Practical hardware for evolvable robots.
Practical hardware for evolvable robots.
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DOI：
10.3389/frobt.2023.1206055
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发表时间：
2023
影响因子：
3.4
通讯作者：
Tyrrell, Andy M
中科院分区：
文献类型：
--
作者：
Angus, Mike;Buchanan, Edgar;Le Goff, Leni K;Hart, Emma;Eiben, Agoston E;De Carlo, Matteo;Winfield, Alan F;Hale, Matthew F;Woolley, Robert;Timmis, Jon;Tyrrell, Andy M
进化机器人领域提供了自主生成机器人的可能性，这些机器人通过迭代优化具有不同配置的连续几代机器人，直到找到高性能的候选人，从而适应所需的任务。实际建造这么多机器人的时间和成本令人望而却步，这意味着大多数进化机器人工作都是在模拟中进行的，但要将进化机器人应用于现实世界的问题，必须在硬件上实现，这就带来了新的挑战。本文详细探讨了一个例子系统的设计，实现不同的进化机器人机构，特别是如何与进化过程中相互作用。我们发现，硬件实现的每个方面都引入了改变进化空间的约束，探索硬件约束和进化之间的相互作用是本文的主要贡献。在仿真中，任何可以由合适的遗传表示定义的机器人都可以实现和评估，但在硬件中，现实世界的限制，如制造/装配约束和电力输送意味着许多机器人无法建造，或者在操作中会出现故障。这就提出了一个新的挑战，即如何将可进化表型空间内的进化过程限制在实际可行的区域：可行表型空间。引入表型过滤和修复方法来解决这个问题，并发现降低了机器人种群的多样性，并阻碍了探索空间的遍历。此外，被发现的硬件约束所允许的自由度是不匹配的形态变化的类型，这将是最有用的目标环境。因此，进化过程产生具有有效适应能力的机器人的能力大大降低。由此得出的结论是双重的。1)为进化机器人设计硬件平台需要不同的思维，所有的设计决策都应该参考它们对可行表型空间的影响。2)仅仅在仿真中进化机器人而不详细考虑它们将如何在硬件中实现是不够的，因为硬件约束对进化空间有着深远的影响。
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.