# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 5 Teleoperation Assistant Control Strategy Based on Collision Risk Assessment
### Assistant Strategy for Teleoperation  
Based on the user interface, the teleoperation assistant strategies include the following three aspects.  
Strategy 1: Collision risk indication based on haptic feedback glove  
Considerable studies have been conducted on virtual guidance force based on haptic feedback devices such as generating virtual guidance force based on artificial force field theory. This method synthesizes the integrated guiding force based on the repulsive force of the obstacle and the attractive force of the target so that the operator can intuitively  
Figure 13: Graphical interactive interface and its sub-module.  
feel the distance from the surrounding obstacles. This paper presented a haptic feedback strategy based on collision risk assessment. The risk assessment model not only includes the distance to the surrounding obstacles, but it also includes the operation speed and delay time. After completing the collision risk assessment of the current state, the haptic glove intuitively indicates the collision risk of the current operation to guide the operator to make reasonable decision. The feedback force exerted by the glove is proportional to the risk of collision; the higher the risk, the stronger the force. The glove used in this study is a force feedback glove developed by the laboratory [37], which can achieve a resolution of 0.02 N and a force feedback of up to 1.55 N.  
Strategy 2: Variable motion mapping ratio based on collision risk assessment  
Reducing the operation speed or lowering the motion mapping ratio of the master-slave is an effective means of improving operational safety. However, the continuous adjustment of the robot operating speed may also affect the task execution efficiency. Herein, the motion mapping ratio was adjusted to three levels according to whether the collision risk reached the set threshold. The motion mapping ratio of the master-slave end decreases as the risk index increases, thus improving the operating accuracy and reducing the collision risk. Risk thresholds and motion mapping ratios are obtained based on expert knowledge and experience.  
The collision risk assessment index to motion mapping ratio can be expressed by the equation:  
\[K=\left\{\begin{array}{ll}k_{1}&(s_{d}<s_{safe})\\ k_{2}&(s_{safe}\leq s_{d}\leq s_{danger})\\ k_{3}&(s_{d}>s_{danger})\end{array}\right. \tag{8}\]  
where \(s_{d}\) denotes the collision risk value obtained by fuzzy theory.  
The motion mapping ratio \(K\) of the master-slave is the ratio of the relative posture change of the master robot to the relative posture change of the slave robot. The relationship between the control position of the robot and the relative posture change of the operator is expressed by the following equation:  
\[P_{robot\_i}=P_{robot\_i-1}+KP_{tracker}=P_{robot\_0}+K(P_{tracker\_i}-P_{ tracker\_i-1}) \tag{9}\]  
where \(P_{robot\_i}\) is the current control position, \(P_{robot\_i-1}\) is the robot position at control period \(i-1\), and \(P_{tracker\_i}\) and \(P_{tracker\_i-1}\) are the current and the last positioning position of tracker which is controlled by operator, respectively.  
Strategy 3: Local highlighting display  
This strategy is a further optimization based on the designed Strategy 2. In Figure 13, the top right corner of the user interface is the local highlighting sub-module whose function is to highlight the areas of high collision risk in order to assist the operator in observing the virtual environment more carefully. Objects with the highest collision risk are shown in red. The highlighted magnification of the view is aligned with the motion mapping ratio in Strategy 2 to improve coordination of the operation. The current magnification is shown as the value in the upper right corner.