Decentralized output-feedback control of large-scale nonlinear systems with sensor noise

This paper presents a new tool for decentralized output-feedback control design of large-scale nonlinear systems in the presence of non-smooth sensor noise. Through a recursive control design approach, the closed-loop decentralized system is transformed into a network of input-to-state stable (ISS) systems and the influences of the sensor noise are represented by ISS gains. The decentralized control objective is achieved by applying the cyclic-small-gain theorem to the closed-loop decentralized system. Moreover, the outputs of the closed-loop decentralized system can be driven arbitrarily close to the levels of their corresponding sensor noise.     

A fuzzy-Bayesian approach to target recognition based on multisensor fusion

The Bayesian approach is widely used in automatic target recognition (ATR) systems based on multisensor fusion technology. Problems in data fusion systems are complex by nature and can often be characterized by not only randomness but also fuzziness. However, in general, current Bayesian methods can only account for randomness. To accommodate complex natural problems with both types of uncertainties, it is profitable to improve the existing approach by incorporating fuzzy theory into classical techniques. In this paper, after representing both the individual attribute of the target in the model database and the sensor observation or report as the fuzzy membership function, a likelihood function is constructed to deal with fuzzy data collected by each sensor. A similarity measure is introduced to determine the agreement degree of each sensor. Based on the similarity measure, a consensus fusion approach (CFA) is developed to generate a global likelihood from the individual attribute likelihood for the whole sensor reports. A numerical example is illustrated to show the target recognition application of the fuzzy-Bayesian approach. The text was submitted by the authors in English.     

基于CPCRLB和一致性算法的分散式传感器管理

针对无线传感器网络中的目标跟踪问题,基于条件后验克拉美—罗下界（CPCRLB）提出一种分散式传感器节点管理方法．基于一致性策略给出一种CPCRLB的分布式迭代算法,并且基于分布式粒子滤波器给出该算法的数值逼近实现．对层次结构的无线传感器网络,将CPCRLB作为传感器管理的准则,基于平均一致性给出一种迭代的局部搜索算法,实现了无线传感器网络下观测节点的分散式在线选择．仿真结果表明了基于CPCRLB的分散式传感器管理方法在目标跟踪精度方面的有效性．     

Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors

This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.     

Asynchronous broadcast-based decentralized learning in sensor networks

Abstract

In this paper, we study the problem of decentralized learning in sensor networks in which local learners estimate and reach consensus to the quantity of interest inferred globally while communicating only with their immediate neighbours. The main challenge lies in reducing the communication cost in the network, which involves inter-node synchronisation and data exchange. To address this issue, a novel asynchronous broadcast-based decentralized learning algorithm is proposed. Furthermore, we prove that the iterates generated by the developed decentralized method converge to a consensual optimal solution (model). Numerical results demonstrate that it is a promising approach for decentralized learning in sensor networks.     

Online monitoring and control of a cyber-physical manufacturing process under uncertainty

Recent technological advancements in computing, sensing and communication have led to the development of cyber-physical manufacturing processes, where a computing subsystem monitors the manufacturing process performance in real-time by analyzing sensor data and implements the necessary control to improve the product quality. This paper develops a predictive control framework where control actions are implemented after predicting the state of the manufacturing process or product quality at a future time using process models. In a cyber-physical manufacturing process, the product quality predictions may be affected by uncertainty sources from the computing subsystem (resource and communication uncertainty), manufacturing process (input uncertainty, process variability and modeling errors), and sensors (measurement uncertainty). In addition, due to the continuous interactions between the computing subsystem and the manufacturing process, these uncertainty sources may aggregate and compound over time. In some cases, some process parameters needed for model predictions may not be precisely known and may need to be derived from real time sensor data. This paper develops a dynamic Bayesian network approach, which enables the aggregation of multiple uncertainty sources, parameter estimation and robust prediction for online control. As the number of process parameters increase, their estimation using sensor data in real-time can be computationally expensive. To facilitate real-time analysis, variance-based global sensitivity analysis is used for dimension reduction. The proposed methodology of online monitoring and control under uncertainty, and dimension reduction, are illustrated for a cyber-physical turning process.

     

Convergence-Preserving Switching for Topology-Dependent Decentralized Systems

Stability analysis of decentralized control mechanisms for networked coordinating systems has generally focused on specific controller implementations, such as nearest-neighbor and other types of proximity graph control laws. This approach often misses the need for the addition of other control structures to improve global characteristics of the network. An example of such a situation is the use of a Gabriel graph, which is essentially a nearest-neighbor rule modified to ensure global connectivity of the network if the agents are pairwise connected through their sensor inputs. We present a method of ensuring provable stability of decentralized switching systems by employing a hysteresis rule that uses a zero-sum consensus algorithm. We demonstrate the application of this result to several special cases, including nearest-neighbor control laws, Gabriel graph rules, diffuse target tracking, and hierarchical heterogeneous systems.      

