We introduce a performance-guaranteed limbic system-inspired control (LISIC) strategy for nonlinear multi-agent systems (MASs) with uncertain high-order dynamics and external perturbations, where each agent in the MAS incorporates a LISIC structure to support the consensus controller. This novel approach, which we call double integrator LISIC (DILISIC), is designed to imitate double integrator dynamics after closing the agent-specific control loop, allowing the control designer to apply consensus techniques specifically formulated for double integrator agents. The objective of each DILISIC structure is then to identify and compensate model differences between the theoretical assumptions considered when tuning the consensus protocol and the actual conditions encountered in the real-time system to be controlled. A Lyapunov analysis is provided to demonstrate the stability of the closed-loop MAS enhanced with the DILISIC. Additionally, the stabilization of a complex system via DILISIC is addressed in a synthetic scenario: the consensus control of a team of flexible single-link arms. The dynamics of these agents are of fourth order, contain uncertainties, and are subject to external perturbations. The numerical results validate the applicability of the proposed method.

This article investigates a political party or an association social network where members share a common set of beliefs. In modeling it as a distributed iterative algorithm with network dynamics mimicking the interactions between people, the problem of interest becomes that of determining: 1) the conditions when convergence happens in finite time and 2) the corresponding steady-state opinion. For a traditional model, it is shown that finite-time convergence requires a complete topology and that by removing neighbors with duplicate opinions reduces in half the number of links. Finite-time convergence is proved for two novel models even when nodes contact two other nodes of close opinion. In a deterministic setting, the network connectivity influences the final consensus and changes the relative weight of each node on the final value. In the case of mobile robots, a similar communication constraint is present which makes the analysis of the social network so relevant in the domain of control systems as a guideline to save resources and obtain finite-time consensus. Through simulations, the main results regarding convergence are illustrated paying special attention to the rates at which consensus is achieved.

This paper studies topological entropy of switched nonlinear systems. We construct a general upper bound for the topological entropy in terms of an average of the asymptotic suprema of the measures of Jacobian matrices of individual modes, weighted by the corresponding active rates. A general lower bound is constructed in terms of an active-rate-weighted average of the asymptotic infima of the traces of these Jacobian matrices. For switched systems with diagonal modes, we construct upper and lower bounds that only depend on the eigenvalues of Jacobian matrices, their relative order among individual modes, and the active rates. For both cases, we also construct more conservative upper bounds that require less information on the switching, with their relations illustrated by numerical examples of a switched Lotka-Volterra ecosystem model.

We present a method for optimal coordination of multiple vehicle teams when multiple endpoint configurations are equally desirable, such as seen in the autonomous assembly of formation flight. The individual vehicles' positions in the formation are not assigned a priori and a key challenge is to find the optimal configuration assignment along with the optimal control and trajectory. Commonly, assignment and trajectory planning problems are solved separately. We introduce a new multi-vehicle coordination paradigm, where the optimal goal assignment and optimal vehicle trajectories are found simultaneously from a viscosity solution of a single Hamilton-Jacobi (HJ) partial differential equation (PDE), which provides a necessary and sufficient condition for global optimality. Intrinsic in this approach is that individual vehicle dynamic models need not be the same, and therefore can be applied to heterogeneous systems. Numerical methods to solve the HJ equation have historically relied on a discrete grid of the solution space and exhibits exponential scaling with system dimension, preventing their applicability to multiple vehicle systems. By utilizing a generalization of the Hopf formula, we avoid the use of grids and present a method that exhibits polynomial scaling in the number of vehicles.

This paper studies topological entropy and stability properties of switched linear systems. First, we show that the exponential growth rates of solutions of a switched linear system are essentially upper bounded by its topological entropy. Second, we estimate the topological entropy of a switched linear system by decomposing it into a part that is generated by scalar multiples of the identity matrix and a part that has zero entropy, and proving that the overall topological entropy is upper bounded by that of the former. Third, we prove that a switched linear system is globally exponentially stable if its topological entropy remains zero under a destabilizing perturbation. Finally, the entropy estimation via decomposition and the entropy-based stability condition are applied to three classes of switched linear systems to construct novel upper bounds for topological entropy and novel sufficient conditions for global exponential stability.

