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Consider an agent A at an unknown location, undergoing sufficiently slow drift, and a mobile agent B that must move to the vicinity of and then circumnavigate A at a prescribed distance from A. In doing so, B can only measure its distance from A, and knows its own position in some reference frame. This paper considers this problem, which has applications to surveillance and orbit maintenance. In many of these applications it is difficult for B to directly sense the location of A, e.g. when all that B can sense is the intensity of a signal emitted by A. This intensity does, however provide a measure of the distance. We propose a nonlinear periodic continuous time control law that achieves the objective using this distance measurement. Fundamentally, a) B must exploit its motion to estimate the location of A, and b) use its best instantaneous estimate of where A resides, to move itself to achieve the circumnavigation objective. For a) we use an open loop algorithm formulated by us in an earlier paper. The key challenge tackled in this paper is to design a control law that closes the loop by marrrying the two goals. As long as the initial estimate of the source location is not coincident with the intial position of B, the algorithm is guaranteed to be exponentially convergent when A is stationary. Under the same condition, we establish that when A drifts with a sufficiently small, unknown velocity, B globally achieves its circumnavigation objective, to within a margin proportional to the drift velocity., QC 20120508

Misbehaviors among the agents in a network might be intentional or unintentional, they might cause a system-wide failure or they might improve the performance or even enable us to achieve an objective. In this article we consider examples of these possible scenarios. First, we argue the necessity of monitoring the agents in a network to detect if they are misbehaving or not and outline a distributed method in which each agent monitors its neighbors for any sign of misbehavior. Later, we focus on solving the problem of distributed leader selection via forcing the agents to temporarily misbehave, and introduce an algorithm that enables the agents in a network to select their leader without any interference from the outside of the network., © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.QC 20120615

In this letter, we study the problem of fault detection and mitigation in networks where the measurements satisfy Kirchhoff's voltage law. First, we characterise the class of faults appearing as an additive fault vector (injected by a malicious adversary or due to equipment failures) that can be detected by taking into account the topology of the network. Second, we consider the problem of estimating the fault vector via tools from compressive sensing. Moreover, we comment on the applicability of the developed methods to the case where the measurements satisfy Kirchhoff's current law. The proposed methods are validated via numerical examples with application to time synchronization networks., QC 20121112

This paper addresses a surveillance problem in which the goal is to achieve a circular motion around a target by a non-holonomic agent. The agent only knows its own position with respect to its initial frame, and the bearing angle of the target in that frame. It is assumed that the position of the target is unknown. An estimator and a controller are proposed to estimate the position of the target and make the agent move on a circular trajectory with a desired radius around it. The performance of the proposed algorithm is verified both through simulations and experiments. Robustness is also established in the face of noise and target motion., QC 20130129Conference ISBN: 978-146731737-5

Many properties of interest in graph structures are based on the nodes' average degree (i.e., the average number of edges incident to/from each node). In this work, we present asynchronous distributed algorithms, based on ratio consensus, that can be used to accurately estimate the number of nodes in a multi-component system whose communication topology is described by a directed graph. In addition, we describe an asynchronous distributed algorithm that allows each node to introduce or terminate links in order to reach a target average degree in the network. Such an approach can be useful in many realistic scenarios; for example, for the introduction and removal of renewable energy resources in a power network, while maintaining an average degree that fulfils some structural and dynamical properties and/or optimises some performance indicators of the network. The effectiveness of the proposed algorithms is demonstrated via illustrative examples., QC 20130521

In this paper the problem of revealing stealthy data-injection attacks on control systems is addressed. In particular we consider the scenario where the attacker performs zero-dynamics attacks on the system. First, we characterize and analyze the stealthiness properties of these attacks for linear time-invariant systems. Then we tackle the problem of detecting such attacks by modifying the system's structure. Our results provide necessary and sufficient conditions that the modifications should satisfy in order to detect the zero-dynamics attacks. The results and proposed detection methods are illustrated through numerical examples., QC 20121114, HYCON2

