Modeling the fractal development of modular robots

Abstract

Modeling and controlling self-reconfiguration of modular robots is still a challenging problem in the field of distributed control. The two main constrains are the design of target shapes and the absence of global state for decentralized modules. We present a new way for those two problems inspired from the developmental process of plant growth. As a mathematical theory of plant development, L-systems capture the essence of growth process. We extend L-systems to the self-reconfiguration process of modules robots. Target configurations will be described in a string of symbols, and robotic structures capture fractal characters through the rewriting function. Extended graphical interpretation of Lsystem symbols can generate module-level predictions about robotic global states. Simulations of different selfreconfiguration processes illustrate the proposed method.

Achievements and future directions in self‐reconfigurable modular robotic systems

“…Another challenge is related to the difficulties in conducting experiments with a high number of modules. Simulations with many modules may work, but when it comes to real-world experiments, the number of modules cannot reach to desired levels because of the limited computation time (Bie et al, 2019(Bie et al, , 2017Holobut et al, 2020). In the near future, it is expected that latticetype formed micromodules adaptable to various environments will be introduced in parallel with the developments in computer science.…”

“…The self-reconfiguration of the 3D oriented modules is studied by introducing the Lindenmayer systems, where the mathematics of L-system fractals can interact with the efforts of developing controllers for the SRMRs (Bie et al, 2017). A stochastic algorithm for the navigation control of a snake-like SRMR, the KAIRO robot (Pfotzer et al, 2012(Pfotzer et al, , 2015, is introduced in Pfotzer et al (2017).…”

“…The stochastic methods proposing a self-reconfiguration process of SRMRs, usually use high number of modules acting together (Bie et al, 2019(Bie et al, , 2017Fitch & Butler, 2008;Holobut et al, 2020; Rubenstein et al, 2014). The biologically inspired meta-heuristic search algorithms are applied for biologically inspired swarm functionalities of modules.…”

“…A stochastic algorithm for the navigation control of a snake‐like SRMR, the KAIRO robot (Pfotzer et al, 2012, 2015), is introduced in Pfotzer et al (2017). The stochastic methods proposing a self‐reconfiguration process of SRMRs, usually use high number of modules acting together (Bie et al, 2019, 2017; Fitch & Butler, 2008; Holobut et al, 2020; Rubenstein et al, 2014). The biologically inspired meta‐heuristic search algorithms are applied for biologically inspired swarm functionalities of modules.…”

Natural Growth‐Inspired Distributed Self‐Reconfiguration of UBot Robots

“…For implementation reasons, turtle interpretations are extended to lead the self-reconfiguration process of modular robots [28,39]. The module with L-system symbols, called turtle module, does the moving search work as turtles in graphical interpretation and attaches new modules at neighboring lattice as L-systems described.…”