Abstract
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.
Highlights
Producing autonomous adaptive robots is a huge challenge
PolyBot is capable of self-reconfiguration by changing its geometry and locomotion mode depending on the terrain type – rolling over flat terrain, earthworm to move around obstacles, and a spider to step over hilly terrain
The majority of modular robotic behaviors; such as self-reconfiguration and self-repair were implemented in simulation despite the existence of a physical prototype; which can be considered as a reality gap
Summary
Producing autonomous adaptive robots is a huge challenge. Mainstream robots use machine learning to produce adaptive behavior to simulate biological aspects while neglecting the autonomous side of it. The previous work categorized modular robotic systems based on the dominant feature of each robot. It focused on the modular robots while overlooking the evolutionary aspect of these systems. Evolutionary robotics was discussed as a potential technique that can be applied to evolve the robots control systems. J Intell Robot Syst (2019) 95:815–828 modular robotic systems that applied evolutionary algorithms to improve the resulting modular robotic structure and behavior with an emphasis on applying evolutionary computation to enhance the modular robotic task based design. The current state of the art and challenges are discussed
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