Unmanned vehicle real-time global path planning method based on dynamic window of safe A* guide point

Abstract

The invention relates to an unmanned vehicle real-time global path planning method based on a dynamic window of a safety A* guide point. The method comprises steps that a node safety extension strategy and a path node quadratic optimization method are firstly provided on the basis of an A* algorithm, which are called as a safety A* algorithm, and an optimal safety virtual target point is rapidly found out through employing the safety A* algorithm; secondly, the virtual target point serves as a local target of a dynamic window method, speed sampling is conducted, path planning and obstacle avoidance are achieved, simulation is conducted in the MATLAB environment, the result shows that the unmanned vehicle can safely and stably avoid the obstacle under the guidance of the global path, and safety and stability of the method are verified.

Description

technical field [0001] The invention relates to the field of global path planning for real-time obstacle avoidance of unmanned vehicles, in particular to a real-time global path planning method for unmanned vehicles based on a dynamic window of safety A* guide points. Background technique [0002] Unmanned vehicle path planning is to plan the optimal driving trajectory for unmanned vehicles under the premise of comprehensively considering factors such as driving time, speed, fuel consumption and safety. In order to complete driving on complex roads, unmanned vehicles rely on their autonomous mobility. Path planning and navigation are the core algorithms to ensure the autonomous mobility of unmanned vehicles, and they are the key to reflecting the intelligence level of vehicles. [0003] From the perspective of the target range of path planning, it is usually divided into global path planning and local path planning; from the aspect of whether the planning environment changes...

AI Technical Summary

Problems solved by technology

Global path planning usually assumes that the environmental information is completely known. Before the unmanned vehicle moves, a path from the starting point to the end point is calculated offline; local path planning can realize the obstacle detection online and in real time when the unmanned vehicle is moving. Avoidance; however, due to the lack of guidance of global path information in local path planning, it is often easy to fall into the local minimum situation

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