Leung Binggwong, Gorb Stanislav, Manoonpong Poramate
Bio-inspired Robotics and Neural Engineering Lab, School of Information Science and Technology, Vidyasirimedhi Institute of Science and Technology, Rayong, 21210, Thailand.
Functional Morphology and Biomechanics, Zoological Institute, Kiel University, 24118, Kiel, Germany.
Adv Sci (Weinh). 2024 Dec;11(47):e2408080. doi: 10.1002/advs.202408080. Epub 2024 Oct 30.
Dung beetles impressively coordinate their 6 legs to effectively roll large dung balls. They can also roll dung balls varying in the weight on different terrains. The mechanisms underlying how their motor commands are adapted to walk and simultaneously roll balls (multitasking behavior) under different conditions remain unknown. This study unravels the mechanisms of how dung beetles roll dung balls and adapt their leg movements to stably roll balls over different terrains for multitasking robots. A modular neural-based loco-manipulation control inspired by and based on ethological observations of the ball-rolling behavior of dung beetles is synthesized. The proposed neural-based control contains a central pattern generator (CPG) module, a pattern formation network (PFN) module, and a robot orientation control (ROC) module. The integrated control mechanisms can control a dung beetle-like robot (ALPHA) with biomechanical feet to perform adaptive (multitasking) loco-manipulation (walking and ball-rolling) on various terrains (flat and uneven). It can deal with different ball weights (2.0 and 4.6 kg) and ball types (soft and rigid). The control mechanisms can serve as guiding principles for solving sensory-motor coordination for multitasking robots. Furthermore, this study contributes to biological research by enhancing the understanding of sensory-motor coordination for adaptive (multitasking) loco-manipulation behavior in animals.
蜣螂能令人赞叹地协调其6条腿,以有效地滚动大粪球。它们还能在不同地形上滚动重量各异的粪球。其运动指令如何在不同条件下适应行走并同时滚动粪球(多任务行为)的潜在机制仍不为人知。本研究揭示了蜣螂滚动粪球以及如何调整腿部动作以便在不同地形上稳定滚动粪球的机制,为多任务机器人提供了借鉴。基于对蜣螂滚球行为的行为学观察,合成了一种模块化的基于神经的运动操纵控制方法。所提出的基于神经的控制方法包含一个中央模式发生器(CPG)模块、一个模式形成网络(PFN)模块和一个机器人方向控制(ROC)模块。这种集成控制机制能够控制一个具有生物力学足部的类似蜣螂的机器人(ALPHA),使其在各种地形(平坦和不平坦)上执行适应性(多任务)运动操纵(行走和滚球)。它能够应对不同的球重(2.0千克和4.6千克)以及球的类型(软球和硬球)。这些控制机制可作为解决多任务机器人感觉运动协调问题的指导原则。此外,本研究通过增进对动物适应性(多任务)运动操纵行为的感觉运动协调的理解,为生物学研究做出了贡献。