## 1 Introduction  
Mobile robots are categorized in both classical and heuristic methods in Coverage Path Planning (CPP). These methods can be divided subcategories such as analytical methods, evolutionary methods, enumerative methods, and meta-heuristic methods [1]. In real-world applications, these methods are highly dependent on the ability to optimize coverage path planning with minimized obstacles in a dynamic environment [1]. In mobile robots, Coverage Path Planning (CPP) algorithms have been implemented in many'real-world' applications in dynamic environments; examples include domestic robots, such as cleaning and monitoring robots, automatic lawn mowers, inspection robots, painting robots, and industrial robots [1; 2; 3]. There are generally two types of motion planning approaches for a cleaning robot. These are (according to the behavior of obstacles) off-line (or static) and on-line (or dynamic) motion planning [1; 4]. In the case of stationary obstacles, robot routing uses a predefined decision. Such a use-case motion planning is called offline decision making or static motion planning. When the obstacles are not static (that is, they are moving or dynamic obstacles), motion planning is termed as on-line or dynamic motion planning [1; 2; 5; 6; 7; 8; 9]. In an investigation by [PERSON] et al. [6], the authors address the identification of obstacles and path coverage. The sampling-based method employs metrics, collision checking, heuristics, post processing and local planning, addressing the situations covered with an optimal path to attempt to prevent robot collisions. In a dynamic environment, research by [PERSON] et al. [7] has attempted to improve the A* (A-star) algorithms for mobile robot navigation based on a grid map. The research uses heuristics to optimize time, coverage and distance traveled. However, the approach does not address obstacles in a dynamic environment. Recently, algorithms have been developed using an on-line coverage-based approach, the 'bustrophedon division' algorithm, typically the BA* algorithm [9; 10; 11; 12]. The BA* algorithm performs a search using a backtracking mechanism to ensure that all regions are found, and the workspace of the robot is covered completely. In a known environment, the cleaning robot applies to model a graph-based representation, based on the known environmental information; The BA* algorithm then uses the method of graph traversal for coverage path planning. In an unknown environment, the learning robot employs a sensing system to explore and identify the environment [9; 10; 11; 12]. The approach uses modeling, and generates a graph based on the identified environmental information in the coverage path planning. A major approach is the decomposition of the working environment into smaller, simpler, and non-overlapping areas; the robot covers each small area by a primitive motion, such as the 'bustrophedon' (or zig-zag) move. The Spiral-STC algorithm proposed by [PERSON] [13] divides the map into'megacells', then a spanning tree is generated to connect all'megacells' to provide coverage by following the spanning tree. In multiple tasks, [PERSON], et al. have investigated how a robot locally finds the best paths and coverage actions to keep the desired coverage level over the environmental dynamic [5]. The investigation of a robot proposed an on-line coverage algorithm in grid environments, in order to find an optimal way in simulations [13]. In machine learning approaches, some studies have been concerned with K-mean clustering [12], neural networks [14] and genetic algorithms [15] for finding the coverage path of a mobile robot. In monitoring industry robot applications [15; 16; 17; 18; 19], these robots applied the approaches of the cluttered environment with limited onboard energy for robot monitoring [15] and cleaning robot in the control for coverage path [16]. These investigations have considered the CPP problem.