Abstract — In this work we consider the problem of pursuit evasion games (PEGs) where a group of pursuers is required to detect, chase and capture a group of evaders with the aid of a sensor network in minimum time. Differently from standards PEGs where the environment and the location of evaders is unknown and a probabilistic map is built based on the pursuer onboard sensors, here we consider a scenario where a sensor network, previously deployed in the region of concern, can detect the presence of moving vehicles and can relay this information to the pursuers. Here we propose a general framework for the design of a hierarchical control architecture that exploit the advantages of a sensor networks by combining both centralized and decentralized real-time control algorithms. We also propose a coordination scheme for the pursuers to minimize the time-to-capture of all evaders. In particular, we focus on PEGs with sensor networks orbiting in space for artificial space debris detection and removal. Index Terms — Sensor networks, pursuit evasion games, vehicle coordination, space vehicles, space debris over the area of interest. This constraint makes designing a cooperative pursuit algorithm harder because lack of complete observability only allows for suboptimal pursuit policies. See Figure 1(left). Furthermore, a smart evaders makes the map-building process dynamic since their location changes over time. The map-learning phase is, by itself, time-consuming and computationally intensive even for simple two-dimensional rectilinear environments [5]. Moreover, inaccurate sensors complicate this process and a probabilistic approach is often required [21]. I.

This paper considers the problem of pursuit evasion games (PEGs), where a group of pursuers is required to chase and capture a group of evaders in minimum time with the aid of a sensor network. We assume that a sensor network is previously deployed and provides global observability of the surveillance region, allowing an optimal pursuit policy. While sensor networks provide global observability, they cannot provide high quality measurements in a timely manner due to packet losses, communication delays, and false detections. This has been the main challenge in developing a real-time control system using sensor networks. We address this challenge by developing a real-time hierarchical control system which decouples the estimation of evader states from the control of pursuers via multiple layers of data fusion. While a sensor network generates noisy, inconsistent, and bursty measurements, the multiple layers of data fusion convert them into consistent and high quality measurements and forward them to the controllers of pursuers in a timely manner. For this control system, three new algorithms are developed: multi-sensor fusion, multi-target tracking and multi-agent coordination algorithms. The multi-sensor fusion algorithm converts correlated sensor measurements into position estimates, the multi-target tracking algorithm tracks an unknown number of targets, and the multi-agent coordination algorithm coordinates pursuers to capture all evaders in minimum time using a robust minimum-time feedback controller. The combined system is evaluated in simulation and tested in a sensor network deployment. To our knowledge, this paper presents the first demonstration of multi-target tracking using a sensor network without relying on classification.

research in sensor networks has made it possible to deploy networks of sensors with significant local processing. These sensor networks are revolutionising information collection and processing in many different environments. Often the amount of local data produced by these devices, and their sheer number, makes centralised data processing infeasible. Smart camera networks represent a particular challenge in this regard, partly because of the amount of data produced by each camera, but also because many high level vision algorithms require data from more than one camera. Many distributed algorithms exist that work locally to produce results from a collection of nodes, but as this number grows the algorithm’s performance is quickly crippled by the resulting exponential increase in communication overhead. This thesis examines the limits this puts on peer-to-peer cooperation between nodes, and demonstrates how for large networks these can only be circumvented by locally formed organisations of nodes. A local group forming protocol is described that provides a method for nodes to create a bottom-up organisation based purely on local conditions. This allows the formation of a dynamic information network of cooperating

We study the relationship between mobility and localization in the context of wireless sensor networks. We observe that mobility can aid sensor nodes in determining the distance to neighboring nodes without requiring explicit coordination between the mobile object and the sensor network. Distance estimation between nodes is important for many existing localization algorithms and, in this paper, we describe a pair of algorithms with low time and message complexity for estimating these distances. This approach is distinct from prior approaches to estimating inter-node distances in that the nodes do not estimate this distance by directly measuring received signal strength or time of flight from another node. Similarly, we do not require that nodes can range, at three or more distinct times, a mobile beacon that knows and communicates its own position. Instead, our approach is based entirely on the nodes estimating the range to a mobile object in a coordinated fashion and then combining these range estimates to yield the distance estimate between all pairs of observing nodes. We also provide a mechanism for determining the quality of the estimated distances, allowing nodes to reject measurements that would likely result in poor distance estimates. The proposed algorithm is distributed and scalable since all computations and communications are purely local. We validate our approach through both simulated and empirical results. 1.

and the optimum value of max[P (0)] was max[P (0)] = 00:40844 < 0 which indicates that this system has no robustly unobservable states. For the optimal value of given above, a plot of max[P (t)] as a function of t is shown in Fig. 6.