We introduce a scalable observer architecture, which can efficiently estimate the states of a discrete-time linear-time-invariant system whose sensors are manipulated by an attacker, and is robust to measurement noise. Given an upper bound on the number of attacked sensors, we build on previous results on necessary and sufficient conditions for state estimation, and propose a novel Multi-Modal Luenberger (MML) observer based on efficient Satisfiability Modulo Theory (SMT) solving. We present two techniques to reduce the complexity of the estimation problem. As a first strategy, instead of a bank of distinct observers, we use a family of filters sharing a single dynamical equation for the states, but different output equations, to generate estimates corresponding to different subsets of sensors. Such an architecture can reduce the memory usage of the observer from an exponential to a linear function of the number of sensors. We then develop an efficient SMT-based decision procedure that is able to reason about the estimates of the MML observer to detect at runtime which sets of sensors are attack-free, and use them to obtain a correct state estimate. Finally, we discuss two optimization-based algorithms that can efficiently select the observer parameters with the goal of minimizing the sensitivity of the estimates with respect to sensor noise. We provide proofs of convergence for our estimation algorithm and report simulation results to compare its runtime performance with alternative techniques. We show that our algorithm scales well for large systems (including up to 5,000 sensors) for which many previously proposed algorithms are not implementable due to excessive memory and time requirements. Finally, we illustrate the effectiveness of our approach, both in terms of resiliency to attacks and robustness to noise, on the design of large-scale power distribution networks.

Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately or combined on centralized approaches that results in an inefficient use of resources, or in solutions that are not scalable in terms of the number of IoT devices. Therefore, we propose D-SLATS, a framework comprised of three different and independent algorithms to jointly solve time synchronization and localization problems in a distributed fashion. The first two algorithms are based mainly on the distributed Extended Kalman Filter (EKF) whereas the third one uses optimization techniques. No fusion center is required, and the devices only communicate with their neighbors. The proposed methods are evaluated on custom Ultra-Wideband communication Testbed and a quadrotor, representing a network of both static and mobile nodes. Our algorithms achieve up to three microseconds time synchronization accuracy and 30 cm localization error.

This work introduces the one-class slab SVM (OCSSVM), a one-class classifier that aims at improving the performance of the one-class SVM. The proposed strategy reduces the false positive rate and increases the accuracy of detecting instances from novel classes. To this end, it uses two parallel hyperplanes to learn the normal region of the decision scores of the target class. OCSSVM extends one-class SVM since it can scale and learn non-linear decision functions via kernel methods. The experiments on two publicly available datasets show that OCSSVM can consistently outperform the one-class SVM and perform comparable to or better than other state-of-the-art one-class classifiers.

This paper proposes a control algorithm based on adaptive dynamic programming to solve the infinite-horizon optimal control problem for known deterministic nonlinear systems with saturating actuators and nonquadratic cost functionals. The algorithm is based on an actor/critic framework, where a critic neural network (NN) is used to learn the optimal cost, and an actor NN is used to learn the optimal control policy. The adaptive control nature of the algorithm requires a persistence of excitation condition to be a priori validated, but this can be relaxed using previously stored data concurrently with current data in the update of the critic NN. A robustifying control term is added to the controller to eliminate the effect of residual errors, leading to the asymptotically stability of the closed-loop system. Simulation results show the effectiveness of the proposed approach for a controlled Van der Pol oscillator and also for a power system plant.

This paper studies the impact of clock mismatches in spatially distributed real-time control systems. We consider a configuration in which sensor measurements are collected by one processor that transmits the measurements to another control/actuation processor through a network, but the two processors do not have a common clock. Due to the clock mismatch, there will be an offset between the actual time at which a measurement is taken and the time reported by the sensor. Our goal is to discover fundamental limitations to the ability to stabilize the control loop arising from the clocks mismatch. We consider time-varying bounded offsets and derive limitations on the offset bound for the stability of the feedback system. For the case of a scalar linear process, there exists a critical limitation, which depends on the level of instability of the plant and the nominal sampling period. In contrast, for the vector linear processes, if the process dynamics has at least two distinct real eigenvalues, then there is no fundamental limitation on the offset bound.

