Purpose

The purpose of this paper is to put forward a path planning method in complex environments containing dynamic obstacles, which improves the performance of the traditional A* algorithm, this method can plan the optimal path in a short running time.

Design/methodology/approach

To plan an optimal path in a complex environment with dynamic and static obstacles, a novel improved A* algorithm is proposed. First, obstacles are identified by GoogLeNet and classified into static obstacles and dynamic obstacles. Second, the ray tracing algorithm is used for static obstacle avoidance, and a dynamic obstacle avoidance waiting rule based on dilate principle is proposed. Third, the proposed improved A* algorithm includes adaptive step size adjustment, evaluation function improvement and path planning with quadratic B-spline smoothing. Finally, the proposed improved A* algorithm is simulated and validated in real-world environments, and it was compared with traditional A* and improved A* algorithms.

Findings

The experimental results show that the proposed improved A* algorithm is optimal and takes less execution time compared with traditional A* and improved A* algorithms in a complex dynamic environment.

Originality/value

This paper presents a waiting rule for dynamic obstacle avoidance based on dilate principle. In addition, the proposed improved A* algorithm includes adaptive step adjustment, evaluation function improvement and path smoothing operation with quadratic B-spline. The experimental results show that the proposed improved A* algorithm can get a shorter path length and less running time.

中文翻译：

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动态环境下基于改进A*算法的机器人导航

目的

本文的目的是提出一种在包含动态障碍物的复杂环境中的路径规划方法，提高了传统A*算法的性能，该方法可以在较短的运行时间内规划出最优路径。

设计/方法/方法

为了在具有动态和静态障碍物的复杂环境中规划最优路径，提出了一种新颖的改进A*算法。首先，通过GoogLeNet识别障碍物，分为静态障碍物和动态障碍物。其次，采用光线追踪算法进行静态避障，提出一种基于扩张原理的动态避障等待规则。第三，所提出的改进A*算法包括自适应步长调整、评估函数改进和具有二次B样条平滑的路径规划。最后，将所提出的改进A*算法在实际环境中进行了仿真验证，并与传统A*和改进A*算法进行了比较。

发现

实验结果表明，在复杂动态环境下，与传统A*和改进A*算法相比，所提出的改进A*算法是最优的，执行时间更短。

原创性/价值

本文提出了一种基于扩张原理的动态避障等待规则。此外，所提出的改进A*算法包括自适应步长调整、评估函数改进和二次B样条路径平滑操作。实验结果表明，所提出的改进A*算法可以获得更短的路径长度和更少的运行时间。