Abstract
Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A ∗ algorithm. First, this method improves the heuristic function of the traditional A ∗ algorithm to improve the efficiency of global path planning. Second, this method uses a path optimization strategy to make the global path smoother. Third, this method is combined with the dynamic window method to improve the real-time performance of the dynamic obstacle avoidance of the intelligent vehicle. Finally, the global dynamic path planning method of the proposed improved A ∗ algorithm is verified through simulation experiments and real vehicle tests. In the simulation analysis, compared with the modified A ∗ algorithm and the traditional A ∗ algorithm, the method in this paper shortens the path distance by 2.5%∼3.0%, increases the efficiency by 10.3%∼13.6% and generates a smoother path. In the actual vehicle test, the vehicle can avoid dynamic obstacles in real time. Therefore, the method proposed in this paper can be applied on the intelligent vehicle platform. The path planning efficiency is high, and the dynamic obstacle avoidance is good in real time.
Highlights
As one of the development directions of future automobiles, intelligent driving is receiving more and more attention [1]
Aiming at the optimal path and planning efficiency of global path planning for intelligent driving, this paper proposes a global dynamic path planning method based on improved A∗ algorithm and dynamic window method. e improved path planning method has many advantages
Compared with the traditional A∗ algorithm and dynamic window algorithm, the path distance of the algorithm proposed in this paper is reduced by 3.0% and the time is reduced by 13.6%
Summary
As one of the development directions of future automobiles, intelligent driving is receiving more and more attention [1]. Path planning is an important part of intelligent driving. Path planning is an obstacle-free path from the starting point to the target point that the intelligent vehicle plans out based on environmental information [2]. In the dynamic environment, in order to ensure the real-time obstacle avoidance and the efficiency of path planning, it is necessary to improve the path planning algorithm. Ziang Zhang et al [9] proposed an improved hybrid path planning method for a spherical mobile robot based on a pendulum, which improves the efficiency of path search, but it is aimed at a spherical mobile robot. Bijun Tang et al [10] proposed an algorithm that uses an artificial potential field method to optimize the path of the hybrid A∗ algorithm
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