A probabilistic approach to 3-dimensional mapping is proposed that only uses data gathered by GNSS (Global Navigation Satellite System) devices. To accomplish this, the environment is gridded and a physically motivated sensor model is developed that assigns likelihoods of blockage to satellite signals based on their measured SNR (signal-to-noise ratio). It is then shown that the posterior distribution of the map represents a sparse factor graph on which a low complexity implementation of Loopy Belief Propagation can be used for efficient Bayesian estimation. Experimental results are presented which demonstrate our algorithm's ability to coarsely map in three dimensions a corner of a university campus.

A novel approach is proposed to achieve simultaneous localization and mapping (SLAM) based on the signal-to-noise ratio (SNR) of global navigation satellite system (GNSS) signals. It is assumed that the environment is unknown and that the receiver location measurements (provided by a GNSS receiver) are noisy. The 3D environment map is decomposed into a grid of binary-state cells (occupancy grid) and the receiver locations are approximated by sets of particles. Using a large number of sparsely sampled GNSS SNR measurements and receiver/satellite coordinates (all available from off-the-shelf GNSS receivers), likelihoods of blockage are associated with every receiver-to-satellite beam. The posterior distribution of the map and poses is shown to represent a factor graph, on which Loopy Belief Propagation is used to efficiently estimate the probabilities of each cell being occupied or empty, along with the probability of the particles for each receiver location. Experimental results demonstrate our algorithm's ability to coarsely map (in three dimensions) a corner of a university campus, while also correcting for uncertainties in the location of the GNSS receiver.

Due to frequent non-line-of-sight (NLOS) signal reception, geopositioning using Global Navigation Satellite Systems (GNSS), such as GPS, is unreliable in urban environments, with errors on the order of tens of meters. This poses a major problem for mobile services that benefit from accurate urban localization, such as navigation, geofencing, and hyperlocal advertising applications. Mobile network operators also seek improvements in localization, as government regulators increase handset location accuracy requirements of enhanced 991 service (e911). In our demonstration, we will present the most recent prototype of our urban location improvement technology, called ShadowMaps, which will be shown to accurately track a mobile device in an urban environment, with up to an order of magnitude reduction in GNSS positioning error.

We propose and demonstrate a novel architecture for on-the-fly inference while collecting data from sparse sensor networks. In particular, we consider source localization using acoustic sensors dispersed over a large area, with the individual sensors located too far apart for direct connectivity. An Unmanned Aerial Vehicle (UAV) is employed for collecting sensor data, with the UAV route adaptively adjusted based on data from sensors already visited, in order to minimize the time to localize events of interest. The UAV therefore acts as a information-seeking data mule, not only providing connectivity, but also making Bayesian inferences from the data gathered in order to guide its future actions. The system we demonstrate has a modular architecture, comprising efficient algorithms for acoustic signal processing, routing the UAV to the sensors, and source localization. We report on extensive field tests which not only demonstrate the effectiveness of our general approach, but also yield specific practical insights into GPS time synchronization and localization accuracy, acoustic signal and channel characteristics, and the effects of environmental phenomena.

Many applications in computer vision rely on determining the correspondence between two images that share an overlapping region. One way to establish this correspondence is by matching binary keypoint descriptors from both images. Although, these descriptors are efficiently computed with bits produced by an arrangement of binary features (pattern), their matching performance falls short in comparison with other more elaborated descriptors such as SIFT. We present an approach based on noncooperative game theory for computing the locations of every binary feature in a pattern, improving the performance of binary-feature-based matchers. We propose a simultaneous two-player zero-sum game in which a maximizer wants to increase a payoff by selecting the possible locations for the features; a minimizer wants to decrease the payoff by selecting a pair of keypoints to confuse the maximizer; and the payoff matrix is computed from the pixel intensities across the pixel neighborhood of the keypoints. We use the best locations from the obtained maximizer's optimal policy for locating every binary feature in the pattern. Our evaluation of this approach coupled with Ferns shows an improvement in matching keypoints, in particular those with similar texture. Moreover, our approach improves the matching performance when fewer bits are required.

