**6. Challenges and future directions of QoS control**

As described in the previous section, although we proposed serval types of QoS control for the remote robot system with force feedback, there still exist many challenges. In this section, we discuss the challenges and future directions of QoS control.

## **6.1 Integration of QoS control and stabilization control**

In order to achieve stable and high quality of service, we also need to carry out stabilization control, and it is important to integrate the QoS control with stabilization control. To integrate the QoS control with stabilization control, we can carry out QoS control and stabilization control independently, or integrate QoS control into stabilization control for the system. We investigate the effects by integrating the position control using force information as QoS control with stabilization control with filters [9], and experimental results show that the effect when we carry out QoS control in the loop of stabilization control is better than that when we carry out QoS control and stabilization control independently. It is important to investigate the effect by using other types of QoS control and stabilization control.

### **6.2 Multilateral control**

In this chapter, we introduced the QoS control only in a communication loop, which is between a haptic interface device and a remote robot (see **Figure 3**). However, in the remote operation using multiple systems, there are multiple loops caused by communication in the systems (see **Figure 4**), and there exist interrelationships among the loops. This means that we need to carry out multilateral control for QoS as well as stabilizations and it becomes complex and difficult.

#### **6.3 Application of big data, cloud computing and AI technologies**

In order to improve the efficiency of QoS control, we need to take account of many factors, for example, contents of work, movement speed, room temperature and wind [27]. Therefore, big data [28], cloud computing [29], and AI (Artificial Intelligence) [30] technologies such as neural network, fuzzy theory, and genetic algorithm can be useful methods for efficient control. The necessary information for QoS control can be transmitted to a cloud server, and the information can be combined as big data for analysis and applied as training data and evaluation data. Efficient QoS control can be expected by using AI after studying the training data. Also, in order to solve the problem of AI computing, we can apply AI chips [31],

**65**

*QoS Control in Remote Robot Operation with Force Feedback*

which realizes edge AI computing, to the remote robot terminal to improve the

and the possibility of the application over the mobile network increases.

Since we need to transmit the necessary information for QoS control to a cloud server, it is important to consider the safety and security of data. Also, in many situations, we need to use movable robots as remote robots. This means that we may need to consider the QoS control in wireless and/or mobile networks. This is because a 5G network [32] which is wideband and low latency becomes available

In addition, we need to carry out QoE assessment to investigate the effects of QoS control and to clarify how to set parameter values optimally under each type of the control as well as QoS assessment at lower layers. QoE subjective assessment is the most important because the assessment can reflect end users' opinions directly

In this chapter, we focus on QoS control for remote robot operation. We introduce our remote robot system with force feedback which we constructed to study QoS control. We also present the expected applications and the problems to be solved for widespread application of remote robot system with force feedback. We mainly focused on the problems of network delay, delay jitter, and packet loss. We explain several types of QoS control which we previously proposed to solve the problems. Finally, we also discuss the challenges and future directions of QoS

For the future plan of our study, we need to solve the problems described in

The authors thank Prof. Hitoshi Watanabe of Tokyo University of Science, Prof. Hitoshi Ohnishi of the Open University of Japan, Prof. Takanori Miyoshi of Nagaoka University of Technology, and Prof. Takashi Okuda of Aichi Prefectural

The authors declare no conflicts of interest associated with this chapter.

*DOI: http://dx.doi.org/10.5772/intechopen.97011*

efficiency of QoS control.

**6.4 Others**

[4], [33–35].

control.

Section 6.

**Acknowledgements**

**Conflict of interest**

University for their valuable discussions.

**7. Conclusions**

**Figure 4.** *Control loops in remote robot operations.*

which realizes edge AI computing, to the remote robot terminal to improve the efficiency of QoS control.
