A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured

A Novel Hybrid NN-ABPE-Based Calibration Method for Improving Accuracy of Lateration Positioning System

Positioning systems based on the lateration method utilize distance measurements and the knowledge of the location of the beacons to estimate the position of the target object. Although most of the global positioning techniques rely on beacons whose locations are known a priori, miscellaneous factors and disturbances such as obstacles, reflections, signal propagation speed, the orientation of antennas, measurement offsets of the beacons hardware, electromagnetic noise, or delays can affect the measurement accuracy. In this paper, we propose a novel hybrid calibration method based on Neural Networks (NN) and Apparent Beacon Position Estimation (ABPE) to improve the accuracy of a lateration positioning system. The main idea of the proposed method is to use a two-step position correction pipeline that first performs the ABPE step to estimate the perceived positions of the beacons that are used in the standard position estimation algorithm and then corrects these initial estimates by filtering them with a multi-layer feed-forward neural network in the second step. In order to find an optimal neural network, 16 NN architectures with 10 learning algorithms and 12 different activation functions for hidden layers were implemented and tested in the MATLAB environment. The best training outcomes for NNs were then employed in two real-world indoor scenarios: without and with obstacles. With the aim to validate the proposed methodology in a scenario where a fast set-up of the system is desired, we tested eight different uniform sampling patterns to establish the influence of the number of the training samples on the accuracy of the system. The experimental results show that the proposed hybrid NN-ABPE method can achieve a high level of accuracy even in scenarios when a small number of calibration reference points are measured

Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories

Trajectory optimization using learning from demonstration with meta-heuristic grey wolf algorithm

Nowadays, most robotic systems perform their tasks in an environment that is generally known. Thus, robot’s trajectory can be planned in advance depending on a given task. However, as a part of modern manufacturing systems which are faced with the requirements to produce high product variety, mobile robots should be flexible to adapt to changing and diverse environments and needs. In such scenarios, a modification of the task or a change in the environment, forces the operator to modify robot’s trajectory. Such modification is usually expensive and time-consuming, as experienced engineers must be involved to program robot’s movements. The current paper presents a solution to this problem by simplifying the process of teaching the robot a new trajectory. The proposed method generates a trajectory based on an initial raw demonstration of its shape. The new trajectory is generated in such a way that the errors between the actual and target end positions and orientations of the robot are minimized. To minimize those errors, the grey wolf optimization (GWO) algorithm is applied. The proposed approach is demonstrated for a two-wheeled mobile robot. Simulation and experimental results confirm high accuracy of generated trajectories

The expression of Platelet-derived Growth factor receptors (PDGFRs) and their correlation with overall survival of patients with ovarian cancer

Objectives: The main aim of the study was to investigate the expression of Platelet-Derived Growth Factor Receptors alpha (PDGFR-alpha) and beta (PDGFR-beta) in malignant and benign ovarian tumors. We performed an analysis of the correlation of PDGFRs expression and stage of the disease, tumor grade and histopathological type of epithelial ovarian cancer (EOC). Additionally, we evaluated patient prognosis according to PDGFR expression.  Material and methods: Our study group was composed of 52 samples of EOCs, 35 samples of benign ovarian tumors (BOTs), and 21 samples of unchanged ovaries (UOs). The samples were collected from patients who had been operated on in the Division of Gynecological Surgery of the Poznan University of Medical Sciences.  Results: PDGFR-alpha was found to be expressed more frequently in cancer cells of EOCs, when compared with tumor cells of BOTs and epithelium of UOs. On the other hand, PDGFR-alpha receptors were present less frequently in the stroma of EOCs, when compared with the stroma of BOTs and UOs. Comparing the studied groups, there were no statistically significant differences in the expression of PDGFR-beta. The expression of both PDGFRs was not related to the FIGO stage, grade or histopathological type of EOCs. The expression of the PDGFR-beta receptor in cancer cells was associated with an improved overall survival among patients with EOCs. Patient prognosis was not affected by either PDGFR-alpha expres- sion or by PDGFR-beta tumor stroma expression.  Conclusions: The expression of PDGFR-alpha is significantly different when comparing EOCs, BOTs and UOs. However, the prognosis of EOC only seems to be affected by PDGFR-beta expression in cancer cells.

Neural network-based calibration for accuracy improvement in lateration positioning system

Mobile robot positioning is a crucial problem in modern industrial autonomous solutions. Lateration Positioning Systems base on the distance measurements to estimate the object's position. These measurements are however often affected by numerous sources of noise: obstacles, multi-pathing, signal propagation speed etc. Effective calibration methods are therefore required to eliminate these errors to achieve precise positioning. In this paper, we present the application of neural networks to improve the accuracy of a UWB lateration system. We present the network architecture and demonstrate how it can be used to alleviate the effects of multi-pathing and environment anisotropy in a real positioning setup. We furthermore compare the efficiency of the neural network with the state-of-the-art calibration methods

Neural network-based calibration for accuracy improvement in lateration positioning system

Mobile robot positioning is a crucial problem in modern industrial autonomous solutions. Lateration Positioning Systems base on the distance measurements to estimate the object's position. These measurements are however often affected by numerous sources of noise: obstacles, multi-pathing, signal propagation speed etc. Effective calibration methods are therefore required to eliminate these errors to achieve precise positioning. In this paper, we present the application of neural networks to improve the accuracy of a UWB lateration system. We present the network architecture and demonstrate how it can be used to alleviate the effects of multi-pathing and environment anisotropy in a real positioning setup. We furthermore compare the efficiency of the neural network with the state-of-the-art calibration methods

The cosmic ray detector for the NICA collider

Multi-Purpose Detector (MPD) is a main part of a new Ion Collider fAcility (NICA) located in Dubna, Russia. To increase MPD functionality, it was proposed to add an additional muon trigger system for off-beam calibration of the MPD sub-detectors and for rejection of cosmic ray background during experiments. The system could also be very useful for astrophysical observations of cosmic showers initiated by high energy primary particles. This article describes the main goals of MCORD detector and the early stage of MCORD design, based on plastic scintillators with silicon photomultiplier photodetectors (SiPM) for scintillation readout and electronic system based on MicroTCA standard

The cosmic ray detector for the NICA collider

Multi-Purpose Detector (MPD) is a main part of a new Ion Collider fAcility (NICA) located in Dubna, Russia. To increase MPD functionality, it was proposed to add an additional muon trigger system for off-beam calibration of the MPD sub-detectors and for rejection of cosmic ray background during experiments. The system could also be very useful for astrophysical observations of cosmic showers initiated by high energy primary particles. This article describes the main goals of MCORD detector and the early stage of MCORD design, based on plastic scintillators with silicon photomultiplier photodetectors (SiPM) for scintillation readout and electronic system based on MicroTCA standard