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检索条件"任意字段=Conference on Sensor Fusion and Distributed Robotic Agents"

共 371 条记录，以下是81-90 订阅

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A Mobile-Agent-Based Middleware for Wireless sensor Networks Data fusion

A Mobile-Agent-Based Middleware for Wireless Sensor Networks...

引用

26th IEEE International Instrumentation and Measurement Technology conference

作者： Zhang, Li Wang, Qiang Shu, Xijuan Harbin Inst Technol Dept Control Sci & Engn Harbin 150006 Peoples R China

ISBN: (纸本)9781424433520

Mobile agent system provides us with a new way to realize distributed collaborative information processing in sensor networks. In this paper, we present a middleware to realize mobile-agent-based data fusion on wireless sensor networks nodes, which is implemented entirely in TinyOS and has been tested on TelosB motes. It consists of two parts. One is the strategy for the serialization of algorithm in mobile agents in form of character string. The other is an interpreter written in nesC language which is used to interpret and deserialize the programmed algorithm character string to commands, and execute commands line by line. Finally, experiments are conducted in the environment temperature inspection scenario using wireless sensor networks to evaluate the performances of our mobile-agent-based middleware.

关键词： Wireless sensor networks Data fusion Mobile agent

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Fault-tolerant Bayesian Decentralized Data fusion Using Reliability Variables and Mixture Models 27

Fault-tolerant Bayesian Decentralized Data Fusion Using Reli...

引用

27th International conference on Information fusion (fusion)

作者： Shin, Changkyo Dagan, Ofer Ahmed, Nisar Choi, Han-Lim Korea Adv Inst Sci & Technol Dept Aerosp Engn Daejeon South Korea Univ Colorado Boulder Smead Aerosp Engn Sci Dept Boulder CO USA

ISBN: (纸本)9798350371420;9781737749769

In uncertain and dynamic environments, decentralized data fusion (DDF) techniques have been widely used to estimate the states and the uncertainty levels over large mission spaces in a robust and scalable way. In data fusion frameworks using distributed sensor networks, undetected sensor failures can degrade the quality of fusion results of the entire system. Therefore, DDF methods which are robust to inconsistent data are needed. In this paper, a fault-tolerant Bayesian DDF method using Gaussian mixture models is developed. The probability of agent reliability states, which represent consistency of local estimates that agents share with their neighbors, are modeled as weights of mixture models and estimated together with the target process. The target process and reliability states are updated in a decentralized Bayesian way, exploiting the properties of Gaussian mixture models. To prevent the hypothesis explosion problem of Gaussian mixture models, a mixture compression method considering the physical meaning of mixture weights is utilized. A numerical simulation on a 2D dynamic target tracking problem is presented to verify performance of the suggested algorithm and compared with existing DDF methods. It is shown that the suggested algorithm gives more compact fusion results compared to existing fault-tolerant DDF method.

关键词： State estimation distributed inference Multirobot systems distributed sensor network Fault-tolerant Bayesian DDF

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Intelligent processing research for target fusion recognition system based on multi-agents

Intelligent processing research for target fusion recognitio...

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International conference on Computational Intelligence and Software Engineering

作者： Wu, Xia Zhou, Yan Department of Early Warning and Surveillance Intelligence Air Force Radar Academy Wuhan China

ISBN: (纸本)9781424453924

According to the target fusion recognition under dynamic and uncertain target information conditions, a distributed target fusion recognition model is presented based on multi-agents. As a distributed multi-agents target recognition system, the model includes several widely distributed agent workgroups. The model is mainly made up of the detection agent, the recognition agent and the decision-making agent. The detection agent mainly realizes characteristics matching and sensor conditions controlling, the recognition agent is composed of information fusion algorithms into the nesting agents, the decision-making agent carries out various decision-making and cooperation mechanism. Then, the intelligent fusion processing under dynamic and uncertain environment is realized through communication and cooperation of the three agents for the actual condition request. It combines MAS and information fusion technology in target recognition system, and incarnates the advantages such as flexibility, reliability. Finally, by analyzing a ballistic target recognition experiment, the result is shown the model efficiency is advanced by the means. ©2010 IEEE.

