Human Error Identification of Simulated Manual Rendezvous and Docking Based on Multi-Indicators Ensemble Learning
In order to identify the human errors of rendezvous and docking in manned space missions,this paper build an experimental environment using the Kerbal Space Program as a simulation model,and a method for human error behaviors analysis and recognition is constructed based on machine learning algorithms.By simulating the col-lection of six physiological indicators of the operator during the rendezvous and docking task,machine learning mod-eling analysis is performed for each indicator to filter out the indicators that are more applicable for identification and the most suitable learners,and the learners are integrated through stacking ensemble algorithm.The simulation results of the model suggest that the prediction accuracy of the test set reaches 96.36%,which verifies the effectiveness of the method and provides an effective supplement and reference for the analysis of human error behavior identification in rendezvous and docking.
Ensemble learningRendezvous and dockingMachine learningHuman errorPattern recognition
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