Page 51 - Read Online

Page 144 Ortiz et al. Intell Robot 2021;1(2):131-50 I http://dx.doi.org/10.20517/ir.2021.09

A
2.2
2
1.8
E[l sub ] 1.6
1.4
1.2
1
0.8
0 0.1 0.2 0.3 0.4 0.5 0.6
Density
B
2.2
2
1.8
E[l sub ] 1.6
1.4
1.2
1
0.8
0 0.1 0.2 0.3 0.4 0.5 0.6
Density

Figure 7. The average path length: (A) sliding mode simultaneous localization and mapping and genetic algorithm; and (B) polar histogram.

B
obs
x
100 i g(x ,x ) j f* x T
i
90
80
70
60
y [m] 50

40
30
20
x
10 S
0
0 10 20 30 40 50 60 70 80 90 100
x [m]
Figure 8. Sliding mode simultaneous localization and mapping (gray) and grid method (black)

of obstacles was bigger, the path length of the polar histogram grew more quickly than that of ours. When the
obstacle density was 0.3, [ , 1] = 1.053, [ , 2] = 1.152.

Next, we compare our method with the grid method [51] . The comparison results are shown in Figure 8. For
the task of navigating the robot or system in partially unknown or completely unknown environments, the
SLAM algorithm was used to construct the environment and know the position of the robot. At the beginning
of navigation in the partially unknown environment, there was a planned trajectory of navigation through the
GA algorithm; however, if an obstacle were found in the planned trajectory, the GA algorithm needed to be
used to search for a new trajectory within the built environment by the SLAM, .

The size of the environments was 100 m × 100 m, in which a solution was sought to find a trajectory from the
initial point to the target point . Figure 9 shows a path planning based on the proposed methods to find