adjustable autonomy for uav supervision applications through mental workload assessment techniques

In recent years, unmanned aerial vehicles have received a significant attention in the research community, due to their adaptability in different applications, such as surveillance, disaster response, traffic monitoring, transportation of goods, first aid, etc. Nowadays, even though UAVs can be equipped with some autonomous capabilities, they often operate in high uncertainty environments in which supervisory systems including human in the control loop are still required. Systems envisaging decision-making capabilities and equipped with flexible levels of autonomy are needed to support UAVs controllers in monitoring operations. The aim of this paper is to build an adjustable autonomy system able to assist UAVs controllers by predicting mental workload changes when the number of UAVs to be monitored highly increases. The proposed system adjusts its level of autonomy by discriminating situations in which operators' abilities are sufficient to perform UAV supervision tasks from situations in which system suggestions or interventions may be required. Then, a user study was performed to create a mental-workload prediction model based on operators' cognitive demand in drone monitoring operations. The model is exploited to train the system developed to infer the appropriate level of autonomy accordingly. The study provided precious indications to be possibly exploited for guiding next developments of the adjustable autonomy system proposed

Mental workload assessment for UAV traffic control using consumer-grade BCI equipment

The increasing popularity of unmanned aerial vehicles (UAVs) in critical applications makes supervisory systems based on the presence of human in the control loop of crucial importance. In UAV-traffic monitoring scenarios, where human operators are responsible for managing drones, systems flexibly supporting different levels of autonomy are needed to assist them when critical conditions occur. The assessment of UAV controllers' performance thus their mental workload may be used to discriminate the level and type of automation required. The aim of this paper is to build a mental-workload prediction model based on UAV operators' cognitive demand to support the design of an adjustable autonomy supervisory system. A classification and validation procedure was performed to both categorize the cognitive workload measured by ElectroEncephaloGram signals and evaluate the obtained patterns from the point of view of accuracy. Then, a user study was carried out to identify critical workload conditions by evaluating operators' performance in accomplishing the assigned tasks. Results obtained in this study provided precious indications for guiding next developments in the field

Survey of Bayesian Networks Applications to Intelligent Autonomous Vehicles

This article reviews the applications of Bayesian Networks to Intelligent Autonomous Vehicles (IAV) from the decision making point of view, which represents the final step for fully Autonomous Vehicles (currently under discussion). Until now, when it comes making high level decisions for Autonomous Vehicles (AVs), humans have the last word. Based on the works cited in this article and analysis done here, the modules of a general decision making framework and its variables are inferred. Many efforts have been made in the labs showing Bayesian Networks as a promising computer model for decision making. Further research should go into the direction of testing Bayesian Network models in real situations. In addition to the applications, Bayesian Network fundamentals are introduced as elements to consider when developing IAVs with the potential of making high level judgement calls.Comment: 34 pages, 2 figures, 3 table

Human-Machine Interfaces for Service Robotics

L'abstract è presente nell'allegato / the abstract is in the attachmen

Human Performance Modeling: Analysis of the Effects of Manned-Unmanned Teaming on Pilot Workload and Mission Performance

Due to the advent of autonomous technology coupled with the extreme expense of manned aircraft, the Department of Defense (DoD) has increased interest in developing affordable, expendable Unmanned Aerial Vehicles (UAVs) to become autonomous wingmen for jet fighters in mosaic warfare. Like a mosaic that forms a whole picture out of smaller pieces, battlefield commanders can utilize disaggregated capabilities, such as Manned-Unmanned Teaming (MUM-T), to operate in contested environments. With a single pilot controlling both the UAVs and manned aircraft, it may be challenging for pilots to manage all systems should the system design not be conducive to a steady state level of workload. To understand the potential effects of MUM-T on the pilot’s cognitive workload, an Improved Performance Research Integration Tool (IMPRINT) Pro pilot workload model was developed. The model predicts the cognitive workload of the pilot in a simulated environment when interacting with both the cockpit and multiple UAVs to provide insight into the effect of Human-Agent Interactions (HAI) and increasing autonomous control abstraction on the pilot’s cognitive workload and mission performance. This research concluded that peaks in workload occur for the pilot during periods of high communications load and this communication may be degraded or delayed during air-to-air engagements. Nonetheless, autonomous control of the UAVs through a combination of Vector Steering, Pilot Directed Engagements, and Tactical Battle Management would enable pilots to successfully command up to 3 UAVs as well as their own aircraft against 4 enemy targets, while maintaining acceptable pilot cognitive workload in an air-to-air mission scenario

Modeling human supervisory control in heterogeneous unmanned vehicle systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2009.Includes bibliographical references (p. 189-195).Given advanced technology that relieves the human operator of low-level tasking and the future vision for network-centric operations, operator supervisory control of Unmanned Vehicle (UV) teams is likely to be a focal point of future research and development. Due to requirements for interoperability among UVs of varying attributes, heterogeneity in vehicle capabilities and tasks is likely to exist in future UV systems. This will lead to a large design space for these systems, which will cause design validation to require lengthy and expensive human-in-the-loop experimentation. This problem is addressed in this thesis through the following: First, identification of human-UV interaction attributes and associated variables that should be captured when modeling supervisory control of heterogeneous UV systems. Second, the derivation of a queuing-based multi-UV discrete event simulation (MUV-DES) model that captures both vehicle-team variables (including team composition and level of autonomy) and operator variables (including attention allocation strategies and situational awareness). The MUV-DES model supports design validation by simulating the impact of alternate designs on vehicle, operator, and system performance. To determine the accuracy and robustness of the MUV-DES model, an Internet-based test bed was developed to support extensive and rapid data collection for supervisory control of multiple heterogeneous UVs. Using data accumulated from online experiments, a multi-stage validation process was applied. The validation process resulted in achieving confidence in the model's accuracy and determination of the model's robustness under different input settings. Following the validation process, the MUV-DES model's ability to aid in the design and assessment of heterogeneous UV teams and related technologies was evaluated.(cont.) More specifically, the MUV-DES model generated design recommendations addressing three underlying research objectives: a) indicating how potential operational/developmental design modifications could lead to performance improvements including 30% reductions in average vehicle wait times, b) identifying potential capabilities and limitations of future designs, including the detrimental impact of service time heterogeneity greater than 40% on average vehicle wait times, and c) replicating observed behavior in an existing system as a means of diagnosing the causes of vehicle-performance inefficiency. A subset of the MUV-DES model design recommendations was then implemented and the predicted benefit was validated using an additional set of experiments.by Carl E. Nehme.Ph.D