Time parameterized route planning method and system for unmanned aerial vehicle in unknown environment

Abstract

The invention provides a time parameterized route planning method for an unmanned aerial vehicle in an unknown environment. The method comprises the following steps: S1, performing feasible path search in an unknown environment by using a sampling-based RRT algorithm, performing redundant point elimination after judging the contribution rate of sampling points, and performing fitting smoothing ona sampling-based RRT path by using a piecewise Bezier curve function to obtain a spatial airway; and S2, performing time parameterized optimization on the space airways obtained in the step S1 to enable the space airways to have unmanned aerial vehicle airway properties, achieving balance between airway execution time and airway state output by introducing weight factors, and finally outputting time parameterized planned airways. The invention further provides a time parameterized route planning system for the unmanned aerial vehicle in the unknown environment. The method has the beneficial effect that the unmanned aerial vehicle route planned by the method shows the particularity of the unmanned aerial vehicle route.

Description

technical field [0001] The present invention relates to unmanned aerial vehicles, in particular to a time-parameterized route planning method and system for unmanned aerial vehicles in an unknown environment. Background technique [0002] In recent years, drones have received increasing attention in various application fields. Especially the quadrotor UAV, because of its high maneuverability and flexibility, it can fly quickly and safely in different complex environments and complete tasks efficiently. One of the most critical issues in UAV flight is route planning, which is a prerequisite for guiding UAVs to fly from the starting point to the target position in different environments, so many institutions and scholars are studying UAV route planning algorithm. Traditional route planning algorithms, such as A* algorithm and Dijkstra algorithm, must obtain global environmental information in advance to establish an environment model, and then conduct route search. In an en...

AI Technical Summary

Problems solved by technology

Although various institutions and scholars have made a lot of progress, there are still aspects that can be optimized in the application of the above route planning methods

[0004] 1) Because the RRT expansion is too random, a lot of time is wasted searching in meaningless areas, making the number of sampling points inconsistent with the number of control points required for subsequent fitting and smoothing. Mechanics, making the route after fitting and smoothing unsatisfactory

[0005] 2) General route planning only generates UAV routes in geometric space, and the route expression is not time parameterized, that is, it is a UAV route that has nothing to do with execution time, which cannot satisfy flight tasks with time constraints or require time coordination multi-drone flight coordination

[0006] 3) The generated route only contains the position information during the flight, which cannot explain the speed, acceleration and other status of the UAV at each moment, and cannot reflect the particularity of the UAV route

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