论文标题
生物启发的中央模式发生器中的快速有节奏的夹带
Rapid rhythmic entrainment in bio-inspired central pattern generators
论文作者
论文摘要
将运动夹带到定期刺激中是人类的特征智能行为，也是适应性机器人技术的重要目标。我们演示了由修饰的Matsuoka神经元组成的四倍的中央模式发生器（CPG），该神经元自发地将其振荡周期调整为周期性输入信号的振荡周期。这是通过借助过滤网络以及具有补品输入依赖性振荡期的神经模型来完成的。我们首先使用NSGA3算法来发展CPG参数，使用单独的适应性功能，以进行周期可调性，肢体同质性和步态稳定性。然后从Pareto前部选择了四个CPG，最大化适应性功能的不同加权平均值，并将每个CPG用作优化滤波器网络的基础。为每个滤镜网络测试了不同数量的神经元。我们发现时期的可调性特别有助于强大的夹带，比步态更容易夹住步态，并且滤波器网络中的更多神经元有益于预处理输入信号。我们提供的系统可以与感觉反馈结合使用，以允许步行机器人中的低水平自适应和健壮的行为。
Entrainment of movement to a periodic stimulus is a characteristic intelligent behaviour in humans and an important goal for adaptive robotics. We demonstrate a quadruped central pattern generator (CPG), consisting of modified Matsuoka neurons, that spontaneously adjusts its period of oscillation to that of a periodic input signal. This is done by simple forcing, with the aid of a filtering network as well as a neural model with tonic input-dependent oscillation period. We first use the NSGA3 algorithm to evolve the CPG parameters, using separate fitness functions for period tunability, limb homogeneity and gait stability. Four CPGs, maximizing different weighted averages of the fitness functions, are then selected from the Pareto front and each is used as a basis for optimizing a filter network. Different numbers of neurons are tested for each filter network. We find that period tunability in particular facilitates robust entrainment, that bounding gaits entrain more easily than walking gaits, and that more neurons in the filter network are beneficial for pre-processing input signals. The system that we present can be used in conjunction with sensory feedback to allow low-level adaptive and robust behaviour in walking robots.
