Real Time Mission Planning

The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission

Real Time Mission Planning

The different advantageous embodiments provide a system comprising a number of computers, a graphical user interface, first program code stored on the computer, and second program code stored on the computer. The graphical user interface is executed by a computer in the number of computers. The computer is configured to run the first program code to define a mission using a number of mission elements. The computer is configured to run the second program code to generate instructions for a number of assets to execute the mission and monitor the number of assets during execution of the mission

Severe Asthma Standard-of-Care Background Medication Reduction With Benralizumab: ANDHI in Practice Substudy

Background: The phase IIIb, randomized, parallel-group, placebo-controlled ANDHI double-blind (DB) study extended understanding of the efficacy of benralizumab for patients with severe eosinophilic asthma. Patients from ANDHI DB could join the 56-week ANDHI in Practice (IP) single-arm, open-label extension substudy. Objective: Assess potential for standard-of-care background medication reductions while maintaining asthma control with benralizumab. Methods: Following ANDHI DB completion, eligible adults were enrolled in ANDHI IP. After an 8-week run-in with benralizumab, there were 5 visits to potentially reduce background asthma medications for patients achieving and maintaining protocol-defined asthma control with benralizumab. Main outcome measures for non-oral corticosteroid (OCS)-dependent patients were the proportions with at least 1 background medication reduction (ie, lower inhaled corticosteroid dose, background medication discontinuation) and the number of adapted Global Initiative for Asthma (GINA) step reductions at end of treatment (EOT). Main outcomes for OCS-dependent patients were reductions in daily OCS dosage and proportion achieving OCS dosage of 5 mg or lower at EOT. Results: For non-OCS-dependent patients, 53.3% (n = 208 of 390) achieved at least 1 background medication reduction, increasing to 72.6% (n = 130 of 179) for patients who maintained protocol-defined asthma control at EOT. A total of 41.9% (n = 163 of 389) achieved at least 1 adapted GINA step reduction, increasing to 61.8% (n = 110 of 178) for patients with protocol-defined EOT asthma control. At ANDHI IP baseline, OCS dosages were 5 mg or lower for 40.4% (n = 40 of 99) of OCS-dependent patients. Of OCS-dependent patients, 50.5% (n = 50 of 99) eliminated OCS and 74.7% (n = 74 of 99) achieved dosages of 5 mg or lower at EOT. Conclusions: These findings demonstrate benralizumab's ability to improve asthma control, thereby allowing background medication reduction

Toward Measures of Human-robot Teaming Effectiveness

As robot capabilities rapidly evolve, the dynamics of human-robot teams will change. Autonomous, intelligent technologies will come to serve in roles that more closely resemble those of teammates, as opposed to tools. This will require humans to adapt and remain agile in developing novel strategies and tactics for employing these systems in complex, real-world scenarios. Building on previous work that presented a novel data set collected from teams of humans and robots playing capture the flag, the current research aims to identify measures capable of predicting successful teaming that lead to a winning outcome. Three case studies highlight the difficulty in characterizing human-robot interaction and game play to create an objective score

Insights into Expertise and Tactics in Human-Robot Teaming

As robot capabilities rapidly evolve, the dynamics of human-robot teams will change. Autonomous, intelligent technologies will come to serve in roles that more closely resemble those of teammates, as opposed to tools. This will require humans to adapt and remain agile in developing novel strategies and tactics for employing these systems in complex, real-world scenarios. Building on previous work that presented a novel data set collected from teams of humans and robots playing capture the flag, the current research aims to identify measures capable of predicting successful teaming that lead to a positive, winning outcomes in the game. Video and text log analysis were used to describe gameplay and identify specific successful tactics. In conjunction with the experience levels of the participants, a number of measures of communication with autonomous robot teammates and robot efficiency were used to predict game performance. Only one metric was found to successfully predict game outcomes across all four games: level of robot involvement with offensive maneuvers. Several possible mechanisms for this observation are discussed, as well as multiple directions for future research directions leveraging this human-robot teaming platform

