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.

通过将一些高风险任务委托给自主系统，自动化技术的进步对提高生活质量起到了至关重要的作用。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相结合，在特定情况下共同驱动它们，例如人群动态和控制，例如在突发危险造成的疏散情况下。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能够可靠地适应不断变化的环境，并且相对于标称操作条件具有高度的不确定性。这给设计运动系统带来了一系列挑战，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得到很好的解决，并在不同环境下的最佳平均行为方面进行权衡。强健的运动特征是通过进化过程获得的小尺度生物结构，通过发展毛发状结构，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存在，并发现了其他功能，如微观尺度上的流体传输，以及更复杂的功能，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。这类系统的高通用性和鲁棒性使其对包括低专业化和多功能运动机器人在内的应用具有吸引力。因此，研究耦合的基本物理是至关重要的，它可以诱导以适合运动的波传播现象形式出现的协调和集体动力学，为设计具有多用途特性的鲁棒自主行走器提供信息。在这类耦合振荡器系统中出现的协调现象的非线性动力学研究扩展到自主机器人网络，即从步行（个人）的协调到任务导向任务中的群体协调，这些任务可能被广泛定义，例如在监测脆弱环境和随后的目标干预方面。基于中枢模式生成器模型的机器学习已被应用于机器人技术，以模拟动物运动背后的基本神经结构。深度学习和潜在的集体运动的非线性动力学之间的联系将被研究，以获得关于自然中出现的集体模式的必要性的见解，以及适合复制它们的神经网络架构。