The Effects Of Video Frame Delay And Spatial Ability On The Operation Of Multiple Semiautonomous And Tele-operated Robots

The United States Army has moved into the 21st century with the intent of redesigning not only the force structure but also the methods by which we will fight and win our nation\u27s wars. Fundamental in this restructuring is the development of the Future Combat Systems (FCS). In an effort to minimize exposure of front line soldiers the future Army will utilize unmanned assets for both information gathering and when necessary engagements. Yet this must be done judiciously, as the bandwidth for net-centric warfare is limited. The implication is that the FCS must be designed to leverage bandwidth in a manner that does not overtax computational resources. In this study alternatives for improving human performance during operation of teleoperated and semi-autonomous robots were examined. It was predicted that when operating both types of robots, frame delay of the semi-autonomous robot would improve performance because it would allow operators to concentrate on the constant workload imposed by the teleoperated while only allocating resources to the semi-autonomous during critical tasks. An additional prediction was that operators with high spatial ability would perform better than those with low spatial ability, especially when operating an aerial vehicle. The results can not confirm that frame delay has a positive effect on operator performance, though power may have been an issue, but clearly show that spatial ability is a strong predictor of performance on robotic asset control, particularly with aerial vehicles. In operating the UAV, the high spatial group was, on average, 30% faster, lazed 12% more targets, and made 43% more location reports than the low spatial group. The implications of this study indicate that system design should judiciously manage workload and capitalize on individual ability to improve performance and are relevant to system designers, especially in the military community

Handing over the keys: a qualitative study of the experience of automation in driving

This article presents a qualitative driving simulator study designed to understand the experience of giving up control to automated processes in semiautonomous driving systems. The study employed an experience prototyping methodology, with 12 drivers (4 female) completing 2 sessions in a high-fidelity driving simulator. Condition A simulated a normally functioning car, while Condition B simulated a semiautonomous system that monitors driver behavior and takes evasive action when danger is detected. The simulator experience was used to ground wider discussion of automation and the experience of driving, which was explored through a semistructured interview. Results identify design challenges for autonomous driving systems; the loss of user agency and confidence, and handling the change between manual and automated control. Opportunities were identified; in augmenting rather than removing human abilities, and in providing new learning opportunities for drivers

Identifying Modes of Intent from Driver Behaviors in Dynamic Environments

In light of growing attention of intelligent vehicle systems, we propose developing a driver model that uses a hybrid system formulation to capture the intent of the driver. This model hopes to capture human driving behavior in a way that can be utilized by semi- and fully autonomous systems in heterogeneous environments. We consider a discrete set of high level goals or intent modes, that is designed to encompass the decision making process of the human. A driver model is derived using a dataset of lane changes collected in a realistic driving simulator, in which the driver actively labels data to give us insight into her intent. By building the labeled dataset, we are able to utilize classification tools to build the driver model using features of based on her perception of the environment, and achieve high accuracy in identifying driver intent. Multiple algorithms are presented and compared on the dataset, and a comparison of the varying behaviors between drivers is drawn. Using this modeling methodology, we present a model that can be used to assess driver behaviors and to develop human-inspired safety metrics that can be utilized in intelligent vehicular systems.Comment: Submitted to ITSC 201

Planning for execution monitoring on a planetary rover

A planetary rover will be traversing largely unknown and often unknowable terrain. In addition to geometric obstacles such as cliffs, rocks, and holes, it may also have to deal with non-geometric hazards such as soft soil and surface breakthroughs which often cannot be detected until rover is in imminent danger. Therefore, the rover must monitor its progress throughout a traverse, making sure to stay on course and to detect and act on any previously unseen hazards. Its onboard planning system must decide what sensors to monitor, what landmarks to take position readings from, and what actions to take if something should go wrong. The planning systems being developed for the Pathfinder Planetary Rover to perform these execution monitoring tasks are discussed. This system includes a network of planners to perform path planning, expectation generation, path analysis, sensor and reaction selection, and resource allocation

SAFTE-VAT Functionality Effects on Flight Instructors\u27 Situation Awareness and Instrument Student Pilots\u27 Performance during FTD Training

SAFTE-VAT is a virtual air traffic control systems that adds the capability to integrate automated air traffic control functionality and generate semiautonomous and autonomous air traffic to the Frasca 172S level 6 plus FTD to improve behavioral fidelity and to facilitate flight instructors the capacity to focus more on instructing student pilots instead of role-playing ATC duties. While SAFTE-VAT may offer a more realistic ATC interaction experience onboard the FTD that may result in a positive transfer of training increase, the effects on flight instructors’ situation awareness and overall student pilot performance are uncertain. In this small study, flight instructors and instrument student pilots were observed completing while lesson 32 of the FA221 instrument course was onboard a Frasca 172S level 6 plus FTD with and without the use of SAFTE-VAT. During each FTD lesson 32 flight instructors were queried to test for situation awareness. Student performance data was collected upon completion of each FTD lesson and analyzed. The results revealed the possibility of situation awareness decrease during periods of low FTD activity levels when SAFTE-VAT was used. Student performance data favored the lessons conducted without the SAFTE-VAT

Semiautonomous Robotic Manipulator for Minimally Invasive Aortic Valve Replacement

Aortic valve surgery is the preferred procedure for replacing a damaged valve with an artificial one. The ValveTech robotic platform comprises a flexible articulated manipulator and surgical interface supporting the effective delivery of an artificial valve by teleoperation and endoscopic vision. This article presents our recent work on force-perceptive, safe, semiautonomous navigation of the ValveTech platform prior to valve implantation. First, we present a force observer that transfers forces from the manipulator body and tip to a haptic interface. Second, we demonstrate how hybrid forward/inverse mechanics, together with endoscopic visual servoing, lead to autonomous valve positioning. Benchtop experiments and an artificial phantom quantify the performance of the developed robot controller and navigator. Valves can be autonomously delivered with a 2.0±0.5 mm position error and a minimal misalignment of 3.4±0.9°. The hybrid force/shape observer (FSO) algorithm was able to predict distributed external forces on the articulated manipulator body with an average error of 0.09 N. FSO can also estimate loads on the tip with an average accuracy of 3.3%. The presented system can lead to better patient care, delivery outcome, and surgeon comfort during aortic valve surgery, without requiring sensorization of the robot tip, and therefore obviating miniaturization constraints.</p

Autoplan: A self-processing network model for an extended blocks world planning environment

Self-processing network models (neural/connectionist models, marker passing/message passing networks, etc.) are currently undergoing intense investigation for a variety of information processing applications. These models are potentially very powerful in that they support a large amount of explicit parallel processing, and they cleanly integrate high level and low level information processing. However they are currently limited by a lack of understanding of how to apply them effectively in many application areas. The formulation of self-processing network methods for dynamic, reactive planning is studied. The long-term goal is to formulate robust, computationally effective information processing methods for the distributed control of semiautonomous exploration systems, e.g., the Mars Rover. The current research effort is focusing on hierarchical plan generation, execution and revision through local operations in an extended blocks world environment. This scenario involves many challenging features that would be encountered in a real planning and control environment: multiple simultaneous goals, parallel as well as sequential action execution, action sequencing determined not only by goals and their interactions but also by limited resources (e.g., three tasks, two acting agents), need to interpret unanticipated events and react appropriately through replanning, etc

Multi-axis control of telemanipulators

The development of multi-axis hand controllers for use in telemanipulator systems is described. Experience in the control of the SRMS (shuttle remote manipulator system) arm is reviewed together with subsequent tests involving a number of simulators and configurations, including use as a side-arm flight control for helicopters. The factors affecting operator acceptability are reviewed