Adaptive maneuvering and safe flight method and system for quadrotor drone in unknown environment

By constructing ESDF maps in unknown environments and implementing adaptive exploration step size strategies, combining low-order and high-order motion models to optimize trajectories, the problems of safety, reliability and efficiency in drone track planning are solved, and the adaptive maneuvering and safe flight of quadrotor drones are realized.

CN118963379BActive Publication Date: 2025-03-28YUNNAN MINZU UNIV
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Patent Information

Application Number
CN202410982433.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2024-07-22
Publication Date
2025-03-28
Estimated Expiration
2044-07-22

AI Technical Summary

Technical Problem

The prior art is difficult to ensure the safety, reliability and efficiency of flight at the same time in uncharted planning of drones in unknown environments, and research on adaptive speed often tends to be one indicator.

Method used

A method for adaptive maneuvering and safe flight of four-rotor UAV in unknown environments is proposed. By obtaining the position information of the UAV, building an ESDF map, selecting target points to perform adaptive exploration step size strategy to search the initial path point, combining low-order and high-order motion models to optimize the trajectory, considering the joint punishment of obstacles and speed.

Benefits of technology

It realizes safe flight and efficient path planning of drones in unknown environments, and improves the maneuverability and flight efficiency of drones through adaptive exploration of step size strategies and multi-layer model prediction.

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Abstract

The present invention provides a method and system for adaptive maneuvering and safe flight of a quad-rotor drone in an unknown environment, and relates to the field of drone control technology. This method takes into account the safety and flexibility of drone flight, and introduces the density information of obstacles based on the A* algorithm in the path point search process, so that the initial trajectory is in a sparse obstacle range, paving the way for allocating a higher speed to the drone. At the same time, a quasi-arc selection strategy is introduced to accurately search for each adjacent path point of the drone when the drone generates a certain turning angle to reduce turning oscillations. In the optimization process, the relationship between obstacles and motion speed is taken into account, and a joint penalty function of obstacles and speed is designed, which is applied to the multi-layer model prediction contour control, and flexible adjustment of speed is achieved through constraint relationships.
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