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Trajectory Planning of a Mobile Robot using Enhanced A-Star Algorithm
Objectives: This paper presents an optimization technique for a robot to identify the best path for traversing from a start state to a goal state without colliding with any obstacles. Methods: The existing A-star algorithm is modified to make the robot to move in an unknown scenario in which static obstacles are located. The robot is expected to travel to the goal position without colliding with any of the obstacles present. The Enhanced A* algorithm guides the robot to reach the target following an optimal path. By adding a new parameter namely number of turnings (p(n)) the robot makes during its traverse, currently existing A* algorithm is improved. This is done in order to improve the algorithm for optimal motion of the robot. Findings: The proposed Enhanced A* algorithm will be shown to remove certain drawbacks, which are found in the currently existing algorithms, for optimal travel of the robot. Simulation experiments are carried out to compare the performances of the proposed Enhanced A* algorithm and the original A* algorithm. The outcome of the simulation experiments is expected to provide better performance as compared to the A* algorithm concerning Elapsed time of travel. It is expected that the Enhanced A* algorithm is able to provide an effective contrivance for exploring path planning in robotics education and path control of robot. Improvements: The Future Enhancement is to make the algorithm work in an unknown environment with dynamic obstacles present and implement the same in real time using an autonomous mobile robot.
A* Algorithm, Mobile Robot, Machine Learning, Path Planning, Static Obstacles, Unknown Environment.
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