We propose and investigate a deterministic traveling wave model for the progress of epidemic routing in disconnected mobile ad hoc networks. In epidemic routing, broadcast or unicast is achieved by exploiting mobility: message-carrying nodes "infect" non message-carrying nodes when they come within communication range of them. Early probabilistic analyses of epidemic routing follow a "well-mixed" model which ignores the spatial distribution of the infected nodes, and hence do not provide good performance estimates unless the node density is very low. More recent work has pointed out that the infection exhibits wave-like characteristics, but does not provide a detailed model of the wave propagation. In this paper, we model message propagation using a reaction-diffusion partial differential equation that has a traveling wave solution, and show that the performance predictions made by the model closely match simulations in regimes where the well- mixed model breaks down. In particular, we show that well-mixed models are generally overly optimistic in regard to the scaling of the message delivery delay with problem parameters such as communication range, node density, and total area. In contrast to prior work, our model provides insight into the spatial distribution of the "infection," and reveals that the performance is sensitive to the geometry of the deployment region, not just its area.

The concept of distributed transmit beamforming is implicit in many key results of network information theory. However, its implementation in a wireless network involves the fundamental challenge of ensuring phase coherence of the radio frequency signals from the different transmitters in the presence of unknown phase offsets between the transmitters and unknown channel gains from the transmitters to the receiver. In this paper, it is shown that such phase alignment can be achieved using distributed adaptation by the transmitters with minimal feedback from the receiver. Specifically, each transmitter independently makes a small random adjustment to its phase at each iteration, while the receiver broadcasts a single bit of feedback, indicating whether the signal-to-noise ratio (SNR) improved or worsened after the current iteration. The transmitters keep the ¿good¿ phase adjustments and discard the ¿bad¿ ones, thus implementing a distributed ascent algorithm. It is shown that, for a broad class of distributions for the random phase adjustments, this procedure leads to asymptotic phase coherence with probability one. A simple analytical model, borrowing ideas from statistical mechanics, is used to characterize the progress of the algorithm, and to provide guidance on parameter choices. This analytical model is based on a conjecture on the distribution of the received phases when the number of transmitters becomes large. Finally, the proposed system is shown to be scalable: the random phase perturbations can be chosen such that the convergence time is linear in the number of collaborating nodes.

We study the problem of estimating vector-valued variables from noisy "relative" measurements. This problem arises in several sensor network applications. The measurement model can be expressed in terms of a graph, whose nodes correspond to the variables and edges to noisy measurements of the difference between two variables. We take an arbitrary variable as the reference and consider the optimal (minimum variance) linear unbiased estimate of the remaining variables. We investigate how the error in the optimal linear unbiased estimate of a node variable grows with the distance of the node to the reference node. We establish a classification of graphs, namely, dense or sparse in Rd,1<= d <=3, that determines how the linear unbiased optimal estimation error of a node grows with its distance from the reference node. In particular, if a graph is dense in 1,2, or 3D, then a node variable's estimation error is upper bounded by a linear, logarithmic, or bounded function of distance from the reference, respectively. Corresponding lower bounds are obtained if the graph is sparse in 1, 2 and 3D. Our results also show that naive measures of graph density, such as node degree, are inadequate predictors of the estimation error. Being true for the optimal linear unbiased estimate, these scaling laws determine algorithm-independent limits on the estimation accuracy achievable in large graphs.

