Teams organization and performance analysis in autonomous human-robot teams

This paper proposes a theory of human control of robot teams based on considering how people coordinate across different task allocations. Our current work focuses on domains such as foraging in which robots perform largely independent tasks. The present study addresses the interaction between automation and organization of human teams in controlling large robot teams performing an Urban Search and Rescue (USAR) task. We identify three subtasks: perceptual search-visual search for victims, assistance-teleoperation to assist robot, and navigation-path planning and coordination. For the studies reported here, navigation was selected for automation because it involves weak dependencies among robots making it more complex and because it was shown in an earlier experiment to be the most difficult. This paper reports an extended analysis of the two conditions from a larger four condition study. In these two "shared pool" conditions Twenty four simulated robots were controlled by teams of 2 participants. Sixty paid participants (30 teams) were recruited to perform the shared pool tasks in which participants shared control of the 24 UGVs and viewed the same screens. Groups in the manual control condition issued waypoints to navigate their robots. In the autonomy condition robots generated their own waypoints using distributed path planning. We identify three self-organizing team strategies in the shared pool condition: joint control operators share full authority over robots, mixed control in which one operator takes primary control while the other acts as an assistant, and split control in which operators divide the robots with each controlling a sub-team. Automating path planning improved system performance. Effects of team organization favored operator teams who shared authority for the pool of robots. © 2010 ACM

Operational Concepts for Uninhabited Tactical Aircraft

This paper describes experiences with five remotely piloted flight research vehicle projects in the developmental flight test phase. These projects include the Pathfinder, Perseus B, Altus, and X-36 aircraft and the Highly Maneuverable Aircraft Technology (HiMAT). Each of these flight projects was flown at the NASA Dryden Flight Research Center. With the exception of the HiMAT, these projects are a part of the Flight Research Base Research and Technology (R&T) Program of the NASA Aeronautics and Space Transportation Technology Enterprise. Particularly with respect to operational interfaces between the ground-based pilot or operator, this paper draws from those experiences, then provides some rationale for extending the lessons learned during developmental flight research to the possible situations involved in the developmental flights proceeding deployed uninhabited tactical aircraft (UTA) operations. Two types of UTA control approaches are considered: autonomous and remotely piloted. In each of these cases, some level of human operator or pilot control blending is recommended. Additionally, "best practices" acquired over years of piloted aircraft experience are drawn from and presented as they apply to operational UTA

Optimal Control of an Uninhabited Loyal Wingman

As researchers strive to achieve autonomy in systems, many believe the goal is not that machines should attain full autonomy, but rather to obtain the right level of autonomy for an appropriate man-machine interaction. A common phrase for this interaction is manned-unmanned teaming (MUM-T), a subset of which, for unmanned aerial vehicles, is the concept of the loyal wingman. This work demonstrates the use of optimal control and stochastic estimation techniques as an autonomous near real-time dynamic route planner for the DoD concept of the loyal wingman. First, the optimal control problem is formulated for a static threat environment and a hybrid numerical method is demonstrated. The optimal control problem is transcribed to a nonlinear program using direct orthogonal collocation, and a heuristic particle swarm optimization algorithm is used to supply an initial guess to the gradient-based nonlinear programming solver. Next, a dynamic and measurement update model and Kalman filter estimating tool is used to solve the loyal wingman optimal control problem in the presence of moving, stochastic threats. Finally, an algorithm is written to determine if and when the loyal wingman should dynamically re-plan the trajectory based on a critical distance metric which uses speed and stochastics of the moving threat as well as relative distance and angle of approach of the loyal wingman to the threat. These techniques are demonstrated through simulation for computing the global outer-loop optimal path for a minimum time rendezvous with a manned lead while avoiding static as well as moving, non-deterministic threats, then updating the global outer-loop optimal path based on changes in the threat mission environment. Results demonstrate a methodology for rapidly computing an optimal solution to the loyal wingman optimal control problem

Robust Average Formation Tracking for Multi-Agent Systems With Multiple Leaders

In this paper, the formation tracking problem of the multi-agent system under disturbances and unmodeled uncertainties has been studied. An identifier-based robust control algorithm using the neighboring relative information has been proposed to ensure the followers to maintain a given, and time-varying formation and track the average state of the leaders at the same time. Some sufficient conditions for the second-order multi-agent system with multiple leaders in the presence of disturbances and unmodeled uncertainties have been proposed based on the graph theory and the Lyapunov method. Numerical simulations are provided to testify the validity of the algorithm

Integration of Autonomous UAVs into Multi-agent Simulation

In recent years, Unmanned Aerial Vehicles (UAVs) have attracted much attention both in the research field and in the field of commercial deployment. Researchers recently started to study problems and opportunities connected with the usage, deployment and operation of teams of multiple autonomous UAVs. These multi-UAV scenarios are by their nature well suited to be modelled and simulated as multi-agent systems. In this paper we present solutions to the problems that we had to deal with in the process of integrating two hardware UAVs into an existing multi-agent simulation system with additional virtual UAVs, resulting in a mixed reality system where hardware UAVs and virtual UAVs can co-exist, coordinate their flight and cooperate on common tasks. Hardware UAVs are capable of on-board planning and reasoning, and can cooperate and coordinate their movement with one another, and also with virtual UAVs

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

Mixed Initiative Systems for Human-Swarm Interaction: Opportunities and Challenges

Human-swarm interaction (HSI) involves a number of human factors impacting human behaviour throughout the interaction. As the technologies used within HSI advance, it is more tempting to increase the level of swarm autonomy within the interaction to reduce the workload on humans. Yet, the prospective negative effects of high levels of autonomy on human situational awareness can hinder this process. Flexible autonomy aims at trading-off these effects by changing the level of autonomy within the interaction when required; with mixed-initiatives combining human preferences and automation's recommendations to select an appropriate level of autonomy at a certain point of time. However, the effective implementation of mixed-initiative systems raises fundamental questions on how to combine human preferences and automation recommendations, how to realise the selected level of autonomy, and what the future impacts on the cognitive states of a human are. We explore open challenges that hamper the process of developing effective flexible autonomy. We then highlight the potential benefits of using system modelling techniques in HSI by illustrating how they provide HSI designers with an opportunity to evaluate different strategies for assessing the state of the mission and for adapting the level of autonomy within the interaction to maximise mission success metrics.Comment: Author version, accepted at the 2018 IEEE Annual Systems Modelling Conference, Canberra, Australi

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

Authority Management and Conflict Solving in Human-Machine Systems

This paper focuses on vehicle-embedded decision autonomy and the human operator’s role in so-called autonomous systems. Autonomy control and authority sharing are discussed, and the possible effects of authority conflicts on the human operator’s cognition and situation awareness are highlighted. As an illustration, an experiment conducted at ISAE (the French Aeronautical and Space Institute) shows that the occurrence of a conflict leads to a perseveration behavior and attentional tunneling of the operator. Formal methods are discussed to infer such attentional impairment from the monitoring of physiological and behavioral measures and some results are given