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首页> 外文期刊>Frontiers in Neurorobotics >Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot
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Development and Training of a Neural Controller for Hind Leg Walking in a Dog Robot

机译：用于狗机器人中后腿行走的神经控制器的开发和培训

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Animals dynamically adapt to varying terrain and small perturbations with remarkable ease. These adaptations arise from complex interactions between the environment and biomechanical and neural components of the animal's body and nervous system. Research into mammalian locomotion has resulted in several neural and neuro-mechanical models, some of which have been tested in simulation, but few “synthetic nervous systems” have been implemented in physical hardware models of animal systems. One reason is that the implementation into a physical system is not straightforward. For example, it is difficult to make robotic actuators and sensors that model those in the animal. Therefore, even if the sensorimotor circuits were known in great detail, those parameters would not be applicable and new parameter values must be found for the network in the robotic model of the animal. This manuscript demonstrates an automatic method for setting parameter values in a synthetic nervous system composed of non-spiking leaky integrator neuron models. This method works by first using a model of the system to determine required motor neuron activations to produce stable walking. Parameters in the neural system are then tuned systematically such that it produces similar activations to the desired pattern determined using expected sensory feedback. We demonstrate that the developed method successfully produces adaptive locomotion in the rear legs of a dog-like robot actuated by artificial muscles. Furthermore, the results support the validity of current models of mammalian locomotion. This research will serve as a basis for testing more complex locomotion controllers and for testing specific sensory pathways and biomechanical designs. Additionally, the developed method can be used to automatically adapt the neural controller for different mechanical designs such that it could be used to control different robotic systems.
机译：动物非常容易地动态适应变化的地形和小的扰动。这些适应源于环境与动物身体和神经系统的生物力学和神经成分之间的复杂相互作用。对哺乳动物运动的研究已经产生了几种神经和神经机械模型，其中一些已经在仿真中进行了测试，但是在动物系统的物理硬件模型中却很少实现“合成神经系统”。原因之一是在物理系统中的实现并不简单。例如，很难制造出能够对动物模型进行建模的机器人致动器和传感器。因此，即使非常详细地了解感觉运动电路，那些参数也不适用，并且必须在动物的机器人模型中为网络找到新的参数值。该手稿演示了一种自动方法，该方法可以在由非尖峰泄漏积分神经元模型组成的合成神经系统中设置参数值。该方法通过首先使用系统模型来确定所需的运动神经元激活以产生稳定的步行来工作。然后，系统地调节神经系统中的参数，以使其产生与使用预期的感觉反馈确定的所需模式相似的激活。我们证明，开发的方法成功地产生了由人造肌肉驱动的狗状机器人后腿的自适应运动。此外，结果支持了当前哺乳动物运动模型的有效性。这项研究将作为测试更复杂的运动控制器以及测试特定的感觉途径和生物力学设计的基础。此外，开发的方法可用于自动使神经控制器适应不同的机械设计，从而可用于控制不同的机器人系统。

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