In this paper, we address discrete-time pursuit-evasion games in the plane where every player has identical sensing and motion ranges restricted to closed disks of given sensing and stepping radii. A single evader is initially located inside a bounded subset of the environment and does not move until detected. We propose asweep-pursuit-capturepursuer strategy to capture the evader and apply it to two variants of the game. The first involves a single pursuer and an evader in a bounded convex environment, and the second involves multiple pursuers and an evader in a boundaryless environment. In the first game, we give a sufficient condition on the ratio of sensing to stepping radius of the players that guarantees capture. In the second, we determine the minimum probability of capture, which is a function of a novel pursuer formation and independent of the initial evader location. The sweep and pursuit phases reduce both games to previously studied problems with unlimited range sensing, and capture is achieved using available strategies. We obtain novel upper bounds on the capture time and present simulation studies that address the performance of the strategies under sensing errors, different ratios of sensing to stepping radius, greater evader speed, and a different number of pursuers.

This paper considers the problem of model based control of the joints of a snake robot without wheels. The potential range of applications for snake robots are numerous, and delicate operations such as inspection and maintenance in industrial environments or performing search and rescue operations require precise control of a snake robot joints. To this end we present a controller that asymptotically stabilizes the joints of the snake robot to a desired reference trajectory. The controller is based on input-output linearization of a control plant model of the snake robot dynamics also developed in this paper. In addition, we provide a formal Lyapunov-based proof of the closed-loop stability, together with simulation results for a smooth dynamical model. Finally, the performance of the controller is tested on a non-smooth snake robot model with set-valued Coulomb friction that offers an accurate description of the stick-slip transitions during locomotion.

Networked control systems (NCSs) are spatially distributed systems for which the communication between sensors, actuators, and controllers is supported by a shared communication network. We review several recent results on estimation, analysis, and controller synthesis for NCSs. The results surveyed address channel limitations in terms of packet-rates, sampling, network delay, and packet dropouts. The results are presented in a tutorial fashion, comparing alternative methodologies.

We introduce the game-theoretic stochastic routing (GTSR) framework, a proactive alternative to today's reactive approaches to route repair. GTSR minimizes the impact of link and router failure by 1) computing multiple paths between source and destination and 2) selecting among these paths randomly to forward packets. Besides improving fault tolerance, the fact that GTSR makes packets take random paths from source to destination also improves security. In particular, it makes connection eavesdropping attacks maximally difficult as the attacker would have to listen on all possible routes. The approaches developed are suitable for network layer routing, as well as for application layer overlay routing and multipath transport protocols such as the stream control transmission protocol (SCTP). Through simulations, we validate our theoretical results and show how the resulting routing algorithms perform in terms of the security/fault-tolerant/delay/throughput trade-off. We also show that a beneficial side effect of these algorithms is an increase in throughput, as they make use of multiple paths.

This paper describes a technique to reduce the computational complexity of max-flow routing, based on a hierarchical decomposition of the network. The computational complexity of this hierarchical max-flow routing is comparable to that of Dijkstra's and Bellman-Ford's algorithms. It is shown that in many of today's networks, this hierarchical approach provides nearly the same performance as flat (i.e., non-hierarchical) routing, with significantly less computation.

Recent work has shown that large gains in communication capacity are achievable by distributed beamforming in sensor networks. The principal challenge in realizing these gains in practice, is in synchronizing the carrier signal of individual sensors in such a way that they combine coherently at the intended receiver. In this paper, we provide a scalable mechanism for achieving phase synchronization in completely distributed fashion, based only on feedback regarding the power of the net received signal. Insight into the workings of the protocol is obtained from a simple theoretical model that provides accurate performance estimates

Most standard implementations of TCP perform poorly when packets are reordered. In this paper, we propose a new version of TCP that maintains high throughput when reordering occurs and yet, when packet reordering does not occur is friendly to other versions of TCP. The proposed TCP variant, or TCP-PR, does not rely on duplicate acknowledgments to detect a packet loss. Instead, timers are maintained to keep track of how long ago a packet was transmitted. In case the corresponding acknowledgment has not yet arrived and the elapsed time since the packet was sent is larger than a given threshold, the packet is assumed lost. Because TCP-PR does not rely on duplicate acknowledgments, packet reordering (including out-of-order acknowledgments) has no effect on TCP-PR performance. Through extensive simulations, we show that TCP-PR performs consistently better than existing mechanisms that try to make TCP more robust to packet reordering. When the case that packets are not reordered, we verify that TCP-PR maintains the same throughput as typical implementations of TCP (specifically, TCP-SACK) and shares network resources fairly.

