# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 7 Qualitative and Quantitative Performance Assessment of a Robot  
Normally, two types of performance assessments can be carried out on a robot: qualitative and quantitative. Quantitative performance is easier to assess since it involves measuring performance parameters (like speed, time, agility, and others), which require less to no human judgment. On the other hand, qualitative performance assessment cannot be carried out directly since it usually involves intangibles that require more human judgment. One common method of qualitative assessment is to compare a robot's performance with that of a fixed benchmark set. In order to conduct a qualitative and quantitative performance assessment of a robot, the robot deployment categories and performance requirement categories need to be identified. The assessment criteria may vary depending on the type of robot. One such assessment was carried out to measure the performance of the urban search and rescue (USAR) robot [69]. In this project, the stakeholders identified robot deployment categories and performance requirements. They then translated these requirements into tests and metrics with which the performance of robots would be measured. The robot deployment categories included ground and aerial. The ground robots were further classified into subcategories which included Peek robots, collapsed-structure, and non-collapsed-structure wide-area survey robots, wall-climbing deliver robots, confined space robots, temporary shore robots, confined space shape shifters, and confined space retrieval robots. Similarly, the sub-category aerial robots included high-altitude loiter robots, rooftop payload drop robots, ledge access robots, variable depth sub robots, and bottom crawler robots. At the beginning of the project, the expectations of robot performance were defined for each robot. This process of definition captured individual categories of requirements, along with how to measure robot performance against each requirement and objective value. These requirements continue to grow with the evolution and maturity of robot capabilities. For the case of our study, the concept of human-robot interaction (HRI) needs to be incorporated. A security personnel must have some sort of interaction with the security robot for a good physical security posture. Using this concept, several studies have been conducted to determine the efficiency of human-robot interaction. One such experiment was performed by [PERSON] et al. [70]. The researchers presented a practical human-robot interaction system based on a wearable glove (Figure 13) for object grasp applications. Three different types of sensors were embedded to capture grasp information from human demonstrators, enabling the imitation of the posture and dynamics of the human hand. Calibration algorithms were implemented to mitigate errors caused by the sensors. After analyzing the collected data, the positions of the accelerometer, pressure, and flex sensors were determined in the wearable glove to capture grasp information for objects with different sizes and shapes. A three-layer neural network was trained to recognize grasp orientation and position based on the multi-sensor fusion method. The experimental results showed that an industrial robot could successfully grasp spheres, rectangles, and cylinder objects (see Figure 14) with the proposed HRI based on learning from demonstration. The performance of various techniques developed and used, mainly computational algorithms, is subjected to quantitative assessment, but these techniques do not incorporate measures of robot behavior directly. They do not even provide us with a qualitative measure of how well the robot carries out its navigation task, or how well it avoids obstacles, for example [71]. [PERSON] aims to provide some standards for quantitative performance measures of robot behavior and proposes the use of correlation dimension estimation techniques to make quantitative estimates of overall robot behavior.