Path Planning for Localization of Radiation Sources Based on Principal Component Analysis

Abstract

In this paper, we propose a path planning method for the localization of radiation sources using a mobile robot equipped with an imaging gamma-ray detector, which has a field of view in all directions. The ability to detect and localize radiation sources is essential for ensuring nuclear safety, security, and surveillance. To enable the autonomous localization of radiation sources, the robot must have the ability to automatically determine the next location for gamma ray measurement instead of following a predefined path. The number of incident events is approximated to be the squared inverse proportional to the distance between the radiation source and the detector. Therefore, the closer the distance to the source, the shorter the time required to obtain the same radiation counts measured by the detector. Hence, the proposed method is designed to reduce this distance to a position where a sufficient number of gamma-ray events can be obtained; then, a path to surround the radiation sources is generated. The proposed method generates this path by performing principal component analysis based on the results obtained from previous measurements. Both simulations and actual experiments demonstrate that the proposed method can automatically generate a measurement path and accurately localize radiation sources.

Path-Planning System for Radioisotope Identification Devices Using 4π Gamma Imaging Based on Random Forest Analysis

“…This is the case when one measurement position is close to the source and the other position is far from the source considering the parallax to the source. Therefore, the measurement positions suggested by the prediction model are also consistent with ones preferred as per the detector movement algorithm for a single source, as discussed in our previous study [13,14].…”

“…In autonomous source identification by a 4π Compton imager mounted on a robotic vehicle, it is necessary to optimize the measurement procedure, i.e., the measurement positions around the target sources. For this purpose, we proposed a detector movement algorithm for a single source [13,14]. In this study, we developed a path-planning system for radioisotope identification devices by using 4π gamma imaging based on random forest (RF) analysis.…”

Path planning for autonomous mobile robots using multi-objective evolutionary particle swarm optimization

“…Testing results from the simulation found that an instructed system could identify a wide and unfamiliar environment and move to the required targets. Another study was conducted by [29] using principal component analysis (PCA) to teach robots to move toward the optimal paths. Although the neural network method could build paths as needed, it required a long time to prepare information examples and teach the robots.…”

Object Localization and Tracking System Using Multiple Ultrasonic Sensors with Newton–Raphson Optimization and Kalman Filtering Techniques

“…Furthermore, the nonlinear LS problem can be obtained such that: (10) which represents the minimum summation of the squared errors between measurement values and model values at the corresponding points in time. An argument vector can be constructed as: r(z g ) = [r 1 (z g ) .…”

“…In many sensing technologies, object localization and tracking with range measurements have received significant attention because of their importance in many applications, such as obstacles avoidance, smart surveillance, human-computer interaction, safe sensing in factories, and autonomous navigation [1][2][3][4][5][6][7][8][9][10]. Aiming at the problem of object localization and tracking, this paper focuses on the construction of an object localization and tracking system based on an ultrasonic array.…”