Department of Computer Science and Information Engineering, National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
Intelligent Recognition Industry Service Research Center (IRIS Research Center), National Yunlin University of Science and Technology, Yunlin 64002, Taiwan.
Sensors (Basel). 2018 Nov 13;18(11):3911. doi: 10.3390/s18113911.
Cleaning robot has the highest penetration rate among the service robots. This paper proposes a high-efficiency mechanism for an intelligent cleaning robot automatically returns to charging in a short time when the power is insufficient. The proposed mechanism initially combines the robot's own motor encoder with neural network linear regression to calculate the moving distance and rotation angle for the location estimation of the robot itself. At the same time, a self-rotating camera is applied to scan the number of infrared spots on the docking station to find the location of the docking station so that the cleaning robot returns to charging properly in two stages, existing infrared range and extended infrared range. In addition, six-axis acceleration and ultrasound are both applied to deal with the angle error that is caused by collision. Experimental results show that the proposed recharging mechanism significantly improves the efficiency of recharging.
清洁机器人在服务机器人中渗透率最高。本文提出了一种高效的机制,使智能清洁机器人在电量不足时能够快速自动返回充电。该机制最初将机器人自身的电机编码器与神经网络线性回归相结合,以计算移动距离和旋转角度,从而对机器人自身的位置进行估计。同时,应用自旋转相机扫描对接站的红外点数量,以找到对接站的位置,从而使清洁机器人在两个阶段(现有红外范围和扩展红外范围)中正确返回充电。此外,还应用了六轴加速度计和超声波来处理碰撞引起的角度误差。实验结果表明,所提出的充电机制显著提高了充电效率。