In-Space Robotic Assembly Joint Characterization Approach

This paper describes the test systems and approaches developed to characterize the performance of a structural joint that is intended for robotic in-space assembly (ISA). The design of the joint is based on a heritage concept from National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC) originally intended for structural assembly by astronauts during extravehicular activity (EVA). Its design was modified under a public-private partnership and is intended to accelerate the availability of, and reduce costs for the infusion of NASA developed technologies into commercial ISA systems. Test systems were developed to measure the axial, bending, and torsional stiffness of the joint at a wide range of temperatures. A test system was also developed to measure the reliability of the joint in terms of translation and rotation when disassembled and re-assembled in space. These test systems were used to characterize the joint behavior and provide performance data for the iterative joint design process. The paper also lists the lessons learned to aid testing of next generation robotic in-space assembly joints

Prediction of Cognitive States During Flight Simulation Using Multimodal Psychophysiological Sensing

The Commercial Aviation Safety Team found the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness (ASA), and distraction was involved in all of them. Research on attention-related human performance limiting states (AHPLS) such as channelized attention, diverted attention, startle/surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors was implemented to simultaneously measure their physiological markers during a high fidelity flight simulation human subject study. Twenty-four pilot participants were asked to wear the sensors while they performed benchmark tasks and motion-based flight scenarios designed to induce AHPLS. Pattern classification was employed to predict the occurrence of AHPLS during flight simulation also designed to induce those states. Classifier training data were collected during performance of the benchmark tasks. Multimodal classification was performed, using pre-processed electroencephalography, galvanic skin response, electrocardiogram, and respiration signals as input features. A combination of one, some or all modalities were used. Extreme gradient boosting, random forest and two support vector machine classifiers were implemented. The best accuracy for each modality-classifier combination is reported. Results using a select set of features and using the full set of available features are presented. Further, results are presented for training one classifier with the combined features and for training multiple classifiers with features from each modality separately. Using the select set of features and combined training, multistate prediction accuracy averaged 0.64 +/- 0.14 across thirteen participants and was significantly higher than that for the separate training case. These results support the goal of demonstrating simultaneous real-time classification of multiple states using multiple sensing modalities in high fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents