## 1 Introduction  
Robots are fast becoming an integral component of our everyday life and being deployed towards a number of processes over a wide range of applications. Such robots are required to find safe and feasible routes to navigate effectively in the environment. This need is particularly crucial when these robots are navigating in complex and uncertain settings. In order to achieve efficient navigation, a robot must be equipped with necessary control units, sensor systems and the effective coverage path planning intelligence. Path planning is a method that regulates the robot's path that passes over all parts of an area while avoiding obstacles. Path planning becomes an essential ingredient for many robotic applications, such as cleaning, painting, inspection and mining in order to amplify their performances. Over last few decades, different path planning methods have been proposed and demonstrated on robots with various applications. One of the most frequently used path planning methods is cellular decomposition which breaks the obstacle-free places into a non-overlapping region. These regions are called cells which is easy to cover and require simple motions (for instance Zigzag) to sweep the whole area. In literature [1; 2; 3], the authors documented the generation of simple motions using the cellular decomposition method. A simple decomposition method was proposed by [PERSON] and [PERSON] [4]--called the trapezoidal decomposition--to solve the path planning problem in agricultural machines. In this work, they used a top-down approach and split the complex agricultural fields into simple regions (cells) to navigate with elementary motions in order to cover the filed effectively. In Reference [5], [PERSON] and [PERSON] proposed a new approach called boustrophedon cellular decomposition which works similarly to other decomposition methods. However, the proposed method reduced the number of cellular cells compared to trapezoidal algorithm hence, the robot's shorter coverage path is obtained. [PERSON] presented a novel cellular decomposition method in Reference [6], wherein they used Morse function in order to indicate the location of the cell boundaries. In this study, the authors change the Morse function that modifies the path pattern of the robot to cover its free space. As an extension of this study, authors also presented a sensor-based coverage algorithm that uses cellular decomposition in terms of critical points of Morse function in Reference [7]. In this work, they identify the features of the proposed algorithm to eliminate the error sensor data in unstructured environments beyond performing sensor data processing. [PERSON] and [PERSON] presented a coverage path plan method based on the Morse decomposition function for an underwater surface robot [8]. This work aims to determine the best sweep orientation for each separated cell and the inter-lap spacing in the generated path on a lap by lap basis with respect to the ocean depth. Also, they validated the proposed algorithm in simulation experiments with real-time bathymetric data sheet. Similarly, [PERSON] et al. [9] implemented a novel Morse cellular decomposition function combined with GVD (General Voronoi Diagram) algorithm in a space inspection robot. The presented work introduces a method to generate robot's path in the three-dimensional space that reduces its fuel cost to navigate from one point to another. An alternative algorithm for the Morse decomposition method was proposed in Reference [10]. In this work, the author presents a landmark-based topological coverage in which the natural landmarks are added as nodes in the mapped space. In addition, they benchmarked their proposed path planning approach with the Morse based method and demonstrated higher coverage performance with the proposed scheme. Utilization of grid-based methods for coverage path planning opens a lot of opportunity for research and development in the field of robot navigation. There are numerous such algorithms were proposed for efficient path plan for mobile robots.