Design of consensus and adaptive consensus filters for distributed parameter systems

This work establishes an abstract framework that considers the distributed filtering of spatially varying processes using a sensor network. It is assumed that the sensor network consists of groups of sensors, each of which provides a number of state measurements from sensing devices that are not necessarily identical and which only transmit their information to their own sensor group. A modification to the local spatially distributed filters provides the non-adaptive case of spatially distributed consensus filters which penalize the disagreement amongst themselves in a dynamic manner. A subsequent modification to this scheme incorporates the adaptation of the consensus gains in the disagreement terms of all local filters. Both the well-posedness of these two consensus spatially distributed filters and the convergence of the associated observation errors to zero in appropriate norms are presented. Their performance is demonstrated on three different examples of a diffusion partial differential equation with point measurements.     

Intelligent and Efficient Strategy for Unstructured Environment Sensing Using Mobile Robot Agents

In field environments it is not usually possible to provide robots in advance with valid geometric models of its task and environment. The robot or robot teams need to create these models by scanning the environment with its sensors. Here, an information-based iterative algorithm to plan the robot's visual exploration strategy is proposed to enable it to most efficiently build 3D models of its environment and task. The method assumes mobile robot (or vehicle) with vision sensors mounted at a manipulator end-effector (eye-in-hand system). This algorithm efficiently repositions the systems' sensing agents using an information theoretic approach and fuses sensory information using physical models to yield a geometrically consistent environment map. This is achieved by utilizing a metric derived from Shannon's information theory to determine optimal sensing poses for the agent(s) mapping a highly unstructured environment. This map is then distributed among the agents using an information-based relevant data reduction scheme. This method is particularly well suited to unstructured environments, where sensor uncertainty is significant. Issues addressed include model-based multiple sensor data fusion, and uncertainty and vehicle suspension motion compensation. Simulation results show the effectiveness of this algorithm.     

Decentralized Environmental Modeling by Mobile Sensor Networks

Cooperating mobile sensors can be used to model environmental functions such as the temperature or salinity of a region of ocean. In this paper, we adopt an optimal filtering approach to fusing local sensor data into a global model of the environment. Our approach is based on the use of proportional-integral (PI) average consensus estimators, whereby information from each mobile sensor diffuses through the communication network. As a result, this approach is scalable and fully decentralized, and allows changing network topologies and anonymous agents to be added and subtracted at any time. We also derive control laws for mobile sensors to move to maximize their sensory information relative to current uncertainties in the model. The approach is demonstrated by simulations including modeling ocean temperature.     

Entropy-Based Markov Chains for Multisensor Fusion

This paper proposes an entropy based Markov chain (EMC) fusion technique and demonstrates its applications in multisensor fusion. Self-entropy and conditional entropy, which measure how uncertain a sensor is about its own observation and joint observations respectively, are adopted. We use Markov chain as an observation combination process because of two major reasons: (a) the consensus output is a linear combination of the weighted local observations; and (b) the weight is the transition probability assigned by one sensor to another sensor. Experimental results show that the proposed approach can reduce the measurement uncertainty by aggregating multiple observations. The major benefits of this approach are: (a) single observation distributions and joint observation distributions between any two sensors are represented in polynomial form; (b) the consensus output is the linear combination of the weighted observations; and (c) the approach suppresses noisy and unreliable observations in the combination process.     

Cooperative multi-robot systems:: A study of vision-based 3-D mapping using information theory

Building cooperatively 3-D maps of unknown environments is one of the application fields of multi-robot systems. This article addresses that problem through a probabilistic approach based on information theory. A distributed cooperative architecture model is formulated whereby robots exhibit cooperation through efficient information sharing. A probabilistic model of a 3-D map and a statistical sensor model are used to update the map upon range measurements, with an explicit representation of uncertainty through the definition of the map’s entropy. Each robot is able to build a 3-D map upon measurements from its own range sensor and is committed to cooperate with other robots by sharing useful measurements. An entropy-based measure of information utility is used to define a cooperation strategy for sharing useful information, without overwhelming communication resources with redundant or unnecessary information. Each robot reduces the map’s uncertainty by exploring maximum information viewpoints, by using its current map to drive its sensor to frontier regions having maximum entropy gradient. The proposed framework is validated through experiments with mobile robots equipped with stereo-vision sensors.     

