Distributed Search.
Distributed Search with Mobile Sensor Teams.
- Random
- DSA_MST
- CADSA
- DSSA
- Max-sum_MST
- Max-sum_MST with breakdowns
- CAMS
- 7 aforementioned algorithms
- 200 steps to each problem
- 20 problems per map per targets' type
- 4 maps: empy, random, warehouse, room
- 2 types of targets: static and dynamic
- 20 small iterations for CAMS
- 2 metrics: collision (col) metric and remained coverage requirement (rcr) metric
- number of agents is fixed for everything (in small maps - 20, in big maps - )
- SR of agents is fixed for everything
- MR of agents is fixed for everything
- Cred of agents is fixed for everything
- number of targets is fixed for everything (in small maps - 10, in big maps - )
- Req of targets is fixed for everything
- Position of targets fixed while static
- Position of targets changes every 40 steps while dynamic
For one type of target and one map there is the following data structure:
import numpy as np
# per type of targets per map:
data_structure_for_json = {
'alg_name': # (7 of them)
np.zeros((200, 20)) # [[200(steps) x 20(problems) matrix]]
}
map | Static Targets | Dynamic Targets |
---|---|---|
empty (empty-48-48) | ||
random (random-32-32-10) | ||
warehouse (warehouse-10-20-10-2-1) | ||
room (lt_gallowstemplar_n) |