Distributed maximum likelihood for self-localization in sensor networks

We show that the sensor localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we develop fully decentralized versions of the Recursive Maximum Likelihood and the Expectation-Maximization algorithms to localize the network. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a message passing algorithm to propagate the derivatives of the likelihood. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we show that the developed algorithms are able to learn the localization parameters well.

Distributed maximum likelihood for simultaneous self-localization and tracking in sensor networks

We show that the sensor self-localization problem can be cast as a static parameter estimation problem for Hidden Markov Models and we implement fully decentralized versions of the Recursive Maximum Likelihood and on-line Expectation-Maximization algorithms to localize the sensor network simultaneously with target tracking. For linear Gaussian models, our algorithms can be implemented exactly using a distributed version of the Kalman filter and a novel message passing algorithm. The latter allows each node to compute the local derivatives of the likelihood or the sufficient statistics needed for Expectation-Maximization. In the non-linear case, a solution based on local linearization in the spirit of the Extended Kalman Filter is proposed. In numerical examples we demonstrate that the developed algorithms are able to learn the localization parameters. © 2012 IEEE.

Gravitational self-localization for spherical masses

In this work, I consider the center-of-mass wave function for a homogenous sphere under the influence of the self-interaction due to Newtonian gravity. I solve for the ground state numerically and calculate the average radius as a measure of its size. For small masses, M≲10−17 kg, the radial size is independent of density, and the ground state extends beyond the extent of the sphere. For masses larger than this, the ground state is contained within the sphere and to a good approximation given by the solution for an effective radial harmonic-oscillator potential. This work thus determines the limits of applicability of the point-mass Newton Schrödinger equations for spherical masses. In addition, I calculate the fringe visibility for matter-wave interferometry and find that in the low-mass case, interferometry can in principle be performed, whereas for the latter case, it becomes impossible. Based on this, I discuss this transition as a possible boundary for the quantum-classical crossover, independent of the usually evoked environmental decoherence. The two regimes meet at sphere sizes R≈10−7 m, and the density of the material causes only minor variations in this value.

Theory and Algorithms for Hop-Count-Based Localization with Random Geometric Graph Models of Dense Sensor Networks

Wireless sensor networks can often be viewed in terms of a uniform deployment of a large number of nodes in a region of Euclidean space. Following deployment, the nodes self-organize into a mesh topology with a key aspect being self-localization. Having obtained a mesh topology in a dense, homogeneous deployment, a frequently used approximation is to take the hop distance between nodes to be proportional to the Euclidean distance between them. In this work, we analyze this approximation through two complementary analyses. We assume that the mesh topology is a random geometric graph on the nodes; and that some nodes are designated as anchors with known locations. First, we obtain high probability bounds on the Euclidean distances of all nodes that are h hops away from a fixed anchor node. In the second analysis, we provide a heuristic argument that leads to a direct approximation for the density function of the Euclidean distance between two nodes that are separated by a hop distance h. This approximation is shown, through simulation, to very closely match the true density function. Localization algorithms that draw upon the preceding analyses are then proposed and shown to perform better than some of the well-known algorithms present in the literature. Belief-propagation-based message-passing is then used to further enhance the performance of the proposed localization algorithms. To our knowledge, this is the first usage of message-passing for hop-count-based self-localization.

Design and path planning for a remote-brained service robot

This paper presents a novel robot named "TUT03-A" with expert systems, speech interaction, vision systems etc. based on remote-brained approach. The robot is designed to have the brain and body separated. There is a cerebellum in the body. The brain with the expert systems is in charge of decision and the cerebellum control motion of the body. The brain-body. interface has many kinds of structure. It enables a brain to control one or more cerebellums. The brain controls all modules in the system and coordinates their work. The framework of the robot allows us to carry out different kinds of robotics research in an environment that can be shared and inherited over generations. Then we discuss the path planning method for the robot based on ant colony algorithm. The mathematical model is established and the algorithm is achieved with the Starlogo simulating environment. The simulation result shows that it has strong robustness and eligible pathfinding efficiency.

