Construction and Analysis of Real-life Datasets during Operations with Remotely Operated Vehicles: Aerial and Ground

Abstract Ongoing advancements in Remotely Operated Vehicles (ROVs) have enabled their widespread use in various safety-critical applications. However, operators often find themselves controlling the vehicles in harsh and stressful conditions, which can induce stress and fatigue. Such factors may compromise the mission’s safety and outcome, as the operators might issue unintentional commands. Nonetheless, real-time monitoring of both the operator and the ROV can help prevent such potential accidents by introducing a mechanism that detects such anomalies. We present the construction of real-life datasets that include two test cases: aerial and ground vehicles. Data were collected from the operator and the ROV during a mission. The first dataset consists of data from 19 subjects when operating an Unmanned Aerial Vehicle (UAV), while the second dataset includes data from 7 subjects when performing an operation with an Unmanned Ground Vehicle (UGV). The construction of such datasets and the expansion in more than one ROV aim to the generalization of our approach towards abnormal command detection. A thorough analysis was conducted and presented, which included statistical analysis: a t-test and the extraction of average values and box-plots. Further, feature extraction and selection were performed as part of the analysis of the constructed datasets, towards the classification of abnormal commands.