基于多传感器数据融合的智能小车避障的研究

针对智能小车避障问题,提出了一种将模糊逻辑和神经网络相结合的融合方法—Takagi-Sugeno(T-S)模糊神经网络方法。基于此方法的数据融合算法应用在智能小车避障运动中,采用多只超声波传感器和红外线传感器探测障碍物的距离和方向,采集的各种数据利用T-S模糊神经网络进行融合。通过实验仿真表明:此方法能够使智能小车对障碍物的灵活避障和导航行进。     

两级传感器信息融合的移动机器人避障研究

针对移动机器人的避障问题,以AS-R移动机器人为研究平台,提出了一种将神经网络和模糊神经网络相结合的两级融合方法。采用BP神经网络对多超声波传感器信息进行融合,以减少传感器信息的不确定,提高对障碍物识别的准确率;采用模糊神经网络实现移动机器人的避障决策控制,使之更适合系统的避障要求。该方法使移动机器人在避障中具有较好的灵活性和鲁棒性。机器人避障实验验证了所提方法的有效性。     

残疾人轮椅智能避障功能创新研究

为研究电动轮椅的智能避障功能,该研究特点在于将HC-SR04超声波传感器和ZY101红外避障传感器相结合使轮椅对环境的感知更加准确,通过多传感器融合技术和模糊控制技术的研究进一步提高避障功能计算的精确度,使电动轮椅在实际行驶中可以自主地完成路线规划并躲避障碍物,更安全便捷地抵达目的地.     

吸尘机器人系统设计与避障算法研究

基于Cortex-M0微控制器设计超声波、红外和碰撞等多传感器硬件系统感知机器人工作环境,应用模糊神经网络对采集的数据进行信息融合处理,输出结果用来控制吸尘机器人的定位与避障。实验证明,多传感器硬件系统和基于模糊神经网络的避障算法大大提高了吸尘机器人的定位与避障精度,对不同的工作环境也具有良好的鲁棒性。     

基于多传感器融合的智能电动轮椅模糊测距控制

传统电动轮椅在复杂的外界环境及技术误差下,避障方法存在着误判率高,检测精准度低的问题。为了提高智能轮椅自动避障的安全性,设计基于多传感器融合技术,对智能轮椅实现模糊测距控制的智能控制器。能够根据电动轮椅配置的传感器数量和类型,可靠获取障碍物与轮椅之间的数据信息,并利用多传感器信息融合技术,分析预测障碍物相关的具体信息数据。在给定值的基础上,计算需要的多种控制变量,并进行模糊量化处理。根据模糊控制规则,在智能轮椅与障碍物模糊语言描述之间的关系下进行模糊决策,非模糊化处理,确保智能轮椅运行更加安全、稳定。     

基于模糊神经网络在智能轮椅避障中的应用

针对机器人在未知环境中的避障问题,提出了一种多传感器信息融合的避障方法.利用多传感器(声纳、摄像头)来采集外部环境信息,使得智能轮椅在移动过程中可以得到更充分的外部环境信息;使用基于Takagi-Sugeno (T-S)模型的模糊神经网络来对环境信息进行融合;通过融合的结果来控制轮椅的避障行为.通过模拟实验验证和分析,表明了该方法在解决轮椅避障问题方面有很好的效果,同时优化了轮椅避障的路径,提高了智能轮椅使用的安全性和方便性.     

模糊神经网络信息融合方法在机器人避障中的应用

基于Takagi—Sugeno(T—S)模型的模糊神经网络不但具有模糊逻辑和神经网络两者的优点，又具有很好的学习能力。将基于T—S模型的模糊神经网络的信息融合算法应用在移动机器人的避障运动中，采用了多个超声测距传感器探测障碍物的距离和方向，经过模糊神经网络信息融合后，实现了机器人对障碍物和环境类型的识别以及无冲突的运动。实验表明：此方法能够使机器人安全避障。     

多路超声波机器人的模糊避障研究

针对传统的超声波机器人避障中感知信息单一,测距盲区与信号串扰大的问题,提出一种基于多传感器信息融合的机器人避障技术,采取分组采样的技术采集多路无串扰的信号,使用中值滤波的方式加强融合信息的时间空间连续性,使用模糊控制技术对机器人避障进行控制;通过对机器人实际的不同路状下的避障实验证明该方法具有很好的鲁棒性与有效性.     

