Objective In order to improve the adaptability of mobile robots to complex map environments, we propose a global path planning method called laser model, which improves the A* algorithm and ray model.
Methods This method draws inspiration from the real-time scanning principle of planar light detection and ranging, which emits multiple virtual laser rays from the path node to the target point to perceive the boundary of obstacles, thereby quickly crossing obstacles and reaching the target point. At the same time, combined with the Floyd optimization algorithm, a safe, reliable, and stable global path is obtained in a relatively short time.
Results In the concave environment of the experiment, the search time of the laser model is 99% faster than that of the A* algorithm and ray model, and the search process is reduced by 96%. In the experimental infeasible region, the laser model is 99% faster than the A* algorithm and ray model, and the search process is reduced by 97%. Compared with the double ant colony crossover algorithm, the laser model also performs better in concave environments.
Conclusions The experimental results show that the proposed method is effective in solving the time consumption problems of concave traps and infeasible areas.
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