Bio-micro-robots as building blocks for smart materials

我们的目标是为自组织的微机器人群设计控制器，这些微机器人群具有从局部相互作用中产生的特定集体动力学。特别是，我们专注于大型机器人群，数百万微米大小的微型机器人由附着在机器人上的DNA链的反应扩散动力学控制的蔗糖珠组成。我们扩展了前一年实现的方法，通过使用优化和质量多样性算法以及在模拟中测试有前途的解决方案来自动设计这些基于dna的控制器。然而，这种方法在计算上特别昂贵，特别是因为涉及复杂的反应扩散模拟。因此，我们提出了一种使用gpu与CUDA框架来提高此类模拟计算效率的新方法（论文“加速gpu与CUDA的反应扩散模拟的有限元方法”）。我们自动设计机器人控制器的方法的另一个限制是，用户以前必须手动提供分数（“特征描述符”）来量化测试解决方案的多样性，这是质量多样性算法的一个典型方面。我们描述了一种新的方法来自动找到目标问题的这些多样性分数（论文“多容器质量多样性算法的集成特征提取”）。

Our aim was to design controllers for self-organizing swarms of micro-robots with specific collective dynamics emerging from local interactions. In particular, we focused on large robotic swarms with millions micrometer-sized micro-robots composed of sepharose beads controlled through the reaction-diffusion dynamics of DNA strands grafted into the robots. We extended the methodology implemented the previous year to automatically design these DNA-based controllers, by using optimization and quality-diversity algorithms and testing promising solutions in simulations. However this approach was especially computationally expensive, in particular because of the complex reaction-diffusion simulations involved. As such, we presented a new method to improve the computational efficiency of such simulations using GPUs with the CUDA framework (paper "Accelerating the Finite-Element Method for Reaction-Diffusion Simulations on GPUs with CUDA"). Another limitation of our methodology to automatically design robot controllers was that the user previously had to manually provide scores ("feature descriptors") quantifying how diverse the tested solutions were, a typical aspect of quality-diversity algorithms. We described a new methodology to find automatically these diversity scores for a target problem (paper "Ensemble Feature Extraction for Multi-Container Quality-Diversity Algorithms").