Mompó Alepuz Adrià, Papageorgiou Dimitrios, Tolu Silvia
Department of Electrical and Photonics Engineering, Technical University of Denmark, Copenhagen, Denmark.
Front Neurorobot. 2024 Aug 19;18:1395617. doi: 10.3389/fnbot.2024.1395617. eCollection 2024.
Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.
复杂的机器人系统，如仿人机器人手、软体机器人和步行机器人，由于其高维度和严重的非线性，带来了具有挑战性的控制问题。传统的基于模型的反馈控制器表现出鲁棒性和稳定性，但难以应对随着维度增加而不断升级的系统设计和调谐复杂性。相比之下，诸如人工神经网络等数据驱动方法擅长表示高维数据，但缺乏鲁棒性、泛化能力和实时适应性。为应对这些挑战，研究人员将重点转向生物范式，从人体固有的卓越控制能力中汲取灵感。鉴于当前神经科学的见解，这激发了对旨在紧密模仿大脑运动功能的新控制方法的探索。最近对这些控制技术的研究已取得了有希望的成果，特别是在涉及轨迹跟踪和机器人运动的任务中。本文全面综述了仿生脑启发控制方法的主要趋势，以应对与控制复杂机器人系统相关的复杂性。
