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

Once the training is over, the system will be able to determine the next
direction of movement when all the nine components of the feature vector are
supplied. Here the input sensory information is available for the determination
of next direction of movement.
Procedure Find-direction
Begin
For a given input pattern with nine components of
sensory reading and current direction of movement
Find the neuron of the 2D plane where the
Euclidean distance of the respective nine
components of its weight w.r.t. the same of
the feature vector is minimum;
End For;
Select the 10 th weight as the concluding direction
of next movement for the robot.
End.
24.8 Online Navigation by Modular
Back-propagation Neural Nets
The neural network based navigational model has been realized in [15] by
employing 3 back-propagation [26] neural nets, namely the Static Obstacle
Avoidance (SOA) net, the Dynamic Obstacle Avoidance (DOA) net and the
Decision Making (DM) net, shown in fig. 24.18. The SOA net receives
sensory information and generates control commands pertaining to motions
and direction. The DOA net, which works in parallel with the SOA net,
generates directions of motions from the predicted motion of obstacles. The
primary plans for motion generated by these two nets are combined by the
AND logic to determine the common space of resulting motion. A final
decision about the schedule of actions is generated by employing the DM net,
which acts upon the resulting plan of motion generated by the AND logic.
Each of the SOA, DOA and DM models are realized on three layered
neural networks, and are trained with well-known back-propagation
algorithms (fig. 24.18 (a) and (b)). The sample training patterns for the SOA,
DOA and DM nets are presented in tabular form (vide table 24.1 – 24.3),
where the entries under inputs denote the cell numbers surrounding the Robot
(vide fig. 24.18 (b)). The tables are self-explanatory and thus need no further
elaboration. Readers, however, should try to explain the table on their own
from the point of view of current position of static obstacles and predicted
direction of movement of the dynamic obstacles.