Differential flatness-based anti-disturbance unmanned aerial vehicle collision avoidance method

Abstract

The invention relates to the technical field of robots, in particular to an anti-disturbance unmanned aerial vehicle collision avoidance method based on differential flatness. According to the method, the disturbance suffered by the unmanned aerial vehicle is accurately and rapidly learned and estimated by using an incremental Gaussian process, and corresponding compensation is performed according to a prediction result of the disturbance in a trajectory planning and unmanned aerial vehicle control process, so that the unmanned aerial vehicle based on differential flatness control can still realize real-time collision avoidance in the disturbance.

Description

technical field [0001] The present invention relates to the technical field of robots, and more specifically, to a method for collision avoidance of an anti-disturbance UAV based on differential flatness. Background technique [0002] The differential flatness of the UAV can enable the UAV to obtain the control amount of the UAV system through a specific output value and each derivative, and through correlation operations. This property has been used in the prior art to implement collision avoidance algorithms for drones. These algorithms take advantage of this technique to greatly reduce the amount of computation needed to control the drone for collision avoidance. When the computing power of the UAV is limited, these algorithms can still plan a new collision-free trajectory and quickly calculate the corresponding control amount to achieve real-time collision avoidance. However, these algorithms are difficult to cope with the disturbance of the drone caused by the environ...

AI Technical Summary

Problems solved by technology

In reality, the above two factors cannot be ignored, so the disturbance generated by the two to the UAV will greatly reduce the performance of the algorithm

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