In this paper we consider the problem of Distributed Fault Detection and Isolation (D-FDI) in large networked systems with imprecise models. Taking a previously proposed D-FDI scheme for a given initial network model, we analyze its performance under small changes in the network graph, namely the addition or removal of edges. Under some assumptions, it is shown that for this kind of perturbations there exist suitable thresholds for which fault detection and isolation is achieved. As our second contribution, we propose solutions to accomplish D-FDI with considerably lower computational burden, while handling imprecise network models. Numerical experiments demonstrating the effectiveness of the proposed solution are presented, taking the IEEE 118 bus power network as an example., QC 20121113

This paper introduces a new method for performing actuator fault detection and diagnostics (FDD) in heating ventilation and air conditioning (HVAC) systems. The proposed actuator FDD strategy, for testing whether an actuator is stuck in a single position, uses a two-tier approach that includes a dynamic model-based detector and a fast-deciding steady-state detector. The model-based detector is formulated to provide detection performance that asymptotically bounds both the probability of miss and probability of false alarm. To provide a quick confirmation the actuator is working, the steady-state detector utilizes a goodness-of-fit detection strategy to decide if the measurements could be described by an actuator failure. An architecture is introduced that requires multiple steady-state detection experiments to decide that the measurements could be explained by an actuator failure before performing model-based detection. An experimental test bed using a the KTH Royal Institute of Technology campus HVAC system is described and used to evaluate the steady-state and model-based detectors. The experimental test bed is utilized to identify a building dynamics model, that is employed through monte carlo analysis, to characterize the detection performance of both the model-based detector and the steady-state detector., QC 20130114

As a first step towards developing efficient building energy management techniques, in this paper, we first study the energy consumption patterns of heating, ventilation and cooling (HVAC) systems across the KTH Royal Institute of Technology campus and we identify some possible areas where energy consumption can be made less wasteful. Later, we describe a test-bed where wireless sensor networks are used to collect data and eventually control the HVAC system in a distributed way. We present some of the data, temperature, humidity, and CO2 measurements, that are collected by the aforementioned network and compare them with the measurements collected by the legacy sensors already in place. In the end we present a preliminary result on modelling the dynamics of the temperature, humidity, and CO2 using the data gather by the sensor network. We check the validity of the model via comparing the out put of the system with measured data. As a future work we identify the possibility of using the models obtained here for model based control, and fault detection and isolation techniques., QC 20130218

This paper addresses a surveillance problem in which the goal is to achieve a circular motion around a target by a non-holonomic agent. The agent only knows its own position with respect to its initial frame, and the bearing angle of the target in that frame. It is assumed that the position of the target is unknown. An estimator and a controller are proposed to estimate the position of the target and make the agent move on a circular trajectory with a desired radius around it. The performance of the proposed algorithm is verified both through simulations and experiments. Robustness is also established in the face of noise and target motion.

Graph theory has been used to characterize the solvability of the sensor network localization problem with ideal (i.e., precisely known) bearing-only measurements between certain pairs of sensors and a limited amount of information about the position of certain nodes, i.e., anchors. In practice, however, bearing measurements will never be exact, and the equations whose solutions deliver sensor positions in the noiseless case may no longer have a solution. This technical brief argues that if the same conditions for localizability that exist in the noiseless case are satisfied and the bearing measurement errors are small enough (as will be formalized later in the technical brief), then the network will be approximately localizable, i.e., sensor position estimates can be found which are near the correct values. In particular, a bound on the position errors is found in terms of a bound on the bearing errors. Later, this bound is used to propose a method to select anchors to minimize the effect of noisy bearing measurements on the localization solution.