An agent-based approach to multisensor coordination

This paper presents an automated system for multiple sensor placement based on the coordinated decisions of independent, intelligent agents. The problem domain is such that a single sensor system would not provide adequate information for a given sensor task. Hence, it is necessary to incorporate multiple sensors in order to obtain complete information. The overall goal of the system is to provide the surface coverage necessary to perform feature inspection on one or more target objects in a cluttered scene. This is accomplished by a group of cooperating intelligent sensors. In this system, the sensors are mobile, the target objects are stationary and each agent controls the position of a sensor and has the ability to communicate with other agents in the environment. By communicating desires and intentions, each agent develops a mental model of the other agents' preferences, which is used to avoid or resolve conflict situations. In this paper we utilize cameras as the sensors. The experimental results illustrate the feasibility of the autonomous deployment of the sensors and that this deployment can occur with sufficient accuracy as to allow the inspection task to be performed.     

Complementing visual tracking of moving targets by fusion of tactile sensing

Robot control in uncertain and dynamic environments can be greatly improved using sensor-based control. Vision is a versatile low-cost sensory modality, but low sample rate, high sensor delay and uncertain measurements limit its usability, especially in strongly dynamic environments. Vision can be used to estimate a 6-DOF pose of an object by model-based pose-estimation methods, but the estimate is typically not accurate along all degrees of freedom. Force is a complementary sensory modality allowing accurate measurements of local object shape when a tooltip is in contact with the object. In multimodal sensor fusion, several sensors measuring different modalities are combined together to give a more accurate estimate of the environment. As force and vision are fundamentally different sensory modalities not sharing a common representation, combining the information from these sensors is not straightforward. We show that the fusion of tactile and visual measurements enables to estimate the pose of a moving target at high rate and accuracy. Making assumptions of the object shape and carefully modeling the uncertainties of the sensors, the measurements can be fused together in an extended Kalman filter. Experimental results show greatly improved pose estimates with the proposed sensor fusion.     

一种基于模糊理论的一致性数据融合方法

在多传感器检测系统中,每个传感器只能得到环境的部分信息。为作出合理的决策,并识别出有错误的传感器,有必要将传感器的局部观测值组合成全局估计。利用模糊集合理论中的隶属函数获得各传感器的局部决策,并采用“决策距离”的概念对各个传感器的一致性进行检验     

Optimal sensor scheduling for resource-constrained localization of mobile robot formations

This paper addresses the problem of resource allocation in formations of mobile robots localizing as a group. Each robot receives measurements from various sensors that provide relative (robot-to-robot) and absolute positioning information. Constraints on the sensors' bandwidth, as well as communication and processing requirements, limit the number of measurements that are available or can be processed at each time step. The localization uncertainty of the group, determined by the covariance matrix of the equivalent continuous-time system at steady state, is expressed as a function of the sensor measurements' frequencies. The trace of the weighted covariance matrix is selected as the optimization criterion, under linear constraints on the measuring frequency of each sensor and the cumulative rate of the extended Kalman filter updates. This formulation leads to a convex optimization problem (semidefinite program) whose solution provides the sensing frequencies, for each sensor on every robot, required in order to maximize the positioning accuracy of the group. Simulation and experimental results are presented that demonstrate the applicability of this method and provide insight into the properties of the resource-constrained cooperative localization problem.     

Event-triggered robust distributed state estimation for sensor networks with state-dependent noises

This paper is concerned with the event-triggered distributed state estimation problem for a class of uncertain stochastic systems with state-dependent noises and randomly occurring uncertainties over sensor networks. An event-triggered communication scheme is proposed in order to determine whether the measurements on each sensor should be transmitted to the estimators or not. The norm-bounded uncertainty enters into the system in a random way. Through available output measurements from not only the individual sensor but also its neighbouring sensors, a sufficient condition is established for the desired distributed estimator to ensure that the estimation error dynamics are exponentially mean-square stable. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities, and then the explicit expression is given for the distributed estimator gains. Finally, a simulation example is provided to show the effectiveness of the proposed event-triggered distributed state estimation scheme.     