In this paper we present a general hybrid systems modeling framework to describe the flow of traffic in communication networks. To characterize network behavior, these models use averaging to continuously approximate discrete variables such as congestion window and queue size. Because averaging occurs over short time intervals, one still models discrete events such as the occurrence of a drop and the consequent reaction (e.g., congestion control). The proposed hybrid systems modeling framework fills the gap between packet-level and fluid-based models: by averaging discrete variables over a very short time scale (on the order of a round-trip time), our models are able to capture the dynamics of transient phenomena fairly accurately. This provides significant flexibility in modeling various congestion control mechanisms, different queuing policies, multicast transmission, etc. We validate our hybrid modeling methodology by comparing simulations of the hybrid models against packet-level simulations. We find that the probability density functions produced by ns-2 and our hybrid model match very closely with an L1-distance of less than 1%. We also present complexity analysis of ns-2 and the hybrid model. These tests indicate that hybrid models are considerably faster.

Shortest path routing leaves connections at risk of interception and eavesdropping since the path over which data packets travel is fairly predictable and easy to determine. To improve routing security, we propose a proactive mechanism, which we call secure stochastic routing, that explores the existence of multiple routes and forces packets to take alternative paths probabilistically. We investigate game theoretic techniques to develop routing policies which make interception and eavesdropping maximally difficult. Through simulations, we validate our theoretical results and show how the resulting routing algorithms perform in terms of the security/delay/throughput trade-off. We observe that a beneficial side-effect of these algorithms is an increase in throughput, as they make use of multiple paths. The Internet was designed to use redundancy to enhance reliability. We suggest that, through stochastic methods, redundancy be used to increase security.

Most of the work in robotic manipulation and visual servoing has emphasized how to specify and perform particular tasks. Recent results have formally shown what tasks are possible with uncalibrated imaging systems. This paper extends those results by characterizing in a constructive manner the set of tasks which can be performed with different types of uncalibrated camera models. The tasks resulting structure provides a principle foundation both for a specification language and for automatic execution monitoring in uncalibrated environments.

Over the last several years, there has been an increasing appreciation of the impact of control architecture on the accuracy of visual servoing systems. In particular, it is generally acknowledged that so-called image-based methods provide the highest guarantees of accuracy on inaccurately calibrated hand-eye systems. Less clear is the impact of the control architecture on the set of tasks which the system can perform. In this article, we present a formal analysis of control architectures for hand-eye coordination. Specifically, we first state a formal characterization of what makes a task performable under three possible encoding methods. Then, for the specific case of cameras modeled using projective geometry, we relate this characterization to notions of projective invariance and demonstrate the limits of achievable performance in this regard.

We develop a face recognition algorithm which is insensitive to large variation in lighting direction and facial expression. Taking a pattern classification approach, we consider each pixel in an image as a coordinate in a high-dimensional space. We take advantage of the observation that the images of a particular face, under varying illumination but fixed pose, lie in a 3D linear subspace of the high dimensional image space-if the face is a Lambertian surface without shadowing. However, since faces are not truly Lambertian surfaces and do indeed produce self-shadowing, images will deviate from this linear subspace. Rather than explicitly modeling this deviation, we linearly project the image into a subspace in a manner which discounts those regions of the face with large deviation. Our projection method is based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variation in lighting and facial expressions. The eigenface technique, another method based on linearly projecting the image space to a low dimensional subspace, has similar computational requirements. Yet, extensive experimental results demonstrate that the proposed "Fisherface" method has error rates that are lower than those of the eigenface technique for tests on the Harvard and Yale face databases.