Page 61 - Innovations in Intelligent Machines

50 P.B. Sujit et al.
Figures 2(b) and 2(d) shows that the team theoretic strategy performs
better than the other strategies. Another study examines the effect of sensor
radius on T d (Figure 2(c)). Here, we considered a random target map and
carried out three simulations for each sensor radius. The effect of sensor radius
shown is the average of the three simulations. The figure shows that for this
particular case sensor radius of about 25 gives the best performance compared
to any other sensor radius. The performance of team theory, greedy and full
communication strategies depends on the sensor range. If the sensor radius
is small, a UAV can sense very small area and the decision taken will not
be effective. We expect that with increase in sensor range the performance
will also improve. In the case of team theory, this is not true because if we
consider a large sensor range, the estimated value of the virtual target will
be incorrect. This is because the area sensed by the k th UAV can include
regions beyond the search region space where there are no targets. But, the
i th UAV does not consider this fact and assumes equal density of targets
everywhere. This unnecessarily gives more weightage on the virtual target
and the overall performance decreases. This effect can be seen in Figure 2(c).
This problem can be resolved if we consider other parameters such as target
density gradients or restriction to the search space.
The ratio of search value to the target value also plays a crucial role. If we
give equal priority to search and attacking a target then the UAV may opt
for search task even though there is a target near it. On the other hand, if we
increase the value of the target then there is a possibility that the UAV may
loiter in the vicinity of a target which is already destroyed. In our simulations,
we considered the search value to be 25% of the target attack value and this
yielded good results. But, a more focused study is necessary to examine this
aspect of the problem.

4 Task Allocation using Negotiation

In this section, we present a task allocation algorithm for multiple UAVs
performing search and attack tasks in an unknown region using negotiation
scheme for the scenario given in Section 3. Here we assume that once a target
is attacked, it is destroyed and hence battle damage assessment task on the
target is not necessary to be performed. This is one of the very few applications
available that exploits the use of negotiation for a network of UAVs involved
in a practical problem of decision-making.

4.1 Problem Formulation

Consider N UAVs/agents performing a search and destroy operation on a
bounded region consisting of M targets whose exact positions are not known
a priori. The basic problem of task allocation is to efficiently assign agent
A i ∈ N, to target m i ∈ M, such that the mission is completed as quickly as