Vehicle path planning method based on hierarchical monitoring domain and adaptive artificial potential field method

Abstract

The invention provides a vehicle path planning method based on a hierarchical monitoring domain and an adaptive artificial potential field method. The method comprises the following steps: acquiring the speed of an unmanned vehicle by using a hierarchical monitoring domain model; calculating resultant force borne by the unmanned vehicle; calculating the position of the next path point; then enabling the unmanned vehicle to move to the next position; judging whether the unmanned vehicle arrives at a target point or not; if the unmanned vehicle does not arrive at the target point, judging whether the unmanned vehicle falls into a local minimum value point or not; and adopting a tangent point-Bezier curve model to guide the unmanned vehicle to get rid of a local minimum value point or oscillation point area, or adopting a virtual random guide strategy to guide the vehicle to get rid of the local minimum value point or oscillation point area. According to the method, the speed of the unmanned vehicle is controlled by using the hierarchical monitoring domain model, the unmanned vehicle is guided to get rid of the local minimum value point or oscillation point area by using the tangent point-Bezier curve model and the virtual random guide strategy, and the method is suitable for a complex space environment.

Description

technical field [0001] The invention belongs to the field of unmanned vehicle path planning, in particular to a vehicle path planning method based on layered monitoring domains and an adaptive artificial potential field method. Background technique [0002] In recent years, unmanned vehicles have been widely used in civilian and military fields, and have undertaken important tasks such as intelligent transportation, emergency rescue, and military operations. Path planning is the core technology for the intelligence and autonomy of unmanned vehicles. It is mainly under the constraints of dynamic conditions and complex environments, unmanned vehicles find a collision-free optimal or suboptimal path from the current position to the target position in real time. In essence, path planning technology is a method of optimizing complex systems. However, the current path planning technology still has problems such as insufficient autonomous obstacle avoidance ability and poor adapt...

AI Technical Summary

Problems solved by technology

Due to the current research on the artificial potential field method ignoring the consideration of the environment, unmanned vehicles cannot generate feasible paths in dynamic or unknown environments.

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