Minotaurs, Not Centaurs: The Future of Manned-Unmanned Teaming

Contesting Paul Scharre’s influential vision of “centaur warfighting” and the idea that autonomous weapon systems will replace human warfighters, this article proposes that the manned-unmanned teams of the future are more likely to be minotaurs, teams of humans under the control, supervision, or command of artificial intelligence. It examines the likely composition of the future force and prompts a necessary conversation about the ethical issues raised by minotaur warfighting

Video summary - Neptus, command and control infrastructure for heterogeneous teams of autonomous vehicles

This video shows a brief overview over Neptus, a command and control infrastructure for heterogeneous teams of autonomous vehicles. Having different,types of vehicles at our laboratory and from our partners, there was an increasing need to create a common infrastructure to all these systems. Additionally, a tool to support the entire mission life cycle (Planning, Execution, Review and Dissemination) was lacking. Neptus was created to provide vehicle independence and seamless inter-systems communications. Currently, Neptus has been already tested with Remotely Operated Vehicles, Autonomous Underwater Vehicles, Unmanned Air Vehicles, Autonomous Surface Vehicles and Wireless Sensor Networks. Some of these systems were operated simultaneously by various operating consoles that were sharing the same communication infrastructure. The received data was being relayed to a web server that allowed for the real-time mission following by using a common web browser

Toward Computational Modeling of C2 for Teams of Autonomous Systems and People (19th ICCRTS)

19th ICCRTS, Toward Computational Modeling of C2 for Teams of Autonomous Systems and People, Autonomy Track – Paper 116The technological capabilities of autonomous systems (AS) continue to accelerate. Although AS are replacing people in many skilled mission domains and demanding environmental circumstances, people and machines have complementary capabilities, and integrated performance by AS and people working together can be superior to that of either AS or people working alone. We refer to this increasingly important phenomenon as Teams of Autonomous Systems and People (TASP), and we identify a plethora of open, command and control (C2) research, policy and decision making questions. Computational modeling and simulation offer unmatched yet largely unexplored potential to address C2 questions along these lines. The central problem is, this kind of C2 organization modeling and simulation capability has yet to be developed and demonstrated in the TASP domain. This is where our ongoing research project begins to make an important contribution. In this article, we motivate and introduce such TASP research, and we provide an overview of the computational environment used to model and simulate TASP C2 organizations and phenomena. We follow in turn with an approach to characterizing a matrix of diverse TASP C2 contexts, as well as a strategy for specifying, tailoring and using this computational environment to conduct experiments to examine such contexts. We conclude then by summarizing our agenda for continued research along these lines

A Complex Adaptive Systems Perspective on Self-Organization in IS Project Portfolios

Portfolio management practices and theory continue to remain focused on a centralized “command and control” perspective. Even though many organizations promote and encourage self-organization, particularly within their software development teams, little is known about how or if IS project portfolios self-organize. Previous studies have explored self-organization at organizational, team, or project level, but do not explore self-organization at portfolio level. Self-organization facilitates the acceptance of innovative ideas and enables autonomous teams to respond to changes in requirements or in the environment without management intervention. This research-in-progress paper aims to firstly contribute to research by using the theory of complex adaptive systems to explain how one aspect of control, namely self-organization, can occur in portfolios of IS projects. Secondly, this study will, through the use of exploratory case studies, contribute to practice by determining the implications and challenges for managers of self-organizing IS portfolios

Computational experimentation to understand C2 for Teams of Autonomous Systems and People

The technological capabilities of autonomous systems (AS) continue to accelerate. Although AS are replacing people in many skilled mission domains and demanding environmental circumstances, people and machines have complementary capabilities, and integrated performance by AS and people working together can be superior to that of either AS or people working alone. We refer to this increasingly important phenomenon as Teams of Autonomous Systems and People (TASP), and we identify a plethora of open, command and control (C2) research, policy and decision making questions. Computational experimentation offers unmatched yet largely unexplored potential to address C2 questions along these lines. The central problem is, this kind of C2 organization experimentation capability has yet to be developed and demonstrated in the TASP domain. This is where our ongoing research project begins to make an important contribution. In this article, we motivate and introduce such TASP research, and we provide an overview of the computational environment used to experiment on TASP C2 organizations and phenomena. We summarize in turn the research method. Key results follow, and we conclude then by summarizing our agenda for continued research along these lines.Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)Consortium for Robotics and Unmanned Systems Education and Research (CRUSER)Approved for public release; distribution is unlimited

Post-Westgate SWAT : C4ISTAR Architectural Framework for Autonomous Network Integrated Multifaceted Warfighting Solutions Version 1.0 : A Peer-Reviewed Monograph

Police SWAT teams and Military Special Forces face mounting pressure and challenges from adversaries that can only be resolved by way of ever more sophisticated inputs into tactical operations. Lethal Autonomy provides constrained military/security forces with a viable option, but only if implementation has got proper empirically supported foundations. Autonomous weapon systems can be designed and developed to conduct ground, air and naval operations. This monograph offers some insights into the challenges of developing legal, reliable and ethical forms of autonomous weapons, that address the gap between Police or Law Enforcement and Military operations that is growing exponentially small. National adversaries are today in many instances hybrid threats, that manifest criminal and military traits, these often require deployment of hybrid-capability autonomous weapons imbued with the capability to taken on both Military and/or Security objectives. The Westgate Terrorist Attack of 21st September 2013 in the Westlands suburb of Nairobi, Kenya is a very clear manifestation of the hybrid combat scenario that required military response and police investigations against a fighting cell of the Somalia based globally networked Al Shabaab terrorist group.Comment: 52 pages, 6 Figures, over 40 references, reviewed by a reade

Autonomous Capabilities for Small Unmanned Aerial Systems Conducting Radiological Response: Findings from a High-fidelity Discovery Experiment

This article presents a preliminary work domain theory and identifies autonomous vehicle, navigational, and mission capabilities and challenges for small unmanned aerial systems (SUASs) responding to a radiological disaster. Radiological events are representative of applications that involve flying at low altitudes and close proximities to structures. To more formally understand the guidance and control demands, the environment in which the SUAS has to function, and the expected missions, tasks, and strategies to respond to an incident, a discovery experiment was performed in 2013. The experiment placed a radiological source emitting at 10 times background radiation in the simulated collapse of a multistory hospital. Two SUASs, an AirRobot 100B and a Leptron Avenger, were inserted with subject matter experts into the response, providing high operational fidelity. The SUASs were expected by the responders to fly at altitudes between 0.3 and 30 m, and hover at 1.5 m from urban structures. The proximity to a building introduced a decrease in GPS satellite coverage, challenging existing vehicle autonomy. Five new navigational capabilities were identified: scan, obstacle avoidance, contour following, environment-aware return to home, andreturn to highest reading. Furthermore, the data-to-decision process could be improved with autonomous data digestion and visualization capabilities. This article is expected to contribute to a better understanding of autonomy in a SUAS, serve as a requirement document for advanced autonomy, and illustrate how discovery experimentation serves as a design tool for autonomous vehicles