## 5 Results and Discussion
To confirm that the PDM-based mapping routine converges to a result similar to that achieved by a typical uniform survey routine, we approximated a raster-type survey by uniformly sampling the survey area where each measurement location is separated by 25 m and 50 m, resulting in a grid-by-grid accuracy of 0.93 and 0.88 using (18), respectively. Figures 7 and 8 shows the uniformly sampled survey area along with the fractional difference to the true distribution. The separation between measurement locations were chosen to reflect a final accuracy comparable to the PDM-based mapping routine. The accuracy and survey time of the uniform surveys will change based on the separation of measurement locations; larger separations produce a less-accurate but faster survey while smaller separations produce a more accurate but slower survey. Figure 6: The calculated prediction uncertainty through \(y=0\) for (**a**) the five-sensor and (**b**) seven-sensor surveys demonstrating that prediction uncertainties decrease near the vicinity of observed locations. Figure 7: (**a**) Uniform measurement locations with measurements taken every 50 m and linearly interpolated between observation points, and (**b**) the grid-by-grid fractional difference to the true distribution shown in Figure 1. The white markers represent locations where a measurement has been taken. The dose rate distributions shown in Figures 7 and 8 are given by implementing a two-dimensional linear interpolation between all sampled measurement locations. A raster-type motion path (from left to right and from top to bottom) for the uniform survey routine was simulated by using five and seven sensors with the same speed and dwell time as the PDM-based mapping routine. Figure 9 shows a comparison of E as calculated by (18) for GPR-predicted maps from the PDM-based routine versus the uniform survey routine. Figure 9 demonstrates that the PDM-based mapping routine provides a convergent map at a faster rate than using uniform survey routines, and that it does indeed converge to the expected results from a uniform survey routine. Furthermore, the ability for the PDM-based routine to provide actionable information at an appreciably faster rate is demonstrated due to its capability for predicting locations  
Figure 1.0 (**a**) Uniform measurement locations with measurements taken every 25 m and linearly interpolated between observation points, and (**b**) the grid-by-grid fractional difference to the true distribution shown in Figure 1.1 The white markers represent locations where a measurement has been taken. Figure 1.2 Direct comparison of the PDM-based survey routine results versus the uniform survey results as a function of the total survey time. The uncertainty in E for the GPR-predicted maps is represented by the prediction uncertainties shown in Figure 1.3  
that have not yet been visited. In practice, the information in Figure 9 would not be available as the true magnitude of the contamination is unknown; however, the figure demonstrates that our PDM-based motion planning procedure allows the sensors to move more efficiently than for uniform scanning, while arriving at the same result.