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NEURAL CONTROL OF A THREE-LEGGED RECONFIGURABLE ROBOT WITH OMNIDIRECTIONAL WHEELS

机译:全环轮毂三腿可重构机器人的神经控制

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摘要

In this article, we present neural control of a three-legged reconfigurable robot with omnidirectional wheels. It is systematically synthesized based on a modular structure such that the neuromodules are small and their structure-function relationship can be understood. The resulting network consists of four main modules. A so-called minimal recurrent control (MRC) module is for sensory signal processing and state memorization. It directly drives the motion of two front wheels while a rear wheel is indirectly controlled through a velocity regulating network (VRN) module. In parallel, a simple neural oscillator network module serves as a central pattern generator (CPG) producing basic rhythmic signals for sidestepping where stepping directions are controlled by a phase switching network (PSN) module. The combination of these neuromodules generates various locomotion patterns. Applying sensor inputs to the neural controller enables the robot to avoid obstacles as well as a corner. The presented neuromodules are developed and firstly tested using a physical simulation environment, and then finally transferred to the real robot.
机译:在本文中,我们用全向轮子呈现三条腿可重构机器人的神经控制。基于模块化结构系统地合成,使得神经微小尺寸小,并且可以理解它们的结构功能关系。生成的网络由四个主模块组成。所谓的最小反复控制(MRC)模块是用于感官信号处理和状态记忆。它直接驱动两个前轮的运动,而后轮通过速度调节网络(VRN)模块间接控制。并行地,简单的神经振荡器网络模块用作产生基本节奏信号的中央图案发生器(CPG),用于搁置步进方向由相位切换网络(PSN)模块控制。这些神经微小的组合产生了各种运动模式。将传感器输入应用于神经控制器使机器人能够避免障碍物以及角落。呈现的神经形式率先使用物理仿真环境开发并首先测试,然后最终转移到真实机器人。

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