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A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot
A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot
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A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot
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A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot
A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot

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A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot
A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot
Journal Article

A Self-Organizing Interaction and Synchronization Method between a Wearable Device and Mobile Robot

2016
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Overview
In the near future, we can expect to see robots naturally following or going ahead of humans, similar to pet behavior. We call this type of robots “Pet-Bot”. To implement this function in a robot, in this paper we introduce a self-organizing interaction and synchronization method between wearable devices and Pet-Bots. First, the Pet-Bot opportunistically identifies its owner without any human intervention, which means that the robot self-identifies the owner’s approach on its own. Second, Pet-Bot’s activity is synchronized with the owner’s behavior. Lastly, the robot frequently encounters uncertain situations (e.g., when the robot goes ahead of the owner but meets a situation where it cannot make a decision, or the owner wants to stop the Pet-Bot synchronization mode to relax). In this case, we have adopted a gesture recognition function that uses a 3-D accelerometer in the wearable device. In order to achieve the interaction and synchronization in real-time, we use two wireless communication protocols: 125 kHz low-frequency (LF) and 2.4 GHz Bluetooth low energy (BLE). We conducted experiments using a prototype Pet-Bot and wearable devices to verify their motion recognition of and synchronization with humans in real-time. The results showed a guaranteed level of accuracy of at least 94%. A trajectory test was also performed to demonstrate the robot’s control performance when following or leading a human in real-time.