Direction-based formation shape control for a collection of autonomous agents involves the design of distributed control laws that ensure the formation moves so that certain relative bearing constraints achieve, and maintain, some desired value. This paper looks at the design of a distributed control scheme to solve the direction-based formation shape control problem. A gradient control law is proposed based on the notion of bearing-only constrained graph rigidity and parallel drawings. This work provides an interesting and novel contrast to much of the existing work in formation control where distance-only constraints are typically maintained. A stability analysis is sketched and a number of illustrative examples are also given., QC 20140908

A variety of social, biological and communication networks can be modelled using graph theoretical tools. Similar graphical tools can be used to model the topology by which disease, errors, and/or other undesired phenomenon etc. is spread and propagated through such networks. Certain network operations are proposed in this work that can be used to slow the spread of diseases in complex network topologies. The approach considered in this work differs from existing techniques in that it is based on optimally removing (or immunizing) individual links in the network as opposed to individual nodes. A systematic algorithm is outlined to achieve this edgewise immunization via a relaxed convex optimization protocol., QC 20111109

This paper explores the use of accelerated gradient methods in networked optimization. Optimal algorithm parameters and associated convergence rates are derived for distributed resource allocation and consensus problems, and the practical performance of the accelerated gradient algorithms are shown to outperform alternatives in the literature. Since the optimal parameters for the accelerated gradient method depends on upper and lower bounds of the Hessian, we study how errors in these estimates influence the convergence rate of the algorithm. This analysis identifies, among other things, cases where erroneous estimates of the Hessian bounds cause the accelerated method to have slower convergence than the corresponding (non-accelerated) gradient method. An application to Internet congestion control illustrates these issues., QC 20120119

The problem of localizing or tracking a number of targets using a network of bearing-only sensors is considered. To solve such a high-level problem, each sensor report must be successfully recorded in a common spatial reference frame and the position of the sensors must be determined. In practice, however, the reports from individual sensors are characterized by both random (called noise) and systematic errors (called biases). Typical bias errors are axis misalignments (due to azimuth and elevation biases) and range offset errors. Conditions under which the systematic errors can be removed given noisy measurements are examined in this work. In addition, certain conditions are identified which lend themselves naturally to the design of algorithms for network registration, localization and subsequently target localization. These conditions are feasible from a computational complexity point of view. This work provides a comprehensive solution to the problem of sensor network-based target localization with bearing measurements as very little a prior information is assumed known and, if certain sensing conditions are met, efficient algorithms are provided., QC 20140902

We consider distributed localization in a sensor network in ℝ 2 from inter-agent distances. Sensors and anchors exchange data with their neighbors. No centralized data processing is required. We establish a differential equation for the unknown sensor positions, and show that the estimated positions of sensors converge to their actual values in finite time (assuming noise-free measurements). The key assumption is that all sensors are in the convex hull of three or more anchors. The proposed localization method uses the barycentric coordinates of each sensor with respect to some of its neighbors (which may not include those anchors), assuming the sensor falls in the convex hull of these neighbors., QC 20140903

In this paper, we consider the general problem of guaranteeing safe interaction of humans and a group of mobile agents in an environment of interest. The way that we guarantee such a safe interaction is via proposing algorithms that prevent the agents from colliding with environmental obstacles with unknown sizes and locations, e.g. human operators. To do so, we first consider the problem of forcing a set of autonomous agents form a desired formation and follow a trajectory only known to one of them, namely the leader of the formation. Then we build upon the solution given to this problem and solve another problem of interest. This is to guarantee obstacle collision avoidance while the agents are following an unknown trajectory and keeping the desired shape of the formation. The stability of the control laws presented here is analytically established and some simulation results are presented to show the performance of the proposed control laws in different scenarios., QC 20140903

This paper outlines the problem of doppler-based target position and velocity estimation using a sensor network. The minimum number of doppler shift measurements at distinct generic sensor positions to have a finite number of solutions, and later, a unique solution for the unknown target position and velocity is stated analytically, for the case when no measurement noise is present. Furthermore, we study the same problem where not only doppler shift measurements are collected, but also other types of measurements are available, e.g. bearing or distance to the target from each of the sensors. Subsequently, allowing nonzero measurement noise, we present an optimization method to estimate the position and the velocity of the target. An illustrative example is presented to show the validity of the analysis and the performance of the estimation method proposed. Some concluding remarks and future work directions are presented in the end., QC 20140903

This paper considers the problem of localizing multiple agents, e.g. unmanned aerial vehicles (UAVs), robots, etc., moving in two-dimensional space when the known data comprise 1) the inter-agent distances, and 2) the angle subtended at each agent by lines drawn from two landmarks at known positions. Later it is shown that this result has direct application in a different general robotic problem, viz. robot-to-robot relative pose determination (relative reference frame determination), using measured distances. The methods proposed are validated through simulations and experiments.

