Leader-follower controllability on signed multiagent networks is investigated in this paper. Specifically, we consider a dynamic signed multiagent network, where the agents interact via neighbor-based Laplacian feedback and the network allows positive and negative edges to capture cooperative and competitive interactions among agents. The agents are classified as either leaders or followers, thus forming a leader-follower signed network. To enable full control of the leader-follower signed network, controllability ensured leader group selection approaches are investigated in this paper, that is, identifying a small subset of nodes in the signed network, such that the selected nodes are able to drive the network to a desired behavior, even in the presence of antagonistic interactions. In particular, graphical characterizations of the controllability of signed networks are first developed based on the investigation of the interaction between network topology and agent dynamics. Since signed path and cycle graphs are basic building blocks for a variety of networks, the developed topological characterizations are then exploited to develop leader selection methods for signed path and cycle graphs to ensure leader-follower controllability. Along with illustrative examples, heuristic algorithms are also developed showing how leader selection methods developed for path and cycle graphs can be potentially extended to more general signed networks. In contrast to existing results that mainly focus on unsigned networks, this paper characterizes controllability and develops leader selection methods for signed networks. In addition, the developed results are generic, in the sense that they are not only applicable to signed networks but also to unsigned networks, since unsigned networks are a particular case of signed networks that only contain positive edges.

A dialog-based human-autonomy interaction ap- proach, called curious partner, is presented for a class of systems where the role of autonomy is to assist humans in decision making tasks. Even if the human and the autonomy share the environment and receive identical information, they may have inconsistencies in the representation of the environment due to difference in their perception and expert knowledge. The curious partner interaction framework is presented to resolve model-level differences between the human and the autonomy to establish common ground. The knowledge base of the autonomy is modeled using a Bayesian engine. The autonomy's dialog with the human acts as a feedback mechanism to resolve any differences either by suggesting maximally probable actions to human based on the state of its Bayesian model or by updating its model to achieve analogous world representation.

The risk-aware path planning problem is considered, which aims to locate a target in a congested urban environment and facilitate aid in the decision making on target interdiction. The target is modeled as a ground vehicle moving randomly within a road network and following traffic rules. To locate the target, a heterogeneous sensor network composed of passive sensors (e.g., Static traffic cameras and mobile human observers) and active sensors (e.g., A UAV) is tasked to cooperatively search for the target. A sample-based Bayesian filter is developed to fuse various sensor measurements to estimate the target state. To facilitate the decision making on target interdiction, a notion of risk is considered, which evaluates the incurred loss of target interdiction at certain locations based on incomplete information of target state and urban factors (e.g., The proximity to critical areas such as populated shopping malls, schools, military, or government buildings). As opposed to the static traffic cameras and the randomly walking human observers that passively provide target measurements, the UAV actively plans its path, based on mutual information, to maximize the in formativeness of future measurements. In contrast to classical target tracking that only focuses on reducing the uncertainty of target state, the risk is encoded in the particle weights to guide the motion of UAV to improve target state estimation and, ultimately, reduce the risk of decision on target interdiction. Simulation results are provided to demonstrate the integrated sensing framework and the risk-aware path planning algorithm.

This paper considers stabilizing a nonlinear system with input constraints using switched control. The first result is that, for a given control Lyapunov function (CLF), a stabilizable set of initial conditions can be found. The paper then considers switching between multiple CLFs to increase the space of stabilizing inputs which also increases the set of stabilizable initial conditions. An algorithm is presented that identifies a subset of the known CLFs that may be used at the current time to produce stabilizing control inputs. While there are existing results for switched system stabilization using CLFs, stabilization for systems with input constraints is an original contribution. Sum of squares (SOS) methods are discussed for generating multiple CLFs, and examples are provided.

In this paper, context-aware communication that stabilizes a class of nonlinear networked control systems (NCS) operating over a bandwidth-limited shared-channel is proposed to reduce network traffic. The NCS under consideration includes feedback and feed forward communication channels, and is assumed to be perturbed by an unmodeled, nonlinear disturbance. Informational value of the output states can be analyzed in the context of system stability using model-based approaches. When fused with event-based triggering, these context-aware communication policies result in a periodic feedback and feed forward signals that guarantee uniformly ultimately bounded tracking of output states of an uncertain perturbed system along the desired time-varying trajectory. The proposed NCS is validated using extensive simulation results for nonlinear coupled MIMO systems.

A human-RRT (Rapidly-exploring Random Tree) collaborative algorithm is presented for path planning in urban environments. The well-known RRT algorithm is modified for efficient planning in cluttered, yet structured urban environments. To engage the expert human knowledge in dynamic replanning of autonomous vehicles, a graphical user interface is developed that enables interaction with the automated RRT planner in real-time. The interface can be used to invoke standard planning attributes such as way areas, space constrains, and waypoints. In addition, the human can draw desired trajectories using the touch interface for the RRT planner to follow. Based on new information and evidence collected by human, state-dependent risk or penalty to grow paths based on an objective function can also be specified using the interface.

In this paper, an algorithm for hard and soft data fusion is developed for tracking moving objects using hard data from sensors on autonomous agents and soft data from human observations. Two main challenges are identified and addressed in this paper: 1. how to model the human observation, 2. how to estimate state using soft data and fuse it with the state estimates from the sensors on autonomous agents (e.g., a camera sensor). A novel approach is developed to model perceived human observations to the real physical states using artificial neural networks (ANN). A particle filter (PF) is used to estimate a moving target's state based on range and bearing observation data from a human observer and an EKF is used to estimate the target state using on-board camera sensor. The range measurement is represented using Kumaraswamy's double bounded distribution. The state estimates computed based on a model of human observation learned by an ANN are fused with the state estimates from the on-board sensors using a fast covariance intersection (CI) algorithm. The CI algorithm yields consistent fused estimates in the absence of unknown correlations between state estimates obtained using human measurements and robot sensor measurements. The performance of the developed algorithms is validated on a target tracking simulation platform.

A novel approach for human-generated “soft information” modeling and Bayesian fusion using touch interface devices is presented. The human-generated soft information can be encoded using a combination of single, multiple, and overlapping strokes that represent arbitrary measurement likelihood functions which can be approximated using non-parametric density estimators. The proposed interface offers a flexible and natural medium to encode a large class of qualitatively distinct types of information for both positive and negative observations. The touch interface naturally provides robustness with respect to human variability in terms of psycho-physiological and environmental parameters without the need for offline training. An urban-target tracking example is provided to illustrate fusion of soft information (generated using the proposed soft sensor model) with measurements from traditional automated sensors.

In this report we describe the progress in developing a control architecture for human-in-the-loop wide area search munitions to reduce operator errors in the presence of unreliable automation and operator cognitive limitations. An optimal input tracking controller with adaptive automation uncertainty compensation and real-time workload assessment is developed to improve the system performance. Extensive simulations based on the experimental data involving 12 subjects demonstrate effectiveness of the presented controller.

In this paper, we formulate a visual servo control problem when a reference image corresponding to the desired position and orientation (i.e., pose) of a camera is not available. The desired pose is described in terms of the introduced angle of incidence and point of arrival of a camera coordinate frame. The presented problem is motivated by the application of homing missiles where a reference image may not be available and the angle of contact with a target is crucial to maximizing the target penetration. A new geometric approach is presented to formulate a rotation error system that minimizes the required control effort while achieving the desired angle of incidence. A nonlinear adaptive control law is developed to provide 6 degrees of freedom camera motion while compensating for an unknown target depth; stability analysis is presented to prove asymptotic regulation of the system states. High-fidelity numerical simulation results are provided to verify the performance of the proposed visual servo controller.