基于ARM的轮式机器人避障系统设计

为了提高轮式机器人在未知环境中的避障性能,采用ARM控制器,结合多传感器融合技术和模糊控制技术,对轮式机器人避障系统进行了研究。利用多路传感器采集未知环境中搜索范围内的障碍物距离信息,根据不同的距离信息来制定具有避障功能的模糊控制算法,进而控制轮式机器人的运动状态。实验中,根据轮式机器人反馈的信息不断调整参数,以达到准确避障。实验结果表明:所提方法能够有效地解决轮式机器人在未知环境中的避障问题,在无人驾驶方面具有广泛的应用前景。     

基于多传感器信息融合的移动机器人避障

为了更好地解决移动机器人在未知环境下的自主避障问题,采用多传感器信息融合的方法,通过多个超声传感器对障碍物信息进行采集。合理确立模糊控制器的输入输出,通过模糊推理将障碍物距离信息模糊化,建立模糊规则并解模糊,以达到对移动机器人的安全避障的控制。通过建立移动机器人运动模型,设计了仿真平台,得到实验结果表明:该算法具有良好的可行性。     

Ultrasonic sensor data integration and its application to environment perception

To move in an unknown or uncertain environment, a mobile robot must collect information from various sensors and use it to construct a representation of the external world. Ultrasonic sensors can provide range data for this purpose in a simple and cost-effective way. However, most ultrasonic sensors are not sufficient for environment recognition because of their large beam opening angles. In this article the beam-opening-angle problem is solved by fusing data from multiple ultrasonic sensors. We propose two methods for sensor data fusion. One uses an artificial neural network (ANN), and the other is based on a mathematical model. Simulations and experiments show that the mathematical model is more accurate when there is no noise in the sensor readings, but the ANN method is better when the sensors are subject to much noise. To extract line segments from the ultrasonic image, we develop a line extractor that is more efficient than traditional line fitting methods in this application. Experimental results show that this method is effective for environment perception in a robotic system. © 1996 John Wiley & Sons, Inc.     

Sensor-fusion of hydraulic data for burst detection and location in a treated water distribution system

This paper presents research into analysis and data fusion for sensors measuring hydraulic parameters (flow and pressure) of the pipeline water flow in treated water distribution systems. An artificial neural network (ANN) based system is used on time series data produced by sensors to construct an empirical model for the prediction and classification of leaks. A rules based system performs a fusion on the ANNs’ outputs to produce an overall state classification for a set of zones. Results are presented using data from an experimental site in a distribution system of a UK water company in which bursts were simulated by hydrant flushing. The ANN system successfully detected events and a study of the pressure gradient across the zone provided a more precise location within the zone.     

基于环境监测的两级数据融合模型与算法

利用多源传感器采集的数据不仅存在大量冗余,而且会影响最终监测结果.为了提高监测的准确度,本文提出一种面向草原环境监测的两级数据融合模型.在一级数据融合中,首先采用自适应加权平均法对各区域内的同类传感器进行融合,然后利用BP神经网络对该区域内的异类传感器进行训练和融合,从而得到对各区域环境状况的初步判断.由于经BP神经网络融合的结果具有不确定性,因此,二级融合利用D-S证据理论对一级融合结果进行综合分析,从而得到对草原环境的决策判断.最后对模型及算法进行了有效性验证与分析,实验结果表明本文的方法能够较准确地监测草原环境状况,同时对草原环境的高效管理和科学养护等提供一些有价值的指导和决策依据.     

水声信号处理中的多传感器数据融合

水下环境的复杂性往往使得单个传感器的可靠性降低,而多个水下传感器的共同使用则成为一个趋势,这就涉及到对于来自多个传感器的数据进行多方面、多层次的融合处理。概括和分析了当前多传感器数据融合技术在水声信号处理领域中的应用现状,并将诸多的融合方法按照具体应用范畴进行了分类,主要包括水下目标探测、跟踪和识别,以及水下自制机车导航等方面,对每种应用情况下的各种数据融合方法进行了比较。     

