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Cleaning Robot
Method for path planning of cleaner robot for cleaning robot coverage area.
Cleaning robot algorithms approach.
Numerical comparisons to confirm the results obtained for robot cleaning efficiency and applicability.
in a nutshell
The electric cleaning robot needs to have an application such as artificial intelligence that will solve the cleaning problems of all the surrounding areas, taking into account some factors such as the number of turns and the lengths of the orbits. The mechanism or task of a robot is known as path planning of coverage zone areas (PPCR). In this information, we want to talk about an evolutionary approach to solving the PPCR problem in one area. Secondly, the method to obtain the solutions is based on Genetic Algorithms (GA), which consists of several steps. Each application step represents the robot’s position, and in some sub-applications it represents mini-paths. In addition, with this algorithm, it helps the robot avoiding obstacles by using different sensors to pass over every region of the environment.
1. Introdoction
Artificial Intelligence has fully assisted the use of many robotic applications such as mobile cleaning, care for elderly people, underwater research, aircraft and agricultural robots.
For example, one of them, the vacuum cleaner robot, sweeps every accessible area in the entire room environment. This mechanism is known as path planning of the coverage zone (PPCR).
Various containment path planning approaches have been proposed.
Therefore, mapping is an important task for PPCR. By using sensory data obtained from different sensors via camera, infrared sensor and ultrasonic sensors, we can obtain the constructions of these roadmaps. They are then combined with an algorithm to create a PPCR.
Many kinds of algorithms and approaches have been used to perform a PPCR task in dynamic environments.
These approaches did not take into account the optimization of the generated path length, the number of turns, and the revisited areas.
Genetic Algorithms (GA) have given guaranteed results in many areas such as finding the shortest path. Focusing on the PPCR of the vacuum robot in the room environment using Genetic Algorithms gives better results.
The map is built on a disk shape that represents the robot position, connected only by local lateral links. The proposed model works in a real-time and dynamic environment, as it does not require prior knowledge of the room environment.
2. The task of the vacuum cleaner and its environmental modeling
2.1. The task of the vacuum cleaner
Spiral or zigzag movements can be used two different ways to become an empty room. The electric robot has a job like sweeping little by little all around, avoiding its absence. The robot is in an empty space position, the space to be cleaned will still be empty. In this task, your robot will be preferred over its vain. In this method, it will effectively be marked as a non-transmitted (PPCR) path. In this efficient use, the target should be short and revisited in the form of a minimum number of classrooms.
2.2. environmental education
Before the vacuum robot goes to its destination, it must be done with a state that is a model of the environment.
Ambient cultivation, camera, grown and sensory obtained from ultrasonic teachers yet connected to a planned, application area.
Instead of cleans or squares, the disk, which is the shape of a robot, is decomposed to use the space for the movement of a robot. Here, a screen that will be represented by the disk will be equal to the diameter of the merging robot.
Each disk area represents a robot’s way of life or whether it is an obstacle to occupation in the environment.