Distributed and centralised material handling scheduling: Comparison and results of a simulation study

Part of a larger research that employs decentralized holonic modelling techniques in manufacturing planning and control, this work proposes a holonic-based material handling system and contrasts the centralized and distributed scheduling approaches for the allocation of material handling operations to the available system resources. To justify the use of the decentralized holonic approach and assess its performance compared to conventional scheduling systems, a series of evaluation tests and a simulation study are carried out. As illustrated by the results obtained from the simulation study, the decentralized holonic approach is capable of delivering competitive feasible solutions in, practically, real-time.     

Standalone noise and anomaly detection in wireless sensor networks: A novel time-series and adaptive Bayesian-network-based approach

Wireless sensor networks (WSNs) consist of small sensors with limited computational and communication capabilities. Reading data in WSN is not always reliable due to open environmental factors such as noise, weakly received signal strength, and intrusion attacks. The process of detecting highly noisy data is called anomaly or outlier detection. The challenging aspect of noise detection in WSN is related to the limited computational and communication capabilities of sensors. The purpose of this research is to design a local time-series-based data noise and anomaly detection approach for WSN. The proposed local outlier detection algorithm (LODA) is a decentralized noise detection algorithm that runs on each sensor node individually with three important features: reduction mechanism that eliminates the noneffective features, determination of the memory size of data histogram to accomplish the effective available memory, and classification for predicting noisy data. An adaptive Bayesian network is used as the classification algorithm for prediction and identification of outliers in each sensor node locally. Results of our approach are compared with four well-known algorithms using benchmark real-life datasets, which demonstrate that LODA can achieve higher (up to 89%) accuracy in the prediction of outliers in real sensory data.     

An adaptive information dissemination of decentralized warship cooperative engagement with constrained bandwidth based on a geodetic coordinate system

The main objective of this paper is to analyze models, techniques, algorithms and infrastructures needed to complete cooperative engagement for warships with constrained bandwidth based on the geodetic coordinate system. To increase the performance of maneuvering target tracking, the 2D and 3D target motion models and the observation model based on the geodetic coordinate system are derived. In order to cope with the target tracking and fusion problems which arise from the intense non-linearity and decentralization in a geodetic coordinate system, two decentralized fusion algorithms denoted as SR-UKF (TR) and SR-UKF (CI) which we developed before are used. Considering the limited bandwidth of the communication network in a battle environment, an adaptive selection scheme which captures the competitive and cooperative relations among the neighbor sensor nodes is developed for decentralized information dissemination. The simulation results show the efficiency, adaptivity and robustness of the proposed algorithms with high precision.     

Redundant manipulator techniques for partially decentralized path planning and control of a platoon of autonomous vehicles.

An approach to real-time trajectory generation for platoons of autonomous vehicles is developed from well-known control techniques for redundant robotic manipulators. The partially decentralized structure of this approach permits each vehicle to independently compute its trajectory in real-time using only locally generated information and low-bandwidth feedback generated by a system exogenous to the platoon. Our work is motivated by applications for which communications bandwidth is severely limited, such for platoons of autonomous underwater vehicles. The communication requirements for our trajectory generation approach are independent of the number of vehicles in the platoon, enabling platoons composed of a large number of vehicles to be coordinated despite limited communication bandwidth.     

A framework and automotive application of collision avoidance decision making

Collision avoidance (CA) systems are applicable for most transportation systems ranging from autonomous robots and vehicles to aircraft, cars and ships. A probabilistic framework is presented for designing and analyzing existing CA algorithms proposed in literature, enabling on-line computation of the risk for faulty intervention and consequence of different actions. The approach is based on Monte Carlo techniques, where sampling-resampling methods are used to convert sensor readings with stochastic errors to a Bayesian risk. The concepts are evaluated using a real-time implementation of an automotive collision mitigation system, and results from one demonstrator vehicle are presented.     

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.     

Sensor Planning for Mobile Robot Localization---A Hierarchical Approach Using a Bayesian Network and a Particle Filter

In this paper, we propose a hierarchical approach to solving sensor planning for the global localization of a mobile robot. Our system consists of two subsystems: a lower layer and a higher layer. The lower layer uses a particle filter to evaluate the posterior probability of the localization. When the particles converge into clusters, the higher layer starts particle clustering and sensor planning to generate an optimal sensing action sequence for the localization. The higher layer uses a Bayesian network for probabilistic inference. The sensor planning takes into account both localization belief and sensing cost. We conducted simulations and actual robot experiments to validate our proposed approach.     

