Abstract
Notable has been the rapid development of artificial intelligence in recent years, with mobile robots being widely applied in various fields. Greatly facilitating people's daily lives, research on robots has received widespread attention. The obstacle avoidance capability of mobile robots is an important indicator of their intelligence. This article utilizes the A-star algorithm. It optimizes the robot's movement path. The A-star algorithm is characterized by balancing the actual cost from the starting point to the current node with the estimated cost from the endpoint to the current node. This algorithm incorporates elements of both Dijkstra's algorithm and greedy search. By being guided by a heuristic function, A-star efficiently approaches the target point, ensuring optimal path planning. This paper focuses on optimizing the A-star algorithm, enhancing both node expansion during path exploration and search time for paths as the robot navigates through a maze.
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