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

自主步行者和探索者：用于自主机器人运动和协调的耦合振荡器的集体动力学

• 批准号: RGPIN-2022-03921
• 负责人: Spinello, Davide
• 金额: $ 1.97万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=760118
• 关键词: Autonomous; walkers; explorers; collective; dynamics

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.

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，例如机器人助手、有毒环境中的维护和监控以及受污染生态系统的质量控制。可以与生物系统耦合，在特定场景（例如人群动态和控制）中共同驱动它们，例如在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，一个定义的基本特征是可靠地谈判不断发展的能力环境，标称操作条件具有高度的不确定性，这给运动系统的设计带来了一系列挑战，因为机械效率方面的典型约束是通过高度专业化得到最佳满足的，而鲁棒性和多功能性则通过良好的解决。最近的研究揭示了小规模生物结构通过纤毛和鞭毛等毛发状结构的进化过程获得了低专业化和不同环境中最佳平均行为的权衡。自然这些结构的普遍性，以及其他功能的发现，例如微尺度的流体运输，以及更复杂的功能，例如异质生物系统中共生相互作用的管理，以及胚胎发育过程的调节。​高度的多功能性和这类系统的协调动力学的鲁棒性使其对包括以低专业化和多功能为特征的运动机器人等应用具有吸引力，因此研究可以引起协调和集体出现的耦合的基本物理原理至关重要。适合运动的波传播现象形式的动力学，为具有多功能特性的鲁棒自主步行器的设计提供信息对此类耦合振荡器系统中新兴协调现象的非线性动力学的研究扩展到自主机器人网络。也就是说，从步行协调（个人）到以任务为导向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已经应用了基于中央模式生成器模型的机器学习。机器人模仿将研究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的集体模式的必要性，以及适合重现它们的神经网络架构。