Coordinated dispatching of proximity sensors for the surveillance of manoeuvring targets

The surveillance of a manoeuvring target with multiple sensors in a coordinated manner requires a method for selecting and positioning groups of sensors in real time. Herein, the principles of dispatching, as used for the effective operation of service vehicles, are considered. The object trajectory is first discretized into a number of demand instants (data acquisition times), to which groups of sensors are assigned, respectively. Heuristic rules are used to determine the composition of each sensor group by evaluating the potential contribution of each sensor. In the case of dynamic sensors, the position of each sensor with respect to the target is also specified. Our proposed approach aims to improve the quality of the surveillance data in three ways: (1) The assigned sensors are manoeuvred into “optimal” sensing positions, (2) the uncertainty of the measured data is mitigated through sensor fusion, and (3) the poses of the unassigned sensors are adjusted to ensure that the surveillance system can react to future object manoeuvres. If a priori target trajectory information is available, the system performance may be further improved by optimizing the initial pose of each sensor off-line. The advantages of dispatching dynamic sensors over similar static-sensor systems are demonstrated through comprehensive simulations.     

Distributed consensus estimation for diffusion systems with missing measurements over sensor networks

In this paper, the problem of distributed consensus estimation with randomly missing measurements is investigated for a diffusion system over the sensor network. A random variable, the probability of which is known a priori, is used to model the randomly missing phenomena for each sensor. The aim of the addressed estimation problem is to design distributed consensus estimators depending on the neighbouring information such that, for all random measurement missing, the estimation error systems are guaranteed to be globally asymptotically stable in the mean square. By using Lyapunov functional method and the stochastic analysis approach, the sufficient conditions are derived for the convergence of the estimation error systems. Finally, a numerical example is given to demonstrate the effectiveness of the proposed distributed consensus estimator design scheme.     

Distributed Consensus Filtering in Sensor Networks

In this paper, a new filtering problem for sensor networks is investigated. A new type of distributed consensus filters is designed, where each sensor can communicate with the neighboring sensors, and filtering can be performed in a distributed way. In the pinning control approach, only a small fraction of sensors need to measure the target information, with which the whole network can be controlled. Furthermore, pinning observers are designed in the case that the sensor can only observe partial target information. Simulation results are given to verify the designed distributed consensus filters.     

Modeling unknown environments with a mobile robot

The exploration of an unknown environment is an important task for the new generation of mobile service robots. These robots are supposed to operate in dynamic and changing environments together with human beings and other static or moving objects. Sensors that are capable of providing the quality of information that is required for the described scenario are optical sensors like digital cameras and laserscanners. In this paper sensor integration and fusion for such sensors is described. Complementary sensor information is transformed into a common representation in order to achieve a cooperating sensor system. Sensor fusion is performed by matching the local perception of a laserscanner and a camera system with a global model that is being built up incrementally. The Mahalanobis-distance is used as matching criterion and a Kalman-filter is used to fuse matching features. A common representation including the uncertainty and the confidence is used for all scene features. The system's performance is demonstrated for the task of exploring an unknown environment and incrementally building up a geometrical model of it.     

Evidential fusion of sensor data for activity recognition in smart homes

Advances in technology have provided the ability to equip the home environment with a layer of technology to provide a truly ‘Smart Home’. These homes offer improved living conditions and levels of independence for the population who require support with both physical and cognitive functions. At the core of the Smart Home is a collection of sensing technology which is used to monitor the behaviour of the inhabitant and their interactions with the environment. A variety of different sensors measuring light, sound, contact and motion provide sufficient multi-dimensional information about the inhabitant to support the inference of activity determination. A problem which impinges upon the success of any information analysis is the fact that sensors may not always provide reliable information due to either faults, operational tolerance levels or corrupted data. In this paper we address the fusion process of contextual information derived from uncertain sensor data. Based on a series of information handling techniques, most notably the Dempster–Shafer theory of evidence and the Equally Weighted Sum operator, evidential contextual information is represented, analysed and merged to achieve a consensus in automatically inferring activities of daily living for inhabitants in Smart Homes. Within the paper we introduce the framework within which uncertainty can be managed and demonstrate the effects that the number of sensors in conjunction with the reliability level of each sensor can have on the overall decision making process.     

