Multi-unmanned aerial vehicle distributed cooperative formation method in unknown environment

By constructing a grid map using UAV sensors and depth cameras in an unknown environment, and combining affine transformation and LQR controller, the problem of formation maintenance and trajectory planning for multiple UAVs in an unknown environment was solved, achieving efficient and real-time formation trajectory planning and tracking.

CN117369498BActive Publication Date: 2026-06-26HUNAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
HUNAN UNIV
Filing Date
2023-10-26
Publication Date
2026-06-26

AI Technical Summary

Technical Problem

Existing multi-UAV cooperative formation algorithms have failed to effectively address technical challenges such as weak distributed coordination capabilities in unknown environments, difficulty in maintaining formation in dense obstacle environments, and high real-time computing requirements in unknown environments.

Method used

Each drone acquires position and attitude information through GPS and IMU sensors, constructs a grid map using a depth camera, solves the formation reference trajectory using the principle of affine transformation, and uses an LQR controller for real-time trajectory tracking to construct a multi-target trajectory optimization problem to plan a safe trajectory.

Benefits of technology

It effectively maintains formation in dense obstacle environments, reduces small-group similarity errors, improves the real-time performance and efficiency of trajectory planning, supports the transformation of hard constraints into soft constraints, and reduces computational load.

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Abstract

The application discloses a kind of multi-unmanned aerial vehicle distributed cooperative formation methods under unknown environment, each unmanned aerial vehicle obtains the position, attitude information and other unmanned aerial vehicle planned trajectory of current self in real time;Real-time grid map is constructed by depth camera;Each unmanned aerial vehicle is based on affine change principle, according to the planned trajectory of other unmanned aerial vehicle, the formation reference trajectory of self is solved, as the formation error reference term of trajectory planning;Subsequently, the expression of unmanned aerial vehicle trajectory is defined, a multi-objective optimization problem is constructed, the optimization problem is solved, the formation trajectory planning is completed, and the trajectory is input to LQR controller, and trajectory tracking is completed.Effectively solve the problem that multi-unmanned aerial vehicle formation is kept difficult under dense environment, formation similarity error is greatly reduced;Distributed framework has small amount of calculation, high practicability;Trajectory planning solving efficiency is high, this method supports space-time joint solution, supports to transform hard constraint into soft constraint, can guarantee the real-time performance of planning.
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