Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication
Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication
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DOI：
10.3390/robotics9040106
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发表时间：
2020-12-01
期刊：
影响因子：
3.7
通讯作者：
Tyrrell, Andy M.
中科院分区：
文献类型：
--
作者：
Buchanan, Edgar;Le Goff, Leni K.;Tyrrell, Andy M.
进化机器人的长期愿景是一种技术，能够使整个自主机器人生态系统在具有挑战性和动态的环境中长期生活和工作，而无需人类直接监督。进化机器人由于其在仿真中创造独特机器人设计的能力而得到广泛应用。最近的工作表明，在物理领域自主构建进化设计是可能的;然而，这带来了新的挑战：自主制造和装配过程引入了在仿真中不明显的新约束。为了解决这个问题，我们引入了一种新的方法来生产多样化但可制造的机器人。这个剧目是用来种子的进化循环，随后演变的机器人设计和控制器能够解决一个迷宫导航任务。我们表明，与随机初始化相比，使用多样化和可制造的种群播种可以加快收敛速度，并在某些任务上提高性能，同时保持可制造性。数据集：用于本研究的源代码可在此处获得：https://bitbucket.org/autonomousroboticsevolution/mdpi2020/
A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.Dataset: The source code used for this study is available here: https://bitbucket.org/autonomousroboticsevolution/mdpi2020/