FUZZY REASONING IN A MULTIAGENT SYSTEM OF SURVEILLANCE SENSORS TO MANAGE COOPERATIVELY THE SENSOR-TO-TASK ASSIGNMENT PROBLEM

In this work, a surveillance network composed of a set of sensors and a fusion center is designed as a multiagent system. Negotiation among sensors (agents) is proposed to solve the task-to-sensor assignment problem (the allocation of tasks to sensors), addressing several aspects. First, the fusion center determines the tasks (system tasks) to be performed by the network at each management cycle. To do that, a fuzzy reasoning system determines the priorities of these system tasks by means of a symbolic inference process using the fused data received from all sensors. In addition, a fuzzy reasoning process, similar to that performed in the fusion center, is proposed to evaluate the priority of local tasks (sensor tasks) now executed by each sensor. The network coordination procedure will be based on the system-task priorities, computed in the fusion center, and on the local priorities evaluated in each sensor. Priority values for system and sensor tasks will be the basis to guide a negotiation process among sensors in the multiagent system. The validity of the fuzzy reasoning approach is supported by the fact that it has been able to manage environmental situations in a similar way as experienced human operators do. Included results illustrate how the negotiation scheme, based on task priority and measured through their time-variant priority, allows the adaption of sensor operation to changing situations.     

Sensor selection for parameterized random field estimation in wireless sensor networks

We consider the random field estimation problem with parametric trend in wireless sensor networks where the field can be described by unknown parameters to be estimated. Due to the limited resources, the network selects only a subset of the sensors to perform the estimation task with a desired performance under the D-optimal criterion. We propose a greedy sampling scheme to select the sensor nodes according to the information gain of the sensors. A distributed algorithm is also developed by consensus-based ...     

基于模糊理论的多传感器信息融合算法

D-S证据理论作为一种重要的不确定性推理理论,为处理传感器信息的模糊性及不确定性提供了很好的解决方法。但各个证据中的基本概率分配函数（mass函数）如何生成,仍是人们需要解决的问题。针对这一问题,提出了一种基于模糊理论中的高斯隶属度函数来得到传感器提供信息的可信度,计算了各个传感器之间的相互支持度;将各传感器的可信度和支持度转化成mass函数;利用证据理论对多传感器信息进行融合。仿真试验表明该方法能够有效提高识别的准确性和可靠性。     

随机分组策略下的分布式多智能体一致性

多智能体分布式一致性算法一般需要获得相对状态差值x_i-x_j,本文针对无法得到智能体间相对状态差值的情况,提出一种基于智能体分组,通过组间信息交换来达到智能体状态一致的算法.本文仅讨论离散情况下智能体被随机划分为两组和多组的情况.当存在两个随机分组时,每个智能体都进行状态更新,且更新量为组间的状态差值.此时,系统达到期望一致的充要条件为所给出的状态更新参数应大于1.当存在多个随机分组时,仅通过Gossip算法选中的两组智能体以这两组间的状态差值进行状态更新.在这种情况下,系统达到期望一致的充分条件为各分组概率相等,且状态更新参数大于1.最后通过计算机仿真验证了结论的正确性.     

基于不确定性度量信息融合的团队一致法研究

给出一种新的信息融合方法--基于不确定性度量的团队一致法.这个方法是基于 一个迭代的团队不确定性度量函数,它能使团队成员达到一致;该方法用于融合从多个图象传 感器得到的对同一个目标的关于目标类别不确定性信息,与其它方法相比该方法有效而简单. 在多图象目标识别中有着广泛的应用.还给出了确定权值系数的一般方法.把这种方法用于一 个现成的实例,得到的结果和多数结合算子的结果一致.