Control and Robotics Lab Electrical Engineering Department , Technion Search of targets by multiple UAVs using a probability map. By Alexander Strizhiver Michael Shamis Supervised by Mark Moulin. Presentation outline. Definition of probability map
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Control and Robotics Lab Electrical Engineering Department , TechnionSearch of targets by multiple UAVs using a probability map
(STMU problem) – using STSU case solution.
(MTMU problem) – using STMU case solution.
P – probability map ( probability function ).
- Location of UAV base
- Location of target drawn according to P.
F - size of UAV fuel tank.
- Legal search path, that is an ordered set of point on probability map which satisfy following properties:
S = all closest neighbors C’ of C with non
zero probability, for which
C’ = coordinate with largest probability in S
add path form C to C’ to collectedPath
C = C’
G – Graph.
V - Vertex in graph G
K – number of edges
Find weight of “lightest” (shortest) path from V to all the vertices in graph G with K edges exactly.
Lightest path here means – that sum of all the weights is smallest.
K-Longest path algorithm - algorithm that finds heaviest
( longest ) path instead of
Here is a version of K-longest path that matches our needs better
(find longest path with K or less edges) :
For each vertex u
Path(0,u) = -infinity
Path(0,V) = 0
For k=1 to max_k
for each vertex v
path(k,v) = path(k-1,v)
for each vertex v
for each u neighbor of v
path(k,u) = max(path(k,u),path(k-1,v)+cost(u))
Same inputs as for STSU problem
N – Number of UAVs
Find path for each UAV, so that the mean sum of distances all UAVs pass until finding the target islowest.
Non formal definition of the MTMU problem:
Same inputs as for STMU problem
P(T2|T1) - conditional probability function for second target location given first target location.
Find path for each UAV, such that the mean sum of distances all UAVs pass until finding one of the targets is lowest.
When one of the targets is found then find a path for each UAV, such that the mean sum of distances all UAVs pass until finding the second target is lowest.
1) Run STMU algorithms on P’.
2.a) If target #1 found - run STMU algorithms again with probability map P(T2|T1)
2.b) If target #2 found – run STMU algorithms
again with probability map P(T1|T2) which is given by:
( it finds targets earlier ).