The problem of localizing or tracking a number of targets using a network of bearing-only sensors is considered. To solve such a high-level problem, each sensor report must be successfully recorded in a common spatial reference frame and the position of the sensors must be determined. In practice, however, the reports from individual sensors are characterized by both random (called noise) and systematic errors (called biases). Typical bias errors are axis misalignments (due to azimuth and elevation biases) and range offset errors. Conditions under which the systematic errors can be removed given noisy measurements are examined in this work. In addition, certain conditions are identified which lend themselves naturally to the design of algorithms for network registration, localization and subsequently target localization. These conditions are feasible from a computational complexity point of view. This work provides a comprehensive solution to the problem of sensor network-based target localization with bearing measurements as very little a prior information is assumed known and, if certain sensing conditions are met, efficient algorithms are provided.

Direction-based formation shape control for a collection of autonomous agents involves the design of distributed control laws that ensure the formation moves so that certain relative bearing constraints achieve, and maintain, some desired value. This paper looks at the design of a distributed control scheme to solve the direction-based formation shape control problem. A gradient control law is proposed based on the notion of bearing-only constrained graph rigidity and parallel drawings. This work provides an interesting and novel contrast to much of the existing work in formation control where distance-only constraints are typically maintained. A stability analysis is sketched and a number of illustrative examples are also given.

This chapter reviews a series of results obtained in the field of localization that are based on polynomial optimization. First, it provides a review of a set of polynomial function optimization tools, including sum of squares (SOS). Then the chapter presents several applications of these tools in various sensor network localization tasks. As the first application, it proposes a method based on SOS relaxation for node localization using noisy measurements and describes the solution through semidefinite programming (SDP). Later, the chapter applies this method to address the problems of target localization in the presence of noise and relative reference frame determination based on range and bearing measurements. Finally, it provides some simulation and experiment results to show the applicability of the methods.

Graph theory has been used to characterize t he solvability of the sensor network localization problem. If sensors correspond to vertices and edges correspond to sensor pairs between which the distance is known, a significant result in the theory of range-based sensor network localization is that if the graph underlying the sensor network is generically globally rigid and there is a suitable set of anchors at known positions, then the network can be localized, i.e., a unique set of sensor positions can be determined that is consistent with the data. In particular, for planar problems, provided the sensor network has three or more noncollinear anchors at known points, all sensors are located at generic points, and the intersensor distances corresponding to the graph edges are precisely known rather than being subject to measurement noise, generic global rigidity of the graph is necessary and sufficient for the network to be localizable (in the absence of any further information). In practice, however, distance measurements will never be exact, and the equations whose solutions deliver sensor positions in the noiseless case in general no longer have a solution. This paper then argues that if the distance measurement errors are not too great and otherwise the associated graph is generically globally rigid and there are three or more noncollinear anchors, the network will be approximately localizable, in the sense that estimates can be found for the sensor positions which are near the correct values; in particular, a bound on the position errors can be found in terms of a bound on the distance errors. The sensor positions in this case can be found by minimizing a cost function which, although nonconvex, does have a global minimum.

We introduce a discrete-time Markov switching system control model for a control algorithm that is executed on a device with time-varying computing resources. We outline the stability expressions for a number of linear control scenarios and we illustrate

This paper considers the problem of localization and circumnavigation of a slowly drifting target with unknown speed using the agent's known position and the bearing angle of the target. We first assume that the target is stationary and propose an estimator to localize the target and a control law that forces the agent to move on a circular trajectory around the target such that both the estimator and the control system are exponentially stable. Then the case where the target might experience slow but possibly steady movement is studied.

