technique to two self-reconfiguration algorithms: C2SR and TBSR. The obtained results show that the machine learning tool succeeds 96.67% of the time to determine the suitable algorithm based on the initial and the final shape. Consequently, using machine learning directly leads to the reduction of the required number of moves for the reconfiguration.