Multiple-target tracking is a representative real-time application of sensor networks as it exhibits different aspects of sensor networks such as event detection, sensor information fusion, multi-hop communication, sensor management and real-time decision making. The task of tracking multiple objects in a sensor network is challenging due to constraints on a sensor node such as short communication and sensing ranges, a limited amount of memory and limited computational power. In addition, since a sensor network surveillance system needs to operate autonomously without human operators, it requires an autonomous real-time tracking algorithm which can track an unknown number of targets. In this paper, we develop a scalable real-time multiple-target tracking algorithm that is autonomous and robust against transmission failures, communication delays and sensor localization error. In particular, there is no performance loss up to the average localization error of.7 times the separation between sensors and the algorithm tolerates up to 50 % lost-to-total packet ratio and 90 % delayed-to-total packet ratio.

Abstract — The problem of using wireless sensor networks technology for estimation and control of dynamical systems has recently received widespread attention within the scientific community. Classical control theory is in general insufficient to model distributed control problems where issues of communication delay, jitter, and time synchronization between components cannot be ignored. The purpose of this paper is to extend our work on discrete time Kalman filtering with intermittent observations [1] that was motivated by data losses in a communication channel. Accordingly, we consider the Linear Gaussian Quadratic (LQG) optimal control problem in the discrete time setting, showing that the separation principle holds in the presence of data losses. Then, using our previous results, we show the existence of a critical arrival probability below which the resulting optimal controller fails to stabilize the system. This is done by providing analytic upper and lower bounds on the cost functional, and stochastically characterizing their convergence properties as k → ∞. I.

Abstract: Wireless sensor/actuator networks (WSANs) are emerging as a new generation of sensor networks. Serving as the backbone of control applications, WSANs will enable an unprecedented degree of distributed and mobile control. However, the unreliability of wireless communications and the real-time requirements of control applications raise great challenges for WSAN design. With emphasis on the reliability issue, this paper presents an application-level design methodology for WSANs in mobile control applications. The solution is generic in that it is independent of the underlying platforms, environment, control system models, and controller design. To capture the link quality characteristics in terms of packet loss rate, experiments are conducted on a real WSAN system. From the experimental observations, a simple yet efficient method is proposed to deal with unpredictable packet loss on actuator nodes. Trace-based simulations give promising results, which demonstrate the effectiveness of the proposed approach.

Abstract — Event-triggered and self-triggered control have been recently proposed as an alternative to the more traditional periodic execution of control tasks. The possibility of reducing the number of executions while guaranteeing desired levels of performance makes event-triggered and self-triggered control very appealing in the context of sensor/actuator networks. In this setting, reducing the number of times that a feedback control law is executed implies a reduction in transmissions and thus a reduction in energy expenditures. In this paper we introduce two novel distributed implementations of event-triggered and self-triggered policies over sensor/actuator networks and discuss their performance in terms of energy expenditure. I.

Abstract — The paper considers the Linear Quadratic Gaussian (LQG) optimal control problem in the discrete time setting and when data loss may occur between the sensors and the estimation-control unit and between the latter and the actuation points. We consider the case where the arrival of the control packet is acknowledged at the receiving actuator, as it happens with the common Transfer Control Protocol (TCP). We start by showing that the separation principle holds. Additionally, we can prove that the optimal LQG control is a linear function of the state. Finally, building upon our previous results on estimation with unreliable communication, the paper shows the existence of critical arrival probabilities below which the optimal controller fails to stabilize the system. This is done by providing analytic upper and and lower bounds on the cost functional. I.