This manuscript considers the problem of close target reconnaissance by a group of autonomous agents. The overall close target reconnaissance (CTR) involves subtasks of avoiding inter-agent collisions, reaching a close vicinity of a specific target position, and forming an equilateral polygon formation around the target. The agents performing the task fly at a constant speed to mimic the velocity behavior of small fixed-wing unmanned aerial vehicles (UAV). A decentralized control scheme is developed for this overall task and the finite-time convergence of the system under the proposed control law is established. Furthermore, it is guaranteed that no collision occurs among the agent. The relevant analysis and simulation test results are provided.

This letter introduces a simple convex, constraint based, optimization protocol for the problem of relative clock synchronization in wireless (sensor) networks.

This paper is on obstacle avoidance of swarms of robots moving in certain geometric planar formations. Focus is given to a particular obstacle avoidance approach, which is based on the fluid mechanical principle known as the Circle Theorem. Considering the motion region as a fictitious fluid environment surrounding the obstacles, fluid streamlines are calculated which correspond to unique smooth paths that a robot or a robotic formation can follow without colliding with the obstacles. The design and analysis are initially performed assuming simple integrator dynamics for each agent, and later extended for more realistic non-holonomic unicycle dynamic agent models, with the help of proportional integral (PI) control. The fluid dynamics based designs developed for obstacle avoiding motion control of agents, moving in a prescribed rigid formation are novel, and successfully tested via an extensive set of simulations. Application of the developed designs for motion control of unmanned aerial vehicle (UAV) formations under the constraint of constant speed is also presented.

This paper considers initially the problem of localizing three agents moving in the plane when the inter-agent distances are known, and in addition, the angle subtended at each agent by lines drawn from two landmarks at known positions is also known. In addition, it is shown that there are in general a finite number greater than one of possible sets of positions for the three agents. Later, generalization of the result for more than three agents is presented.

We develop a novel localization theory for planar networks of nodes that measure each other's relative position, i.e., we assume that nodes do not have the ability to perform measurements expressed in a common reference frame. We begin with some basic definitions of frame localizability and orientation localizability. Based on some key kinematic relationships, we characterize orientation localizability for networks with angle-of-arrival sensing. We then address the orientation localization problem in the presence of noisy measurements. Our first algorithm computes a least-square estimate of the unknown node orientations in a ring network given angle-ofarrival sensing. For arbitrary connected graphs, our second algorithm exploits kinematic relationships among the orientation of node in loops in order to reduce the effect of noise. We establish the convergence of the algorithm, and through some simulations we show that the algorithm reduces the meansquare error due to the noisy measurements.

In this paper the problem of close target reconnaissance by a formation of 3 unmanned aerial vehicles (UAVs) is considered. The overall close target reconnaissance (CTR) involves subtasks of avoiding obstacles or no-fly-zones, avoiding inter-agent collisions, reaching a close vicinity of a specified target position, and forming an equilateral triangular formation around the target. The UAVs performing the task fly at constant speeds. A decentralized control scheme is developed for this overall task considering unidirectional sensing/control architecture. Relevant analysis and simulation test results are provided.

Inspired by the recent "Robot Sheepdog Project" in the literature, in this paper a new method for coordination of multi-agent systems moving in a 2-dimensional region with the obstacles along a collision free trajectory is presented. A two level decentralized control scheme is proposed. At lower level, for the agents within the formation, "the sheep", the only concern is to keep that of their formation shape intact and avoid inter-agent collision. At the higher level, an intelligent agent, "the sheepdog", is responsible for determining a collision free trajectory for both the formation and itself, and then coordinating the formation motion along this computed path sequence. This sheepdog utilizes a repulsive vector (acting as a source of repulsive force) to drive the formation along the planned path. The effectiveness of the proposed scheme is demonstrated via simulation results in the presence of multiple obstacles.