Range-only fuzzy Voronoi-enhanced localization of mobile robots in wireless sensor networks

Abstract

Wireless Sensor Network (WSN) localization has shown a growing research interest, thanks to the expected proliferation of WSN applications. This work is focused on indoor localization of a mobile robot in a WSN using only inter-node range measurements, which are estimated by radio frequency signal strength attenuation. These measurements are affected by different sources of uncertainty that make them highly noisy and unreliable. The proposed approach makes use of fuzzy logic for modeling and dealing with such uncertain information. Besides, the position estimation is enhanced using a rough description of indoor environment. The experiments show that the proposed localization approach (i) is fault-tolerant, (ii) results feasible in low-density WSNs, and (iii) provides better position estimations than well-known localization methods when the position measurements are affected by high uncertainty. Networks 1 (WSNs) have gained an increasing attention, thanks to the advances in wireless communications and sensor design that have permitted to reduce the cost and size of sensor devices. These sensor networks are composed of autonomous wireless sensing devices that incorporate sensing, processing, storing, and often radio frequency (RF) based on communication capabilities. Depending on particular applications, the network nodes are spatially distributed to cooperatively process and communicate sensed information. WSNs have been successfully applied in a wide spectrum of robotic applications, such as search and rescue, 2 disaster relief, 3 target tracking, 4 and smart environments, 5 to name but a few.The WSN localization problem consists of estimating the location or spatial coordinates of some or all sensor network nodes. In order to do so, different localization approaches make assumptions about their network and device capabilities, including hardware incorporated in devices, signal propagation models, computational and energy requirements, nature of environment (indoor versus outdoor), communication cost, accuracy requirements, and node mobility. Considering these constraints, each sensor

Mobile Robot Localization Using Fuzzy Segments

“…This state consists of the robot position and a fuzzy set representing the location uncertainty. We can differentiate between methods that represent the robot state using continuous fuzzy sets, such as the implementation of a fuzzy EKF [10], and methods that represent it in a discrete format, like the fuzzy grid-based method adopted in this work [18] [50].…”

An efficient approach for undelayed range-only SLAM based on Gaussian mixtures expectation

“…1. Thus, in the case of range-only localization several methods [10,11,12] are based on numerical optimization approaches which trilaterates the position of the mobile robot employing 3 or more static ranging nodes (also known as anchors) at different positions. On the other hand, [13] proposes a fingerprinting method using a neural network which is particularly useful when RSSI-based devices are used.…”

A Distance-Based Maximum Likelihood Estimation Method for Sensor Localization in Wireless Sensor Networks

“…Range-free localization methods do not need to measure the absolute distance or azimuth information. Rather, they estimate the distance between nodes based on the internode connectivity, the plurality of routing information exchange, and the overlapping areas [6]. Although range-free location methods have lower requirements for hardware and power consumption, the accuracy of localization is generally lower than that of the range-based methods [7,8].…”