Multiple Robot-Multiple Operator Control and Teamwork: Lessons Learned and Design Guidelines

This paper investigates the formation control of multiple Unmanned Aerial Vehicles (UAVs), particularly unmanned aircraft, in an obstacle-laden environment. The main contribution of the paper is to integrate the formation control, trajectory tracking, and obstacle/collision avoidance into one unified optimal control framework. The non-quadratic avoidance cost is innovatively constructed via an inverse optimal control approach, which leads to an analytical, distributed, and optimal formation control law. The stability and optimality of the closed-loop system are proved. In addition, the proposed optimal control law is only dependent on the information from the local neighbors, rather than all UAVs\u27 information. Simulation of multiple UAVs\u27 formation flying demonstrates the effectiveness of the integrated optimal control design with desired behaviors including formation flying, trajectory tracking, and obstacle/collision avoidance. © 2012 by Jianan Wang and Ming Xin

The Influence of Camouflage, Obstruction, Familiarity, and Spatial Ability on Target Identification from an Unmanned Ground Vehicle

The purpose of this study was to examine the effects of environmental and cognitive factors on the identification of targets from an unmanned ground vehicle (UGV). This was accomplished by manipulating obstruction, camouflage and familiarity of objects in the environment, while also measuring spatial ability. The effects of these variables on target identification were studied by measuring performance of participants that observed pre-recorded video from a 1:35 scaled military operations in urban terrain facility. Analyses indicated that a combination of camouflage and obstruction caused the most detrimental effects on performance, and that there were differences in the recognition of familiar and unfamiliar targets. Further analysis indicated that these detrimental effects could only be overcome with a combination of target familiarity and spatial ability. The findings highlight the degree to which environmental factors hinder performance and the need for a multidimensional approach for improving performance under these conditions. Areas in need of future research are also discussed

Leveraging Features of Human—Technology Teams to Support Mental Models in Future Soldier—Robot Teams

The future vision of military robotics is one in which robots will serve as integrated members of Soldier - robot teams. Robots will possess capabilities that will transition their role from functional tools to working teammates. Because robots and Soldiers will be deployed in environments characterized by uncertainty, complexity, and violence, it is imperative that Soldiers have accurate mental models of what their robotic teammates can do, cannot do, and will likely do. In this paper, we present the conclusions of a review into metaphors for facilitating accurate mental models of robotic teammates. Emphasis was placed on investigating existing human - technology teams (i.e., human teaming with automated systems including autopilot in cockpits, driver assistance systems, and personal assistant applications among others) for features that can support accurate mental models for Soldiers in future Soldier - robot teams