多传感器噪声方差未知情况下的异步航迹融合

针对分布式多传感器数据融合系统,提出了一种多传感器异步航迹融合算法。现有的多传感器信息融合算法大都基于Kalman滤波器,要求噪声方差已知,并且假定各传感器同步采样,不考虑通信延迟。本文在分布式处理的模式下,基于各传感器在扩展记忆因子递推最小平方(EFRLS)估计形成本地航迹的基础上,提出了一种融合误差均方差矩阵的迹最小意义下的异步目标航迹融合算法。仿真实验结果表明,这种融合算法是有效的,算法接近集中式融合算法的精度。     

Combining Intelligent Techniques for Sensor Fusion

Mobile robots rely on sensor data to build a representation of their environment. However, sensors usually provide incomplete, inconsistent or inaccurate information. Sensor fusion has been successfully employed to enhance the accuracy of sensor measures. This work proposes and investigates the use of Artificial Intelligence techniques for sensor fusion. Its main goal is to improve the accuracy and reliability of the distance measure between a robot and an object in its work environment, based on measures obtained from different sensors. Several Machine Learning algorithms are investigated to fuse the sensors data. The best model generated by each algorithm is called estimator. It is shown that the employment of estimators based on Artificial Intelligence can improve significantly the performance achieved by each sensor alone. The Machine Learning algorithms employed have different characteristics, causing the estimators to have different behaviors in different situations. Aiming to achieve an even more accurate and reliable behavior, the estimators are combined in committees. The results obtained suggest that this combination can further improve the reliability and accuracy of the distances measured by the individual sensors and estimators used for sensor fusion.     

LSFR算法在多传感器分布式检测中的优化研究

在多传感器分布式检测系统中,常规融合规则算法要求传感器误差概率已知,且系统中传感器和融合中心同时优化存在一定困难.提出最小二乘融合规则(LSFR)算法,算法不依赖噪声环境稳定性以及传感器的虚警概率与检测概率,融合中心根据各个传感器的硬决策,得到全局的硬决策,并在传感器和融合中心处理达到最优时,获得最佳全局性能.仿真结果表明:对比似然比融合决策算法与Neyman Pearson融合规则(NPFR)算法,LSFR算法全局检测概率显著提高,且在不同数量规模传感器和更多类型的分布式检测系统中具有较好兼容性.     

真实-虚拟多层传感器技术在煤岩识别中应用

将真实-虚拟多层传感器信息融合技术应用到煤岩界面识别系统中。采用由真实传感器层和虚拟传感器层组成的复合融合结构,并引用了多层传感器的复合融合估计算法,以解决在测量噪声干扰下的参数估计问题。通过实例仿真结果表明：采用此方法后,煤岩界面识别精度明显提高,从而使采煤系统的生产自动化程度得到进一步增强。     

Sensor-based fuzzy navigation of an autonomous mobile robot in an indoor environment

The work presented in this paper deals with the problem of navigating a mobile robot either in an unknown indoor environment or in a partially known one. A navigation method based on the combination of elementary behaviors has been developed for an unknown environment. Most of these behaviors are achieved by means of fuzzy inference systems. The proposed navigator combines two types of obstacle avoidance behaviors, one for the convex obstacles and one for the concave ones. In the case of a partially known environment, a hybrid method is used to exploit the advantages of global and local navigation strategies. The coordination of these strategies is based on a fuzzy inference system that involves an on-line comparison between the real scene and a memorized one. Both methods have been implemented on the miniature mobile robot Khepera^® which is equipped with rough sensors. The good results obtained illustrate the robustness of a fuzzy logic approach with regard to sensor imperfections.     

A new space and time sensor fusion method for mobile robot navigation

To fully utilize the information from the sensors of mobile robot, this paper proposes a new sensor‐fusion technique where the sample data set obtained at a previous instant is properly transformed and fused with the current data sets to produce a reliable estimate for navigation control. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar‐sensing system or the visual‐sensing system. Notice that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more sensors are required to measure a given physical parameter or to improve the reliability of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequences of the data sets are stored and utilized for the purpose. The basic principle is illustrated by examples and the effectiveness is proved through simulations and experiments. The newly proposed STSF (space and time sensor fusion) scheme is applied to the navigation of a mobile robot in an environment using landmarks, and the experimental results demonstrate the effective performance of the system. © 2004 Wiley Periodicals, Inc.