# Mobile Robot for Security Applications in Remotely Operated Advanced Reactors
## 6 Robots' Way of Formalizing Problems  
Formalization is an act of giving something a fixed structure or form by introducing rules. There are several instances when a robot needs to formalize problems, like when it gets stuck while navigating, to avoid obstacles, and many more based on the performance that the robot is designed for. With a robot that has the task of tidying up a household, although planning for the tasks seems easy, there is a problem for the robot since it is usually uncertain about the state of the household when planning [67]. For example, when the robot is ordered to clean the kitchen, it rarely knows what objects it needs to put away to execute the tidying process and whether or not there are any objects at all that need to be displaced. [PERSON] et al. [67] try to identify conditions under which classical planning can be used in a re-planning loop to solve the problems in a nondeterministic, partially observable open domain. For this purpose, they propose and use a continual planning approach, in which a plan is created to solve the problem at hand, and re-planned if anything does not work as per the previous plans. To experimentally carry this out, they created a test scenario that contained two rooms separated by a door, tables, and a shelf at the back. To tidy up the room, the test subject was required to find objects, pick them up, and bring them to the tidy location by navigating through the rooms. In the end, the robot was required to clean the table where the objects were initially shifted from. From a learning perspective, a humanoid robot should be capable of improving its knowledge of recognizing objects with active perception [68]. [PERSON] et al. have addressed the problem of learning to recognize objects via manipulation in a developmental robotics scenario. In their research, they have integrated a bottom-up vision system, a control system of a complex robot system, and a top-down interactive exploration method to collect data and determine if interacting with humans is beneficial. They set the experiment up to find this out. The main idea was for the robot to learn to associate a camera view with an object via episodes. In each episode, the robot had to decide which object it wanted to learn more about, which manipulation it should use as a strategy to generate new sample data, and then learn to differentiate between objects. Based on the experimental results, the humanoid robot iCub could learn to recognize 3D objects via manipulation and in interaction with teachers, choosing the adequate exploration strategy to enhance competence progress and focusing more of its efforts on the most complex tasks [68]. This improved the autonomous decision capacity of the iCub humanoid robot. Detecting humans through machine vision in complex environments represents a significant research area. To derive meaningful insights from captured audio-visual data, algorithms need to be implemented, either onboard or remotely, in near real-time. Wadoro (a watch dog robot) [36] is an autonomous mobile surveillance robot designed to navigate the semi-outdoor environment around a house, actively seeking potential human intruders. Equipped with a night-vision camera, it can conduct surveillance around the clock. The primary goal of the robot is to promptly generate alerts in the event of any intrusion on the premises. Throughout its surveillance operation, Wadoro undergoes distinct phases--including human detection, tracking, recognition, and the subsequent generation of alerts. Additionally, the robot incorporates protective measures to safeguard itself against collisions and potential attempts of theft or removal from the ground. When idle, the robot remains still, monitoring people or faces via visual observation and PIR sensors. If a person is detected by the camera, it switches to tracking mode; if human motion is sensed by the PIR sensors, it turns and enters patrol mode after a maximum idle duration of 60 s.  
In patrol mode, the robot moves at 0.5 m/s, capturing frames for human or face detection.