Hybrid path planning method based on FLOYD and Astar

Abstract

The invention discloses a hybrid path planning method based on FLOYD and Astar. The method comprises the following steps: firstly building a vehicle mathematical model, constructing a high-precision map, combining a Floyd algorithm with an A * algorithm under the condition, and carrying out the path planning of an automatic driving vehicle through a hybrid algorithm. In a common path planning algorithm, the Floyd algorithm is high in time complexity, and a distance matrix and a path matrix obtained through calculation cannot meet the high real-time performance requirement of an automatic driving vehicle during dynamic driving; and the A * algorithm has a logic design problem, so that the planning result is not always globally optimal. Aiming at the problems of the Floyd algorithm and the A * algorithm in the traditional path planning algorithm, the method provided by the invention provides the hybrid path planning algorithm which combines the characteristics of the two methods, gives consideration to global optimality and real-time performance at the same time, enhances the adaptability of the algorithm to the environment, and improves the path planning efficiency. Therefore, online planning and dynamic global obstacle avoidance of an automatic driving reference path are realized.

Description

technical field [0001] The invention relates to the technical field of automatic driving, in particular to a hybrid path planning method based on FLOYD and Astar. Background technique [0002] Path planning is one of the key technologies of autonomous driving, and it is the focus and difficulty. Path planning for autonomous vehicles refers to planning a collision-free, energy-efficient route based on certain environmental models and driving rules and strategies, given the driving start point and target point, and according to specific performance indicators, on the basis of ensuring driving safety and maximum efficiency. The drivable path to reach the target point safely. Path planning mainly includes two steps. One is to establish an environment map including obstacle areas and free areas, and the other is to select an appropriate path search algorithm based on the environment map to search for a drivable path quickly and in real time. Path planning for autonomous vehicle...

AI Technical Summary

Problems solved by technology

Compared with a single algorithm, this method reduces the route search time, enhances the real-time performance of the self-driving vehicle, and avoids falling into the local optimal solution, but there are still the following problems: the surrounding environment information is constructed as a grid map, and the drivability of the route cannot be guaranteed The improved ant colony algorithm is used to carry out global path planning twice, and the A* algorithm is re-optimized after falling into the local optimum, which still takes a long time; the improved ant colony algorithm still includes some The form of random number selection is a blind choice, lacking heuristic information

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