## 1 Introduction
It consisted of a lightweight manipulator arm, an under-actuated hand, and a mobile platform driven by four Omni-directional wheels. MOCA was considered for human-robot collaboration (HRC) applications, whereby robots work alongside humans in close-in industrial environments. Another important research aspect to be considered while developing AWMRs is the robot's autonomous features. An autonomous mobile robot requires the integration of perception, localization, cognition, and motion control systems [3]. Hence, the environment is perceived with sufficient details to allow the accomplishment of any task with acceptable accuracy. Perception systems require the acquisition of knowledge of the work environment and the robot itself through information extraction and interpretation of robot sensors. Based on the information from the sensors and the robot's objectives, the cognition system will gather and integrate this information into a map. In addition, the cognition system will include a localization technique that will estimate the position of the robot and generate the path planning algorithm [12]. Additionally, localization is attained through the precise position and configuration of the robot. Finally, a motion control system is added to compute a collision-free trajectory from start to end. Navigation in indoor environments is classified into two types: global navigation and local navigation [13]. In global navigation, the robot acquires prior information about the destination, obstacles, and environment. While in local navigation, the robot deals with a partially or fully unknown environment. The problem of navigation in an unknown environment requires constant updates of robot position and direction in addition to constant updates of obstacles and the environment. Such a case is called the Simultaneous Localization and Mapping (SLAM) problem [14]. It is a process of the generation of a map of an unknown environment and the continuous knowledge of its position and direction relative to the generated map. Research has been ongoing to try to solve the SLAM problem in mobile robots. One of the most popular SLAM solutions is based on theoretical estimation or Kalman filter-based approaches [15]. Also, there are two types of sensors used in SLAM navigation systems. The first type is the onboard sensor which includes encoders and measuring inertial units (IMUs). The mobile robot's position and orientation can be detected by integrating the sensor measurement. This method is known as dead-reckoning, which is not suitable for long navigation due to integration error steps. The second type depends on external absolute sensors that include lasers, cameras, and Global Positioning System (GPS) [16]. [PERSON] [17] presented a successful example of WMRs, which are used in indoor autonomous navigation. He presented an omnidirectional mobile robot that is oriented to intelligent manufacturing systems. Each of its wheels is a modular wheel installed on a vertical suspension mechanism to ensure moving stability and keep the distances between the four mecanum wheels invariable. The robot control system consists of two-level controllers that implement motion control and multi-sensor data processing, respectively. In [18], an unmanned ground vehicle (UGV) is presented. It consists of a four-wheeled skid-steer and is equipped with a custom six-degrees of freedom (DOF) manipulator arm. For state estimation, localization, and navigation in unknown environments, the UGV was equipped with wheel encoders, an inertial measurement unit (IMU), light detection and ranging sensor (LIDAR). In [19], the 2D laser scanning data is used in real-time to calculate the area of the whole free space in a room. Then it selected the room center as its position for the omnidirectional map building. Grid-based representation and least square algorithm are adopted to accomplish instant map building, which can reduce the redundant laser points effectively.