Aquaticus:Publicly Available Datasets from a Marine Human-robot Teaming Testbed

In this paper, we introduce publicly available human-robot teaming datasets captured during the summer 2018 season using our Aquaticus testbed. Our Aquaticus testbed is designed to examine the interactions between human-human and human-robot teammates while situated in the marine environment in their own vehicles. In particular, we assess these interactions while humans and fully autonomous robots play a competitive game of capture the flag on the water. Our testbed is unique in that the humans are situated in the field with their fully autonomous robot teammates in vehicles that have similar dynamics. Having a competition on the water reduces the safety concerns and cost of performing similar experiments in the air or on the ground. By having the competitions on the water, we create a complex, dynamic, and partially observable view of the world for participants while in their motorized kayak. The main modality for teammate interaction is audio to better simulate the experience of real-world tactical situations - ie fighter pilots talking to each other over radios. We have released our complete datasets publicly so that we can enable researchers throughout the HRI community that do not have access to such a testbed and may have expertise other than our own to leverage our datasets to perform their own analysis and contribute to the HRI community

Insights into Expertise and Tactics in Human-Robot Teaming

As robot capabilities rapidly evolve, the dynamics of human-robot teams will change. Autonomous, intelligent technologies will come to serve in roles that more closely resemble those of teammates, as opposed to tools. This will require humans to adapt and remain agile in developing novel strategies and tactics for employing these systems in complex, real-world scenarios. Building on previous work that presented a novel data set collected from teams of humans and robots playing capture the flag, the current research aims to identify measures capable of predicting successful teaming that lead to a positive, winning outcomes in the game. Video and text log analysis were used to describe game play and identify specific successful tactics. In conjunction with the experience levels of the participants, a number of measures of communication with autonomous robot teammates and robot efficiency were used to predict game performance. Only one metric was found to successfully predict game outcomes across all four games: level of robot involvement with offensive maneuvers. Several possible mechanisms for this observation are discussed, as well as multiple directions for future research directions leveraging this human-robot teaming platform

Preliminary Interactions of Human-Robot Trust, Cognitive Load, and Robot Intelligence Levels in a Competitive Game

This paper presents a pilot study in which we examine the interactions between human-robot teammate trust, cognitive load, and robot intelligence levels. In particular, we attempt to assess these interactions during a competitive game of capture the flag played between a human and a robot. We present results while the human plays against robots of different intelligence levels and determines their level of trust of each robot as a potential teammate through a post experiment questionnaire. We also present our exploration of heart rate measures as approximations of cognitive load. It is our goal to determine guidelines for future autonomy and interaction designers such that their systems will reduce cognitive load and increase the level of trust in robot teammates. This is an initial experiment that uses the least amount of vehicles yet still gathers competitive data on the water. Future experiments will increase in complexity to many opponents and many teammates

Asymmetric Steering Hydrodynamics Identification of a Differential Drive Unmanned Surface Vessel.

This paper identifies the asymmetric steering characteristics of a Wave Adaptive Modular Vessel (WAM-V) deployed as an Unmanned Surface Vessel (USV). Differentially steered propellers create a virtual rudder movement without explicitly inducing lateral rudder forces. However, a rotating propeller will generate a small lateral force, depending on its rotational direction and speed, also known as propeller walk. The WAM-V USV uses two similar propellers to manoeuvre, hence the propeller walk effects are doubled. Consequentially, the vessel has asymmetric turning characteristics which result in different steering behaviours when turning port or starboard. Heading measurements and virtual rudder movements suffice for identifying these turning characteristics at a certain speed. To do so, a first order Nomoto model was chosen as identification model. Three varieties of this model were identified: one for turning port, one for turning starboard, and one that averages the two aforementioned cases. These offline identified Nomoto models can serve multiple objectives. They can be used for simulation purposes, which themselves can be used to test control algorithms offline. Moreover, the coefficients of the Nomoto model itself can be used to tune a Proportional Integral Derivative (PID) controller. Finally, the Nomoto models can also be used as a feed forward term in control algorithms.status: Published onlin