MobileCharger is a novel mobile charging robot with an Inverted Delta actuator for safe and robust energy transfer between two mobile robots. The RGB-D camera-based computer vision system allows to detect the electrodes on the target mobile robot using a convolutional neural network (CNN). The embedded high-fidelity tactile sensors are applied to estimate the misalignment between the electrodes on the charger mechanism and the electrodes on the main robot using CNN based on pressure data on the contact surfaces. Thus, the developed vision-tactile perception system allows precise positioning of the end effector of the actuator and ensures a reliable connection between the electrodes of the two robots. The experimental results showed high average precision (84.2%) for electrode detection using CNN. The percentage of successful trials of the CNN-based electrode search algorithm reached 83% and the average execution time accounted for 60 s. MobileCharger could introduce a new level of charging systems and increase the prevalence of autonomous mobile robots.
翻译：移动充电器是一种新型的移动充电机器人,使用一个逆向德尔塔动能器,用于在两个移动机器人之间安全、稳健的能量传输。RGB-D摄像机式计算机视像系统能够利用一个进化神经网络(CNN)探测目标移动机器人上的电极。嵌入的高纤维触动传感器用于根据接触表面的压力数据使用CNN来估计充电器装置上的电极与主机器人上电极之间的不匹配情况。因此,发达的视觉触觉系统可以精确定位动能器的终端效应器,并确保两个机器人的电极之间的可靠连接。实验结果显示,使用CNN进行电极探测的平均精度很高(84.2%)。CNN电极搜索算法的成功试验百分比达到83%,平均执行时间为60秒。移动控制器可以引入新的充电系统,增加自动移动机器人的流行率。
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