Design and path planning for a remote-brained service robot

This article introduced an effective design method of robot called remote-brain, which is made the brain and body separated. It leaves the brain in the mother environment, by which we mean the environment in which the brain's software is developed, and talks with its body by wireless links. It also presents a real robot TUT06-B based on this method which has human-machine interaction, vision systems, manipulator etc. Then it discussed the path planning method for the robot based on ant colony algorithm in details, especially the Ant-cycle model. And it also analyzed the parameter of the algorithm which can affect the convergence. Finally, it gives the program flow chat of this algorithm.

用于移动机器人自定位的视觉路标设计

为了实现室内移动机器人的自定位,提出了一种简易美观的新型视觉人工路标以及基于对数极坐标系投影直方图的路标识别方法,并用基于共面四点的位姿估计算法计算机器人位姿。实验结果说明,路标检测具有很高的鲁棒性;路标识别方法抗噪声和形变的能力强;位姿精度足够满足室内移动机器人自定位的需要。

基于视觉的室内移动机器人自定位技术研究

在关于移动机器人的诸多研究领域中，机器人自定位是十分关键的技术，是实现机器人自主运动和其他任务的基础，而且涉及领域广泛，有很多难点有待解决，因而是一个具有重要研究价值的课题。 本论文以沈阳新松机器人股份有限公司自主研发的家庭服务机器人为研发平台，系统地研究了基于计算机视觉的室内移动机器人自定位问题，成功设计了基于单目视觉人工路标以及粒子滤波的室内移动机器人自定位系统。 本文首先根据室内移动机器人自主导航定位的要求，设计了一种简易美观的新型视觉人工路标，并且研究与实现了该路标的实时准确检测以及不同路标的识别。 其次，在位姿计算方面，本文研究了共面P4P（4点透视）问题的解法及其在位姿计算方面的应用，并分析比较了两种不同P4P解法的优缺点，成功地将两种算法结合起来用于机器人位姿计算。 最后，在机器人自定位方面，本文将单目彩色摄像机作为传感器，在基于贝叶斯滤波理论的自定位理论框架下，利用粒子滤波自定位方法融合视觉信息与码盘信息，实现了自主移动机器人的自定位。 实践证明，本文设计的基于单目视觉人工路标的自定位系统能够成功地应用在室内移动机器人上，具有较高的应用推广价值。

基于TOA的无线传感器网络自定位技术的研究

提出一种基于TOA(tim e of arrival)测距、利用节点之间的几何关系实现传感器网络中未知节点自定位的算法.该方法计算量小、算法简单且定位精度高.仿真实验证明了该方法的有效性.*

自主移动机器人足球比赛视觉定位方法综述

综述了RoboCup足球赛中全自主移动机器人基于视觉的定位技术,包括机器人自定位和多机器人协作物体定位.介绍了定位技术的发展情况与分类.从机器人环境构建形式的不同以及先验位姿和概率方法的应用与否等方面,系统地分析和比较了各种自定位方法.对于多机器人协作物体定位,阐述了静态方法和动态跟踪方法.总结了定位过程中需要重点研究的传感器模型构建、图像处理、特征匹配以及协作过程涉及的相关问题.最后就视觉定位存在的问题和技术发展趋势进行了讨论.

移动机器人几何-拓扑混合地图的构建及自定位研究

基于墙角、房门和通路等高级环境特征的辨识与提取,依据几何和拓扑环境模型完成混合地图的构建,并根据混合地图的特点,提出在局部几何环境采用扩展卡尔曼滤波算法实现移动机器人的位姿跟踪,而在拓扑地图的节点位置则根据绑定的高级环境特征进行位姿再校正的混合定位方法.将该方法应用于实际移动机器人平台,所得结果证明了方法的有效性和实用性.