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Ortiz et al. Intell Robot 2021;1(2):131-50 I http://dx.doi.org/10.20517/ir.2021.09 Page 145

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

40
30
20
10
0
0 10 20 30 40 50 60 70 80 90 100
x [m]
Figure 9. Sliding mode simultaneous localization and mapping based path planning (bold) and grid method (gray).

90
Iter 40
80
Iter 30
Iter 20
70
Iter 10
60
50
40
30
20
10
0
185 190 195 200 205 210 215
Histogram (RGA)
Figure 10. Performance of the genetic algorithm.

a solution . Here, 60 local targets were generated with the search space generated by the trajectories of
∗
the local targets that do not intersect with the set of obstacles; thus, it became an optimization problem to find
an optimal path.

In an environment with previously generated obstacles of 100 m × 100 m, 120 possible targets were randomly
) that do not intersect with the set
generated ; therefore, the search space D would be all trajectories ( ,
of obstacles, where the roadmap genetic algorithm solved the problem of optimization to find a solution to the
problem of path planning. For the problem presented above, we found that the proposed algorithm converged
in 40 iterations. For these results, 100 tests were performed for each number of iterations and, as shown in
Figure 10, the roadmap genetic algorithm converged with greater probability within 40 iterations.

5.2. Application
TheKoalamobilerobotbyK-teamCorporation2013wasused tovalidateourslidingmodeSLAM.Thismobile
robot has encoders and one laser range finder. The position precision is less than 0.1 m.