Autonomous walkers and explorers: collective dynamics of coupled oscillators for autonomous robot locomotion and coordination

自动化的进步是通过将一些高风险任务委托给自主系统来提高生活质量的基础。例子包括机器人助手、有毒环境中的维护和监测以及受污染生态系统的质量控制。此外，机器人系统可以与生物系统耦合，以在特定场景中共同驱动它们，例如人群动态和控制，例如在由突发危险引起的疏散情况下。在设计一类专用于这些操作的自主系统时，定义的基本特征是可靠地协商不断变化的环境的能力，相对于标称操作条件具有高度的不确定性。这对设计运动系统提出了一系列挑战，因为机械效率方面的典型约束通过高度专业化得到最佳满足，而鲁棒性和多功能性则通过低专业化和在不同环境中的最佳平均行为方面的权衡得到很好的解决。强大的运动功能已经获得了通过进化过程中的小规模的生物结构，通过发展的毛发状结构，如纤毛和鞭毛。最近的研究揭示了这些结构的基本性质和普遍性，并发现了其他功能，如微尺度的流体运输，以及更复杂的功能，如管理异质生物系统中的共生相互作用，以及胚胎发育过程的调节。这类系统的协调动力学的高通用性和鲁棒性使其具有吸引力的应用，包括运动机器人的特点是低专业化和多功能。因此，至关重要的是研究耦合的基本物理，可以诱导出现的协调和集体动力学的波传播现象，适合运动的形式，通知鲁棒的自主步行器的设计具有多功能的特点。 对这类耦合振荡器系统中出现的协调现象的非线性动力学的研究扩展到自主机器人网络，即从步行协调（个人）到面向任务的任务中的群体协调，这些任务可以被广泛定义，例如在监测脆弱环境和随后的有针对性的干预方面。基于中央模式发生器模型的机器学习已被应用于机器人技术，以模仿动物运动背后的基本神经结构。深度学习与集体运动的潜在非线性动力学之间的联系将被研究，以深入了解自然界中出现集体模式的必要性，以及适合再现它们的神经网络架构。

Advances in automation are fundamental in contributing to the improvement of the quality of life, by delegating to autonomous systems several high-risk tasks. Examples involve robotic assistants, maintenance and monitoring in toxic environments, and quality control of contaminated ecosystems. Additionally, robotic systems can be coupled with biological systems, to co-drive them in specific scenarios such as crowd dynamics and control, for example in evacuation situations caused by sudden hazards. In designing a class of autonomous systems dedicated to these operations, a defining fundamental characteristic is the capability of reliably negotiating an evolving environment, with high degrees of uncertainty with respect to nominal operating conditions. This poses a set of challenges in designing a locomotion system, since typical constraints in terms of mechanical efficiency are optimally fulfilled through high-specialization, whereas robustness and versatility are well addressed through low-specialization and trade-offs in terms of best averaged behavior across different environments. Robust locomotion features have been acquired through evolutionary processes by small scale biological structures through the development of hair-like structures such as cilia and flagella. Recent research revealed the fundamental nature and ubiquity of these structures, with the discovery of additional functions such as fluidic transport at the microscale, as well as more complex functions such as managing of symbiotic interactions in heterogeneous biosystems, and the regulation of processes in embryonic development. The high versatility and robustness of the coordinated dynamics of this class of systems make it attractive for applications that include locomotion robotics characterized by low-specialization and multi-function. It is therefore crucial to study the fundamental physics of the coupling that can induce the emergence of coordination and collective dynamics in the form of wave propagating phenomena that are suitable for locomotion, to inform the design of robust autonomous walkers with versatile characteristics.  The study of the nonlinear dynamics of emerging coordination phenomena in this class of systems of coupled oscillators extends to networks of autonomous robots, that is, from coordination for walking (individual) to group coordination in task-oriented missions that may be broadly defined, for example in terms of monitoring vulnerable environments and subsequent targeted interventions. Machine learning based on central pattern generator models has been applied to robotics to mimic the fundamental neural structures behind animal locomotion. The connection between deep learning and the underlying nonlinear dynamics of collective motions will be investigated to gain insights about the necessity of collective patterns emerging in nature, and about neural networks architectures suitable to reproduce them.