IEEE - Aerospace and Electronic Systems - June 2021 - 16

Search Planning of a UAV/UGV Team With Localization Uncertainty in a Subterranean Environment
Table 3.
Comparison of Multiple Runs for the Best and Worst
Trajectories
Path Run Median Mean RMS
(Pos.
Err.)
Best
Path
2
3
4
5
6
7
8
9
Worst
Path
MPE
Table 5.
Comparison of Multiple Runs for the Second Best and
Second Worst Trajectories
Path Run Median Mean RMS
(Pos.
Err.)
1 1.19 0.55 1.76 9.53
1.02 0.80 1.38 5.03
1.15 0.52 1.90 10.09
0.99 0.46 1.51 5.52
1.03 0.49 1.61 7.81
0.97 0.46 1.57 7.91
0.95 0.46 1.46 5.24
1.05 0.49 1.58 5.28
1.01 0.40 1.66 6.71
10 1.01 0.50 1.54 7.36
1 1.16 0.43 2.16 15.68
2
3
4
5
6
7
8
9
1.54 0.54 3.56 24.63
1.22 0.53 2.08 8.19
0.92 0.49 1.37 5.33
1.07 0.47 1.77 9.24
2.12 0.56 4.55 26.23
0.98 0.44 1.60 8.21
0.98 0.43 1.58 6.62
0.90 0.40 1.42 6.39
10 1.86 0.70 3.64 25.12
Table 4.
Mean and Median of Table III for Best and Worst
Paths
Path
Median Mean RMS
(Pos.
Err.)
Best
Path
Worst
Path
Mean 1.04
Median 1.02
Mean 1.27
Median 1.11
2nd
Worst
Path
2nd
Best
Path
2
3
4
5
6
7
8
9
MPE
1 1.02 0.50 1.57 6.29
1.12 0.49 1.76 6.48
1.48 0.61 3.12 26.96
1.05 0.45 1.68 6.47
1.27 0.58 2.03 8.20
1.05 0.49 1.67 7.47
1.20 0.52 1.84 6.87
1.11 0.61 1.68 7.44
0.98 0.46 1.55 6.83
10 1.09 0.51 1.66 7.74
1 1.32 0.47 2.32 9.56
2
3
4
5
6
7
8
9
0.97 0.42 1.52 6.53
1.00 0.43 1.68 8.09
1.13 0.54 1.75 8.43
0.90 0.42 1.44 6.08
1.01 0.53 1.47 5.61
1.07 0.53 1.59 5.79
0.98 0.41 1.62 7.19
1.08 0.47 1.67 6.19
10 2.75 0.59 5.89 25.06
MPE
0.52 1.60 7.05
0.49 1.57 7.03
0.50 2.37 13.56
0.48 1.93 8.73
inconsistencies in results obtained in Table 5, additional
tests have been conducted using a " perfect " version of the
navigation filter that is free from the possibility of missing
14
or outlier measurement updates of the UAV position from
the UGVs camera and LiDAR system. In particular, in
this scenario, whenever the UAV was within the simulated
modeled field-of-view of the camera or LiDAR, the truth
position of the UAV was used to construct measurement
updates for the EKF. This was to remove the potential
impact of missing sensors' updated during the simulation
that were caused by detection issues (e.g., not seeing the
UAV or incorrectly clustering something as the UAV) and
were, therefore, not reflected in the planning algorithm.
Similar to the previous section, using this " perfect "
EKF, 10 runs of the four paths (best, worst, 2nd best, 2nd
worst) were run and the mean and median are reported in
Tables 7 and 8.
As shown in the previous results, when using a navigation
filter with no missing or outlier measurement updates
and, therefore, more closely reflects the belief space planning
approach, the algorithm provides a better choice of
IEEE A&E SYSTEMS MAGAZINE
JUNE 2021

IEEE - Aerospace and Electronic Systems - June 2021

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