Study on networked collaboration using haptic devices

由于最近通信网络的进步，现在不仅传输像语音和图像这样的传统媒体而且传输作为触觉信息的其他流媒体变得现实。这是一个非常具有挑战性的任务和一些实验结果的触觉信息的使用在网络上已经提出。由于几乎所有的研究都是由机器人研究人员进行的，他们“无辜地”忽略了延迟，抖动和数据包丢失。也就是说，他们把网络视为理想。然而，真实的网络远非理想，网络损伤导致触觉应用的服务质量（QoS）严重下降。从这个角度出发，我们研究了网络损伤，特别是延迟，抖动和数据包丢失，在互联网上的触觉协作的影响。例如，我们发现100 [msec]的延迟在触觉协作中是非常关键的，并且如果它大于100 [msec.]，我们无法以任何方式保持良好的QoS。在此基础上，提出了几种防止这种损害的对策。针对丢包问题，提出了一种实用的航位推算方法。针对带宽的限制，提出了一种基于航位推算和二阶预测的带宽压缩方法。针对抖动问题，我们首先提出了一种实用的媒体内同步机制。我们还提出了一个跨媒体同步机制。最后，将上述对策集成到一个实际系统中。该方法具有很强的鲁棒性，目前已被广泛应用于网络触觉协作系统中的网络损伤对策。

Owing to recent advance of communication networks, it is now becoming realistic to transmit not only conventional media like voice and images but also other streaming media as haptic information. This is a very challenging task and a few experimental result of the usage of haptic information over networks have been proposed. Since almost all the researches were by the community of researchers on robotics, they "innocently" ignored the delay, jitter and packet loss. Namely, they treated the network as ideal. However, the real networks are far from ideal and network impairment causes the heavy deteriation of Quality of Services (QoS) on haptic application. From this viewpoint, we study the effect of network impairment, especially delay, jitter and packet loss, on the haptics collaboration on Internet. For example, we found that delay of 100 [msec] is very crucial in haptic collaboration and if it is more than 100 [msec.], we cannot maintain good QoS in any way. Then, based on them, several countermeasures against such impairment were proposed. Against packet loss, we proposed a practical dead-reckoning method. Against, the limitation of bandwidth, we proposed a bandwidth compression method based on dead-reckoning and the 2nd order prediction. Against jitter, we first proposed a practical intra-media synchronization mechanism. We also proposed an inter-media synchronization mechanism. Finally, we integrated the above mentioned countermeasure into one practical system. It is very and robust and happily, it is now becoming widely used as the de-fact of the countermeasure against network impairment on networked haptic collaboration systems.