Artificial Intelligence and Soft Computing: Behavioral ... - Arteimi.info

It may be noted that the neighborhood relationship of the partitioned
modules is not directly reflected in the tree we constructed. There is an
interesting algorithm [27] to find the neighbors of a node in the tree, but we
do not present it here, for it requires a lot of formalization. We just presume
that the map is partitioned into modules and the robot can safely move from
one module (leaves of the tree) to another. But how do we find the shortest
path between two given modules? The heuristic search will give an answer to
this question. In fact the A* algorithm that reduces the sum of the cost of
traversal from a starting point to an intermediate point (module), and the
predicted cost of reaching the goal from the intermediate point to the goal, is
used in our simulation (see Appendix A) to select the intermediate points.
Here, we used the Euclidean distance measure to estimate the distance
between two partitioned modules.
24.6.2 The GA-based Approach
Michalewicz [16] first successfully applied GA [6] in navigational planning
of mobile robots. In one of their recent papers [29], they considered the data
structure of fig. 24.13(a), for the chromosome in path planning problem,
where the first two field: xi, yi, 1≤ ∀ i ≤ n denote the co-ordinate of the robot,
the third field bi denotes whether the point is on an obstacle and the fourth
field denotes a pointer pointing to the next node to be visited.
X1 x2 b1 X2 y2 b2 Xn yn bn
Fig. 24.13(a): A chromosome, representing a path from the starting point
(x1, y1) to the goal (xn, yn) through a number of points.
They considered the standard crossover operation that could be applied
between two chromosomes and the cross-site could be selected as the region
between two structures within a chromosome. Thus one can change the
current sub-goal by executing a crossover operation. They also devised a
number of mutation operations to improve smoother paths or to delete a point
on the path to avoid its overlap with an obstacle. For details, the readers may
refer to [30]. The fitness function in their GA-based planner comprises of
three factors: i) finding the shortest path between the starting and the goal
point, ii) the path should be smooth and iii) there must be sufficient clearance
between the obstacles for the easy movement of the robot through the path.