基于贝叶斯Noisy Or Gate网络的多传感器目标分类识别

针对多传感器获取空中目标的多识别特征,提出了基于贝叶斯Noisy Or Gate网络的目标识别模型;该模型考虑未知因素的影响,将识别特征按二值节点进行网络识别结构构造,利用单个特征的识别结果,计算得到多个特征识别的任意组合,条件概率个数可以从2n减小为2n.仿真计算结果表明,该方法具有简化知识获取,节省存储空间,证据传播及时,实时性高的特点,为目标分类与识别提供了一个新的途径。     

一种优化的贝叶斯估计多传感器数据融合方法

由于来自多个传感器的测量数据总是有一定程度的不确定性和不一致性,采用多传感器数据融合算法将多个节点的测量数据进行数据融合,利用数据的冗余度来减小这种不确定性,得到高可靠性的数据信息。提出了一种优化的贝叶斯估计多传感器数据融合方法,将贝叶斯估计和卡尔曼滤波器结合起来,应用于无线传感网络数据融合中。根据滤波器应用到传感数据、融合数据或者两者的方式,提出3种不同的技术,即:前向滤波法、后向滤波法和前后向滤波法。通过一个实例研究估计移动机器人的位置,验证算法的有效性。实验表明,在集中式和分布式两个方面数据融合体系结构,结合卡尔曼滤波器的贝叶斯融合算法能够有效地解决数据的不确定性和不一致性。     

Active object tracking using context estimation: handling occlusions and detecting missing targets

When performing visual servoing or object tracking tasks, active sensor planning is essential to keep targets in sight or to relocate them when missing. In particular, when dealing with a known target missing from the sensor’s field of view, we propose using prior knowledge related to contextual information to estimate its possible location. To this end, this study proposes a Dynamic Bayesian Network that uses contextual information to effectively search for targets. Monte Carlo particle filtering is employed to approximate the posterior probability of the target’s state, from which uncertainty is defined. We define the robot’s utility function via information theoretic formalism as seeking the optimal action which reduces uncertainty of a task, prompting robot agents to investigate the location where the target most likely might exist. Using a context state model, we design the agent’s high-level decision framework using a Partially-Observable Markov Decision Process. Based on the estimated belief state of the context via sequential observations, the robot’s navigation actions are determined to conduct exploratory and detection tasks. By using this multi-modal context model, our agent can effectively handle basic dynamic events, such as obstruction of targets or their absence from the field of view. We implement and demonstrate these capabilities on a mobile robot in real-time.

     

Autonomous feature following for visual surveillance using a small unmanned aerial vehicle with gimbaled camera system

This paper represents the development of feature following control and distributed navigation algorithms for visual surveillance using a small unmanned aerial vehicle equipped with a low-cost imaging sensor unit. An efficient map-based feature generation and following control algorithm is developed to make an onboard imaging sensor to track a target. An efficient navigation system is also designed for real-time position and velocity estimates of the unmanned aircraft, which is used as inputs for the path following controller. The performance of the proposed autonomous path following capability with a stabilized gimbaled camera onboard a small unmanned aerial robot is demonstrated through flight tests with application to target tracking for real-time visual surveillance.     

Achievable closed-loop properties of systems under decentralizedcontrol: conditions involving the steady-state gain

The question of the existence of decentralized controllers for open-loop stable multivariable systems which provide particular closed-loop properties is investigated. In particular, we study the existence of decentralized controllers which provide integral action (Type I closed-loop performance) and also demonstrate one or more of: unconditional stability, integrity with respect to actuator and sensor failure, and decentralized unconditional stability. Necessary, sufficient, and, in some cases, necessary and sufficient conditions on the open-loop steady-state gain are derived such that there exists a controller which provides these desired closed-loop characteristics. These results provide the basis for a systematic approach to control structure selection for decentralized controller design     

Dynamic target tracking and observing in a mobile sensor network

This paper presents novel approaches to (1) the problem of flocking control of a mobile sensor network to track and observe a moving target and (2) the problem of sensor splitting/merging to track and observe multiple targets in a dynamic fashion. First, to deal with complex environments when the mobile sensor network has to pass through a narrow space among obstacles, we propose an adaptive flocking control algorithm in which each sensor can cooperatively learn the network’s parameters to decide the network size in a decentralized fashion so that the connectivity, tracking performance and formation can be improved. Second, for multiple dynamic target tracking, a seed growing graph partition (SGGP) algorithm is proposed to solve the splitting/merging problem. To validate the adaptive flocking control we tested it and compared it with the regular flocking control algorithm. For multiple dynamic target tracking, to demonstrate the benefit of the SGGP algorithm in terms of total energy and time consumption when sensors split, we compared it with the random selection (RS) algorithm. Several experimental tests validate our theoretical results.