TY - JOUR
T1 - Practical hardware for evolvable robots
AU - Angus, Mike
AU - Buchanan, Edgar
AU - Le Goff, Léni K.
AU - Hart, Emma
AU - Eiben, Agoston E.
AU - De Carlo, Matteo
AU - Winfield, Alan F.
AU - Hale, Matthew F.
AU - Woolley, Robert
AU - Timmis, Jon
AU - Tyrrell, Andy M.
N1 - Funding Information:
This work is funded by EPSRC ARE project, EP/R03561X, EP/R035733, EP/R035679, and the Vrije Universiteit Amsterdam.
Publisher Copyright:
Copyright © 2023 Angus, Buchanan, Le Goff, Hart, Eiben, De Carlo, Winfield, Hale, Woolley, Timmis and Tyrrell.
PY - 2023/8/21
Y1 - 2023/8/21
N2 - The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.
AB - The evolutionary robotics field offers the possibility of autonomously generating robots that are adapted to desired tasks by iteratively optimising across successive generations of robots with varying configurations until a high-performing candidate is found. The prohibitive time and cost of actually building this many robots means that most evolutionary robotics work is conducted in simulation, but to apply evolved robots to real-world problems, they must be implemented in hardware, which brings new challenges. This paper explores in detail the design of an example system for realising diverse evolved robot bodies, and specifically how this interacts with the evolutionary process. We discover that every aspect of the hardware implementation introduces constraints that change the evolutionary space, and exploring this interplay between hardware constraints and evolution is the key contribution of this paper. In simulation, any robot that can be defined by a suitable genetic representation can be implemented and evaluated, but in hardware, real-world limitations like manufacturing/assembly constraints and electrical power delivery mean that many of these robots cannot be built, or will malfunction in operation. This presents the novel challenge of how to constrain an evolutionary process within the space of evolvable phenotypes to only those regions that are practically feasible: the viable phenotype space. Methods of phenotype filtering and repair were introduced to address this, and found to degrade the diversity of the robot population and impede traversal of the exploration space. Furthermore, the degrees of freedom permitted by the hardware constraints were found to be poorly matched to the types of morphological variation that would be the most useful in the target environment. Consequently, the ability of the evolutionary process to generate robots with effective adaptations was greatly reduced. The conclusions from this are twofold. 1) Designing a hardware platform for evolving robots requires different thinking, in which all design decisions should be made with reference to their impact on the viable phenotype space. 2) It is insufficient to just evolve robots in simulation without detailed consideration of how they will be implemented in hardware, because the hardware constraints have a profound impact on the evolutionary space.
KW - autonomous robot fabrication
KW - evolutionary robotics
KW - hardware constraints
KW - hardware design
KW - modular robots
KW - robot manufacturability
UR - http://www.scopus.com/inward/record.url?scp=85169680478&partnerID=8YFLogxK
U2 - 10.3389/frobt.2023.1206055
DO - 10.3389/frobt.2023.1206055
M3 - Article
C2 - 37670906
AN - SCOPUS:85169680478
SN - 2296-9144
VL - 10
JO - Frontiers in Robotics and AI
JF - Frontiers in Robotics and AI
M1 - 1206055
ER -