关键词： Information fusion

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Robot Drivers: Learning to Drive by Trial & Error 15

Robot Drivers: Learning to Drive by Trial & Error

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15th International conference on Mobile Ad-Hoc and sensor Networks (MSN)

作者： Bosello, Michael Tse, Rita Pau, Giovanni Univ Bologna DISI Dept Comp Sci & Engn Cesena Italy Macao Polytech Inst Sch Appl Sci Macau Peoples R China UCLA Dept Comp Sci Los Angeles CA 90024 USA Sorbonne Univ LIP 6 Paris France

ISBN: (纸本)9781728152127

Autonomous cars have been in the making for over 15 years. Skepticism has taken the place of initial hype and enthusiasm. Current autonomous driving systems give no guarantee of 100% correctness and reliability, and users are not willing to take a chance on a car that is unable to cope with all the possible driving scenarios. robotic drivers are expected to be perfect. Major players such as Tesla and Waymo rely on highly detailed maps and very large sensor data in a race to build the ultimate robotic driver to cope with all possible driving scenarios. This approach optimizes for safety but delays the dream of fully autonomous cars. In this paper we consider robot-drivers as teen-drivers eager to learn how to drive but prone to mistakes in the beginning. The question we are trying to investigate is "what if we allow autonomous cars to make mistakes like young human drives do?" In this paper, we explore reinforcement learning for small size autonomous vehicles fusing information from several sensors including a camera, color sensors, and sonar sensors. The robot-drivers have initially no information about the driving scenarios they learn with experience through a mechanism of rewards designed to quickly help our robot-teen to learn its driving skills.

关键词： sensor fusion Reinforcement Learning self-driving multi-agents

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Wireless intelligent monitoring and analysis systems

Wireless intelligent monitoring and analysis systems

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conference on Multisensor, Multisource Information fusion

作者： Berry, N Djordjevich, D Ko, T Coburn, B Elliott, S Tsudama, B Whitcomb, M Sandia Natl Labs Embedded Reasoning Inst Livermore CA 94550 USA

ISBN: (纸本)0819453579

The wireless intelligent monitoring and analysis systems is a proof-of-concept directed at discovering solution(s) for providing decentralized intelligent data analysis and control for distributed containers equipped with wireless sensing units. The objective was to embed smart behavior directly within each wireless sensor container, through the incorporation of agent technology into each sensor suite. This approach provides intelligent directed fusion of data based on a social model of teaming behavior. This system demonstrates intelligent sensor behavior that converts raw sensor data into group knowledge to better understand the integrity of the complete container environment. The emergent team behavior is achieved with lightweight software agents that analyze sensor data based on their current behavior mode. When the system starts-up or is reconfigured the agents self-organize into virtual random teams based on the leader/member/lonely paradigm. The team leader collects sensor data from their members and investigates all abnormal situations to determine the legitimacy of high sensor readings. The team leaders flag critical situation and report this knowledge back to the user via a collection of base stations. This research provides insight into the integration issues and concerns associated with integrating multi-disciplinary fields of software agents, artificial life and autonomous sensor behavior into a complete system.

关键词： wireless sensors intelligent monitoring lightweight agents team coordination data fusion

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Finite-time Consensus for Multi-agent Systems with Application to sensor fusion

Finite-time Consensus for Multi-agent Systems with Applicati...

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Joint 48th IEEE conference on Decision and Control (CDC) / 28th Chinese Control conference (CCC)

作者： Jiang, Fangcui Wang, Long Peking Univ Ctr Syst & Control Dept Mech & Space Technol Coll Engn Beijing 100871 Peoples R China

ISBN: (纸本)9781424438723

This paper studies the finite-time consensus for a group of dynamic agents. For the entire group, we establish the explicit expression of the consensus state, which can be characterized by the new concept of weighted average consensus with respect to a monotonic function. For networks with undirected topology, we prove that the weighted average consensus with respect to a monotonic function can be solved in finite time when the interactions among agents are taken as power-law based function. These results are further extended to a class of networks where the topologies are directed and satisfy a detailed balance condition on coupling weights. As an application of the theoretical results to sensor fusion, we propose a distributed scheme to compute the maximum-likelihood (ML) estimate of unknown parameters. Numerical simulations show that the distributed scheme can be used to compute the ML estimate of unknown parameters effectively.

关键词： Topology

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distributed HIERARCHICAL DECISION fusion WITH CLUSTER VALIDITY

DISTRIBUTED HIERARCHICAL DECISION FUSION WITH CLUSTER VALIDI...

