Self-Reconfigurable Robot Systems: PolyBot

Yim, Mark; Eldershaw, Craig; Zhang, Ying; Duff, David G.

doi:10.7210/jrsj.21.851

Cited by 24 publications

(14 citation statements)

References 3 publications

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“…Chain architectures are more versatile compared to other architectures due to their capa-bility of reaching any point in space through articulation, but they are more difficult to control and more expensive computationally to represent and analyze [27]. An example of a chain-based self-reconfigurable robot is PolyBot [33][34][35].…”

Section: Self-reconfigurationmentioning

confidence: 99%

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Evolutionary Modular Robotics: Survey and Analysis

Alattas

Patel

Sobh

2018

J Intell Robot Syst

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This paper surveys various applications of artificial evolution in the field of modular robots. Evolutionary robotics aims to design autonomous adaptive robots automatically that can evolve to accomplish a specific task while adapting to environmental changes. A number of studies have demonstrated the feasibility of evolutionary algorithms for generating robotic control and morphology. However, a huge challenge faced was how to manufacture these robots. Therefore, modular robots were employed to simplify robotic evolution and their implementation in real hardware. Consequently, more research work has emerged on using evolutionary computation to design modular robots rather than using traditional hand design approaches in order to avoid cognition bias. These techniques have the potential of developing adaptive robots that can achieve tasks not fully understood by human designers. Furthermore, evolutionary algorithms were studied to generate global modular robotic behaviors including; self-assembly, self-reconfiguration, self-repair, and self-reproduction. These characteristics allow modular robots to explore unstructured and hazardous environments. In order to accomplish the aforementioned evolutionary modular robotic promises, this paper reviews current research on evolutionary robotics and modular robots. The motivation behind this work is to identify the most promising methods that can lead to developing autonomous adaptive robotic systems that require the minimum task related knowledge on the designer side.

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Section: Self-reconfigurationmentioning

confidence: 99%

“…Currently the maximum number of modules utilized in one connected PolyBot system is 32 with each module having 1 DOF. The third generation deals with 200 modules to show a variety of capabilities, including moving like a snake, lizard, or centipede as well as humanoid walking and rolling in a loop [33][34][35][36].…”

Section: Polybot -2000mentioning

confidence: 99%

Evolutionary Modular Robotics: Survey and Analysis

Alattas

Patel

Sobh

2018

J Intell Robot Syst

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“…Furthermore, the systems are expected to reach unforeseen functionalities, self-repair themselves when damaged, efficiently reuse components and self-replicate. All these potentials however can only be reached when overcoming challenges in mechanical design and control of the system and its subunits [3], [4].…”

Section: Introductionmentioning

confidence: 99%

“…), as well as algorithms for control problems like self-assembly or self-reconfiguration [5]. Currently, a major limitation of multi-cellular systems is the general lack of flexibility when increasing the number of cells [3], [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Soft Cell Simulator: A tool to study soft multi-cellular robots

Germann

Maesani

Stockli

et al. 2013

2013 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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Abstract-Modular or multi-cellular robots hold the promise to adapt their morphology to task and environment. However, research in modular robotics has traditionally been limited to mechanically non-adaptive systems due to hard building blocks and rigid connection mechanisms. To improve adaptation and global flexibility, we suggest the use of modules made of soft materials. Thanks to recent advances in fabrication techniques the development of soft robots without spatial or material constraints is now possible. In order to exploit this vast design space, computer simulations are a time and cost-efficient tool. However, there is currently no framework available that allows studying the dynamics of soft multi-cellular systems. In this work, we present our simulation framework named Soft Cell Simulator (SCS) that enables to study both mechanical design parameters as well as control problems of soft multi-cellular systems in an time-efficient yet globally accurate manner. Its main features are: (i) high simulation speed to test systems with a large number of cells (real-time up to 100 cells), (ii) large non-linear deformations without module self-penetration, (iii) tunability of module softness (0-500 N/m), (iv) physicsbased module connectivity, (v) variability of module shape using internal actuators. We present results that validate the plausibility of the simulated soft cells, the scalability as well as the usability of the simulator. We suggest that this simulator helps to master and leverage the potential of the vast design space to generate novel soft multi-cellular robots.

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“…Pioneering examples of self-reconfigurable robots are MTRAN [5] and PolyBot [6]. An overview of existing systems and characteristics can be found in the works of Kamimura et al [5] and Yim et al [7].…”

mentioning

confidence: 99%