# Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System
## 1 Introduction
[19; 20] proposed a hydrostatic and electrostatic hazard field containing information about the robot's attitude and velocity to assess the hazard level of the robot's surroundings. [PERSON] et al. [21] generated a collision prediction map by using the collision time between a robot moving at a certain speed and the surrounding obstacles. A dynamic modified SSM (speed and separation monitoring) method was presented in [22] for ensuring the safety of human-robot collaboration and added some inherent characteristics of robots that had been quantified to the risk index; the safety of the assisting robot was improved by adjusting the speed. [PERSON] et al. [23] not only took direct factors, such as speed and position, into consideration, but they also considered indirect indicators, such as application time and operational proficiency; their work was divided into three levels based on the calculation: safe, dangerous, and very dangerous. The kinematic inverse solution with high safety was selected through the redundancy of the robot arm. This method was also demonstrated in [24], despite causing significant joint movements of the robot arm. Although these studies considered multiple factors, they mainly used a linear formula calculation approach for risk assessment with a certain percentage of each risk factor. But this is not reasonable, because the role of different factors is not necessarily fixed in different situations. Under such circumstances, the fuzzy theory has shown excellent outcomes as an intelligent method for speed control, temperature regulation, etc. [25]. It incorporates the wisdom of experts into the decision-making process, thus making it applicable to different situations and allowing decision makers to learn and maintain it in the decisions. Fuzzy theory was used to fuzzify the input risk factors in [10], but fuzzy inference methods were not used for crash risk assessment. In [26], fuzzy theory was applied to the collision risk assessment of ships, but, currently, no application has been reported in the collision risk assessment of teleoperation processes. To address the above issues, this paper proposes a new collision risk assessment model to address the current incomplete and inappropriate issues of collision risk assessment. Based on this model, a series of teleoperation-assisted control strategies have been presented in this paper to improve the safety and efficiency of teleoperation. In order to make a more comprehensive assessment of the collision situation, this paper developed a fuzzy-theory-based integrated collision risk assessment model, which takes three factors into consideration: closest distance, operation speed, and delay time. Moreover, compared to traditional risk evaluation methods, the process of fuzzy reference for collision risk incorporates expert experience, thus helping decision makers in extracting and keeping expert knowledge. In the calculation process of risk factors, this paper puts forward a novel discrete expandable bounding box method in calculating the shortest distance between the robot arm linkage and the surrounding assembly parts; the mechanical linkage is enveloped by a series of bounding boxes of different radii, and the smallest radius of all bounding boxes colliding with the obstacle is considered as the relatively shortest distance, thus reducing the computational difficulty. In order to achieve more effective and safer work for teleoperated robots, this study proposed a teleoperation-assisted control strategy based on the assessed collision risk: The virtual feedback force was linearly mapped to the collision risk through the development of haptic gloves to achieve intuitive physical feedback on the safety of the current operation. The introduction of risk-assessment-based variable scale motion mapping and highlighted dangerous area display method make the robot operate more reasonably and effectively in obstacle avoidance during operation, thus reducing the possibility of collision.