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1990 INTERNATIONAL CONF ON SYSTEMS, MAN, AND CYBERNETICS

作者： XIE, XL BENI, G Center for Robotic Syst Univ of California Santa Barbara CA USA

ISBN: (纸本)0879425970

The decision problem that arises in complex engineering problems where many separate sensors have to be used in parallel to measure a large set of objects, such as target detection/recognition, is addressed. In the situation considered, the feature vectors cannot be combined into a single higher-dimensional feature for central processing, so data fusion at the central station is impossible. A distributed hierarchical decision fusion method based on the validity of fuzzy clustering is proposed. Each sensor makes a local decision using a fuzzy clustering method and sends its decision about the number of clusters and the validity of the decision about the number of clusters and the validity of the decision, measured using a well-defined criterion, to the central station. The central station fuses the decisions collected from each sensor and makes a team decision optimizing all the local decisions. After clustering based on the optimal feature, the process is repeated until all object are properly classified. The result is a hierarchical decision tree that represents a description of a set of a priori unknown objects. This method has been applied to scenes where each objects appears in a specified region. An example is given to illustrate the method.

关键词： Decision Theory and Analysis

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distributed BLIND SYSTEM IDENTIFICATION IN sensor NETWORKS

DISTRIBUTED BLIND SYSTEM IDENTIFICATION IN SENSOR NETWORKS

引用

IEEE International conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Yu, Chengpu Xie, Lihua Soh, Yeng Chai Nanyang Technol Univ Sch Elect & Elect Engn EXQUISITUS Ctr E City Singapore 639798 Singapore

ISBN: (纸本)9781479928934

This paper studies the blind identification of multi-channel FIR systems in the context of sensor networks. distributed identification algorithms are developed for both noise-free and noise-contaminated networked systems. The proposed algorithms distribute the data storage and computational load among multiple agents connected by a specified topology, and are fulfilled via information exchanges among neighboring agents without the need of fusion centers. In the presence of measurement noises, a stabilized distributed algorithm is provided which can avoid trivial estimations of the multiple channels. In addition, convergence properties of the proposed algorithms are provided, and simulation examples are given to show the performances of the proposed algorithms.

关键词： Blind identification multi-agent system consensus based gradient method

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Self-Organized sensor Eggs for Decentralized Localization and Sensing on Vulcano Island - A Glimpse into Future Space Exploration with Swarms 27

Self-Organized Sensor Eggs for Decentralized Localization an...

引用

27th International conference on Information fusion (fusion)

作者： Broghammer, Fabio Wiedemann, Thomas Zhang, Siwei Dammann, Armin Gentner, Christian Djurict, Petar German Aerosp Ctr Inst Commun & Nav Cologne Germany Tech Univ Munich Percept Intelligent Syst Munich Inst Robot & Machine Intelligence Munich Germany SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA

ISBN: (纸本)9798350371420;9781737749769

robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized "sensor eggs". These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite Systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.

关键词： Decentralized particle filter gas source localization sensing

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Wireless acoustic communications for autonomous agents in structural health monitoring sensor networks

Wireless acoustic communications for autonomous agents in st...

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conference on Microelectronics - Design, Technology and Packaging III

作者： Wild, Graham Hinckley, Steven Edith Cowan Univ Phys Res Grp Joondalup WA 6027 Australia

ISBN: (纸本)9780819469694

Wireless acoustic communications methods have been demonstrated. These include both electro-acoustic and acousto-optic communications. The communications methods are intended for use by autonomous robotic agents in the Non-Destructive Evaluation (NDE) of structures containing a distributed acoustic emission sensor network. The acoustic emission sensors can be based on either piezoelectric or optical fibre sensors. The communications channel comprises of a piezoelectric transducer as the transmitter, an aluminium panel as the transmission medium, and either a second piezoelectric transducer or an optical fibre sensor as the receiver. distributed acoustic emission sensors are used in Structural Health Monitoring (SHM) for the detection of impacts and/or strain, in real time. Secondary damage may result from the initial impact or strain. This damage may include surface pitting, erosion, or cracking. These types of secondary damage may not be detectable, and hence may not be able to be monitored by the SHM system;specifically in optical fibre based sensing systems. The integration of NDE by robotic agents into a SHM sensor network enables the detection and monitoring of a wider variety of damage. Acoustic communication represents a wireless communication method that does not require any additional hardware, as piezoelectric transducers are commonly used in the NDE of materials. Various modulation methods were investigated for the communications channel. These include Amplitude Shift Keying (ASK), Frequency Shift Keying (FSK), and Phase Shift Keying (PSK). Successful communication was achieved using both the piezoelectric and optical fibre receivers. The optical fibre sensor used was a Fibre Bragg Grating (FBG).

关键词： acoustics optical fibre sensor piezoelectric transducers sensor networks structural health monitoring ultrasonics wireless communication

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