**6.4 Others**

*Robotics Software Design and Engineering*

control.

**6.2 Multilateral control**

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

**6.1 Integration of QoS control and stabilization 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

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

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.

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],

the effect by using other types of QoS control and stabilization control.

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

**64**

**Figure 4.**

*Control loops in remote robot operations.*

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

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 [4], [33–35].
