Adaptive Bayesian Sensor Motion Planning for Hazardous Source Term Reconstruction

Abstract

There has been a strong interest in emergency planning in response to an attack or accidental release of harmful chemical, biological, radiological or nuclear substances. Under such circumstances, it is of paramount importance to determine the location and release rate of the hazardous source to forecast the future harm it may cause and employ methods to minimize the disturbance. In this paper, a sensor data collection strategy is proposed whereby an autonomous mobile sensor is guided to address such a problem with a high degree of accuracy and in a short amount of time. First, the parameters of the release source are estimated using the Markov chain Monte Carlo sampling approach. The most informative manoeuvre from the set of possible choices is then selected using the concept of maximum entropy sampling. Numerical simulations demonstrate the superior performance of the proposed algorithm compared to traditional approaches in terms of estimation accuracy and the number of measurements required.

Unmanned Aerial Vehicle-Based Hazardous Materials Response: Information-Theoretic Hazardous Source Search and Reconstruction

“…The average search time, the source position estimate RMSE, and the source emission rate RMSE using both methods are shown in Table II. Overall, in agreement with the simulations conducted in [3], the information theoretic search algorithm attained more accurate source position estimates and a significantly lower average search time. The emission rate RMSE was less affected; this is expected to be due to the large amount of noise in the sensor measurements for both strategies, and the lower number of measurements taken during the informative search before the source was found.…”

“…It is hypothesised that a more accurate and rapid response could be achieved by planning the path of the UAS on-line in response to the information gained during the mission. This is termed an information theoretic approach, which was shown in [3] to outperform a systematic approach for source term estimation in simulation. The ability to estimate the source term of a dispersive release using an information theoretic approach was recently explored in [4] where simple experiments validated the approach with a ground robot equipped with a metal oxide (MOX) sensor in an indoor arena with fans to create a wind field and smoke to simulate a HAZMAT release.…”

Source term estimation of a hazardous airborne release using an unmanned aerial vehicle

“…This leads to the problem of how to guide the platform to collect enough useful spatial‐temporal data to effectively estimate the parameters of the release. The most intuitive approaches would be to continue the trajectory of a typical gas source localisation algorithm, or to perform a systematic search pattern, such as a parallel sweep or an Archimedean spiral (Champagne, Carl, & Hill, ; Hutchinson, Oh, & Chen, ). Alternatively, information‐based guidance methods have been proposed to guide the robot to the expected most informative locations to take measurements.…”

Double Q-Learning for Radiation Source Detection

“…Cortez et al, designed radiation surveys based on variances in acquired measurements and uncertainties in the radiation field [ 17 ]. Hutchinson et al, sequentially determined the detector’s placement positions using the concept of maximum entropy sampling [ 18 ]. Lazna et al, proposed a circular path planning strategy to exploit the directional characteristics of detectors [ 19 ].…”