# A Dynamic Route-Planning System Based on Industry 4.0 Technology
## 2 Literature Review
### Motion Planning  
When applying motion planning to the autonomous movement of a robot, the goal is to determine a sequence of motion that moves the robot from an origin to a destination. In general, a motion planning has obstacle collision avoidance capability, using available data from different equipped sensors to navigate the robot by controlling the speed and direction of movement. an A-star algorithm has been applied to many motion planning problems and is considered one of the most popular algorithms.  
[PERSON] and [PERSON] [4] applied many variations of the A-star algorithm for path planning for a rescue robot operation. They presented many variations of the A-star algorithm according to the complexity of the rescue environment, as well as the precision of the sensors. Because of the noise from the signals detected by the robot, approximated information of the robot's orientation is required. [PERSON] et al. [5] stated that A-star is the best general algorithm for searching for the optimal path. Path planning is used in many types of applications, especially in artificial intelligence-related areas. [PERSON] et al. [6] developed an ANN to predict the location of an AGV by using operational data containing the accuracy from the model and the actual location of the AGV. The proposed ANN model could generate a smooth and accurate point-to-point travel plan in field testing without a controlled environment. [PERSON] et al. [7] proposed an enhanced A-star algorithm for guiding an automated guided vehicle (AGV). The proposed method could generate efficient paths that reduce superfluous inflection points and redundant nodes. [PERSON] et al. [8] proposed an integration of a collision avoidance method and the A-star algorithm in an environment with different agents for an AGV system. In the experiment, the AGVs had to follow different assigned paths without colliding with the obstacles. [PERSON] and [PERSON] [9] studied an application of a mobile robot in a warehouse environment with the monitoring of battery health. An enhanced A-star algorithm was used to generate the paths for robots. [PERSON] et al. [10] developed an intelligent controller for path planning using an ANN. The authors constructed collision-free paths for moving robots among obstacles on the basis of multiple inputs from different resources. [PERSON] et al. [11] proposed an ANN model for path control of an AGV that considers the line-of-sight during movement in adverse conditions. The ANN model was created for learning the proposed guidance law in order to control the effects of wind-induced sideslips. [PERSON] et al. [12] developed a control methodology for an AGV. A hybrid kinematic controller was developed on the basis of a combination of an evolved ANN and a genetic algorithm. The controller was used to control car kinematics to avoid obstacles. From the test results, the vehicle reached a certain level of autonomy, according to a controller that took into account the kinematics and dynamics of the system. A new suggested method that combined A-star algorithm and motion planning, and that could find a global path, track the path, and avoid collisions in a dynamic environment, as well as in a static one, was introduced by [PERSON] et al. [13]. For real-time obstacles, path planning with A-star generated a global path that kept distance from the obstacles; the path was tracked with a window adaptive approach that helped the robot track the entire path smoothly and reach the final goal without collisions.