Unmanned aerial vehicle out-of-control or power-loss fault state falling-to-ground injury risk assessment method

Abstract

The invention discloses an unmanned aerial vehicle out-of-control or power-loss fault state falling-to-ground injury risk assessment method. The method comprises the following steps: establishing a falling flight path prediction model in a failure state that an unmanned aerial vehicle is out of control or loses power; setting boundary conditions; calculating the position coordinates of the fallingpoint of the unmanned aerial vehicle; calculating the landing speed and the landing kinetic energy of the unmanned aerial vehicle; and calculating the casualty rate of the ground personnel, and evaluating the risk of the unmanned aerial vehicle at the landing point of the unmanned aerial vehicle obtained in the step 3) by taking the casualty rate as a quantitative index. Compared with the prior art, the method has the following beneficial effects: the accurate kinematics behavior of failure or power loss of the unmanned aerial vehicle is considered in the process of establishing a failure ground falling flight path prediction model, and meanwhile, the failure initial position error and the failure initial speed error are considered in the process of setting the prediction boundary condition, the falling point and the falling kinetic energy of the unmanned aerial vehicle can be more accurately predicted, and more effective support can be provided for quantitative evaluation of the falling injury risk of the unmanned aerial vehicle.

Description

technical field [0001] The invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a risk assessment method for unmanned aerial vehicles falling to the ground and injuring people in a state of loss of control or power failure. Background technique [0002] Prediction of the area of ​​injury caused by UAV falling to the ground and estimation of the kinetic energy of the fall are the key links to assess the risk of UAV falling to the ground and injuring people. Most of the current research realizes the prediction of the crash area by modeling the UAV's failure track. The main modeling methods include empirical estimation and modeling analysis. Among them, the empirical estimation method is based on the weight, size and category of the drone, and refers to the historical crash data of manned aircraft or drones to estimate the track and affected area after the drone fails. Empirical estimation methods can be further divided into two me...

AI Technical Summary

Problems solved by technology

The initial position of the UAV before the failure and crash has a great influence on the accuracy of the predicted output, which is directly related to the risk of ground personnel

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