Research on MCL for Mobile Underground Miner Location System Based on ZigBee

2015 ◽  
Vol 713-715 ◽  
pp. 2325-2328
Author(s):  
Qian Liu ◽  
Yue Qiang Chu ◽  
Xing Han
Keyword(s):  
Monte Carlo ◽  
Complex Terrain ◽  
Mine Safety ◽  
Localization Algorithm ◽  
Real Time Scheduling ◽  
Software Application ◽  
Location System ◽  
Underground Miner ◽  
Time Scheduling ◽  
Monte Carlo Localization

In this paper, we try to solve the problem of mine safety and real-time scheduling in underground miner, the Monte-Carlo localization methods of the underground location system based on ZigBee wireless sensor network were proposed. This paper designs software application for the nodes in the network, builds the overall structure of the miner location system and constructs the ZigBee wireless network. After testing and simulation, the results indicate that we can locate precisely in the complex terrain and terrible communication environment by the Monte-Carlo Localization algorithm.

scholarly journals Multi-sensor three-dimensional Monte Carlo localization for long-term aerial robot navigation

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141773275 ◽  
Author(s):  
Francisco J Perez-Grau ◽  
Fernando Caballero ◽  
Antidio Viguria ◽  
Anibal Ollero
Keyword(s):  
Monte Carlo ◽  
Motion Tracking ◽  
Robot Navigation ◽  
Tracking System ◽  
Three Dimensional ◽  
Measurement Unit ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Motion Tracking System

This article presents an enhanced version of the Monte Carlo localization algorithm, commonly used for robot navigation in indoor environments, which is suitable for aerial robots moving in a three-dimentional environment and makes use of a combination of measurements from an Red,Green,Blue-Depth (RGB-D) sensor, distances to several radio-tags placed in the environment, and an inertial measurement unit. The approach is demonstrated with an unmanned aerial vehicle flying for 10 min indoors and validated with a very precise motion tracking system. The approach has been implemented using the robot operating system framework and works smoothly on a regular i7 computer, leaving plenty of computational capacity for other navigation tasks such as motion planning or control.

scholarly journals Navigating by touch: haptic Monte Carlo localization via geometric sensing and terrain classification

2021 ◽  
Vol 45 (6) ◽  
pp. 843-857
Author(s):  
Russell Buchanan ◽  
Jakub Bednarek ◽  
Marco Camurri ◽  
Michał R. Nowicki ◽  
Krzysztof Walas ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Estimation Error ◽  
Extreme Environments ◽  
Legged Robot ◽  
Terrain Classification ◽  
Localization Algorithm ◽  
Geometric Information ◽  
Contact Points ◽  
Monte Carlo Localization ◽  
Geometric Sensing

AbstractLegged robot navigation in extreme environments can hinder the use of cameras and lidar due to darkness, air obfuscation or sensor damage, whereas proprioceptive sensing will continue to work reliably. In this paper, we propose a purely proprioceptive localization algorithm which fuses information from both geometry and terrain type to localize a legged robot within a prior map. First, a terrain classifier computes the probability that a foot has stepped on a particular terrain class from sensed foot forces. Then, a Monte Carlo-based estimator fuses this terrain probability with the geometric information of the foot contact points. Results demonstrate this approach operating online and onboard an ANYmal B300 quadruped robot traversing several terrain courses with different geometries and terrain types over more than 1.2 km. The method keeps pose estimation error below 20 cm using a prior map with trained network and using sensing only from the feet, leg joints and IMU.

scholarly journals Monte Carlo localization algorithm based on particle swarm optimization

Automatika ◽  
2019 ◽  
Vol 60 (4) ◽  
pp. 451-461 ◽  
Author(s):  
Cuiran Li ◽  
Jianli Xie ◽  
Wei Wu ◽  
Haoshan Tian ◽  
Yingxin Liang
Keyword(s):  
Monte Carlo ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Localization Algorithm ◽  
Swarm Optimization ◽  
Monte Carlo Localization

scholarly journals Self-adaptive Monte Carlo localization for mobile robots using range finders

