Schmidt Annika, Feldotto Benedikt, Gumpert Thomas, Seidel Daniel, Albu-Schäffer Alin, Stratmann Philipp
Sensor Based Robotic Systems and Intelligent Assistance Systems, Department of Informatics, Technical University of Munich, Garching, Germany.
German Aerospace Center (DLR), Institute of Robotics and Mechatronics, Weßling, Germany.
Front Neurorobot. 2021 Dec 10;15:762431. doi: 10.3389/fnbot.2021.762431. eCollection 2021.
To control highly-dynamic compliant motions such as running or hopping, vertebrates rely on reflexes and Central Pattern Generators (CPGs) as core strategies. However, decoding how much each strategy contributes to the control and how they are adjusted under different conditions is still a major challenge. To help solve this question, the present paper provides a comprehensive comparison of reflexes, CPGs and a commonly used combination of the two applied to a biomimetic robot. It leverages recent findings indicating that in mammals both control principles act within a low-dimensional control submanifold. This substantially reduces the search space of parameters and enables the quantifiable comparison of the different control strategies. The chosen metrics are motion stability and energy efficiency, both key aspects for the evolution of the central nervous system. We find that neither for stability nor energy efficiency it is favorable to apply the state-of-the-art approach of a continuously feedback-adapted CPG. In both aspects, a pure reflex is more effective, but the pure CPG allows easy signal alteration when needed. Additionally, the hardware experiments clearly show that the shape of a control signal has a strong influence on energy efficiency, while previous research usually only focused on frequency alignment. Both findings suggest that currently used methods to combine the advantages of reflexes and CPGs can be improved. In future research, possible combinations of the control strategies should be reconsidered, specifically including the modulation of the control signal's shape. For this endeavor, the presented setup provides a valuable benchmark framework to enable the quantitative comparison of different bioinspired control principles.
为了控制诸如奔跑或跳跃等高度动态的顺应性运动，脊椎动物依靠反射和中枢模式发生器（CPG）作为核心策略。然而，解读每种策略对控制的贡献程度以及它们在不同条件下如何调整仍然是一项重大挑战。为了帮助解决这个问题，本文对应用于仿生机器人的反射、CPG以及两者常用的组合进行了全面比较。它利用了最近的研究结果，即哺乳动物中的这两种控制原理都在低维控制子流形内起作用。这大大减少了参数的搜索空间，并使得能够对不同的控制策略进行可量化比较。所选择的指标是运动稳定性和能量效率，这两个都是中枢神经系统进化的关键方面。我们发现，无论是对于稳定性还是能量效率，应用连续反馈自适应CPG的最先进方法都并非有利。在这两个方面，纯反射更有效，但纯CPG在需要时允许轻松改变信号。此外，硬件实验清楚地表明，控制信号的形状对能量效率有很大影响，而先前的研究通常只关注频率匹配。这两个发现都表明，目前用于结合反射和CPG优势的方法可以改进。在未来的研究中，应重新考虑控制策略的可能组合，特别是包括控制信号形状的调制。为此，本文提出的设置提供了一个有价值的基准框架，以实现对不同仿生控制原理的定量比较。