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Lei Zhang ◽  
René Zapata ◽  
Pascal Lépinay
Keyword(s):  
Monte Carlo ◽  
Mobile Robots ◽  
Position Tracking ◽  
Localization Algorithm ◽  
Global Localization ◽  
Adaptive Monte Carlo ◽  
Local Sample ◽  
Monte Carlo Localization ◽  
Sample Set ◽  
Self Adaptive

SUMMARYIn order to achieve the autonomy of mobile robots, effective localization is a necessary prerequisite. In this paper, we propose an improved Monte Carlo localization algorithm using self-adaptive samples (abbreviated as SAMCL). By employing a pre-caching technique to reduce the online computational burden, SAMCL is more efficient than the regular MCL. Further, we define the concept of similar energy region (SER), which is a set of poses (grid cells) having similar energy with the robot in the robot space. By distributing global samples in SER instead of distributing randomly in the map, SAMCL obtains a better performance in localization. Position tracking, global localization and the kidnapped robot problem are the three sub-problems of the localization problem. Most localization approaches focus on solving one of these sub-problems. However, SAMCL solves all the three sub-problems together, thanks to self-adaptive samples that can automatically separate themselves into a global sample set and a local sample set according to needs. The validity and the efficiency of the SAMCL algorithm are demonstrated by both simulations and experiments carried out with different intentions. Extensive experimental results and comparisons are also given in this paper.

An Enhanced Monte Carlo Localization Algorithm for Mobile Node in Wireless Sensor Networks

2013 ◽  
Vol 401-403 ◽  
pp. 1800-1804 ◽  
Author(s):  
Shi Ping Fan ◽  
Yong Jiang Wen ◽  
Lin Zhou
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Sensor Networks ◽  
Mobile Node ◽  
Wireless Sensor ◽  
Distribution Area ◽  
Localization Algorithm ◽  
Monte Carlo Localization ◽  
Sample Points ◽  
Unknown Node

There are some common problems, such as low sampling efficiency and large amount of calculation, in mobile localization algorithm based on Monte Carlo localization (MCL) in wireless sensor networks. To improve these issues, an enhanced MCL algorithm is proposed. The algorithm uses the continuity of the nodes movement to predict the area where the unknown node may reach, constructs high posteriori density distribution area, adds the corresponding weights to the sample points which fall in different areas, and filters the sample points again by using the position relations between the unknown node and its one-hop neighbors which include anchor nodes and ordinary nodes. Simulation results show that the localization accuracy of the algorithm is superior to the traditional localization algorithm. Especially when the anchor node density is lower or the unknown nodes speed is higher, the algorithm has higher location accuracy.

A Novel Stochastic Localization Algorithm for Sensor Nodes in Wireless Sensor Networks

2010 ◽  
Vol 39 ◽  
pp. 510-516
Author(s):  
Ming Wang ◽  
Shou Jun Bai ◽  
Huan Bao Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Monte Carlo ◽  
Sensor Networks ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
The Novel ◽  
Localization Algorithm ◽  
Mobile Wireless ◽  
Monte Carlo Localization ◽  
Mobile Wireless Sensor

Most of the proposed algorithms focus on static networks of sensors with either static or mobile anchors, in which the Monte Carlo localization algorithm is a typical one for localizing nodes in a mobile wireless sensor network. But the radio range being all different or inconstant in this algorithm leads to reduce the accuracy of localization and the efficiency of the algorithm itself. In this article, we propose the novel rang-based stochastic Monte Carlo localization algorithm for wireless sensor networks specifically designed with mobility to improve the accuracy of localization by dealing with the different radio ranges of sensors, and being bound to the narrow sampling area. Our simulation experimental results show that the rang-based stochastic Monte Carlo localization algorithm has improved the accuracy and stability of the estimated locations.

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