By Alexander Strizhiver Michael Shamis Supervised by Mark Moulin

Download Presentation

By Alexander Strizhiver Michael Shamis Supervised by Mark Moulin

Loading in 2 Seconds...

- 49 Views
- Uploaded on
- Presentation posted in: General

By Alexander Strizhiver Michael Shamis Supervised by Mark Moulin

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Control and Robotics Lab Electrical Engineering Department , TechnionSearch of targets by multiple UAVs using a probability map

By

Alexander Strizhiver

Michael Shamis

Supervised by

Mark Moulin

- Definition of probability map
- Case #1: Single target search using single UAV
(STSU problem)

- Greedy and K-Shortest path algorithms
- Variation of K-Shortest path algorithm for STSU.
- Case #2 : Single target search using multiple UAVs
(STMU problem) – using STSU case solution.

- Case #3 : Multiple target search using multiple UAVs
(MTMU problem) – using STMU case solution.

- A given map of an area can be divided into hexagons
- For each hexagon assigned a probability to find target in it.
- Why hexagon ?

- Definitions:
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:

- Legal sub path
- Essential sub path length:
- STSU problem can then be defined as:

- STSU problem is a very hard problem (seems to be NP-hard problem ).
- The optimal solution for NP hard problems is computational heavy, and not used for real problems.
- STSU problem should be solved by approximate solutions.

- Alternative definition of STSU problem ( simplified – not equivalent ):
- This problem is still hard problem, but have good approximate solutions. All the solutions will try to find .

- Each hexagon in the probability map becomes a vertex on a graph
- There is an edge (V,U) iff the hexagons U and V on probability map are neighbors.

- Greedy algorithm:
C=B

clear collectedPath

while |collectedPath|<F

{

S = all closest neighbors C’ of C with non

zero probability, for which

F-|collectedPath|-dist(C,C’)-dist(C’,B)>=0

C’ = coordinate with largest probability in S

add path form C to C’ to collectedPath

C = C’

}

output collectedPath.

- Advantages:
- Computationally light.
- Gives good results when the probability distribution close to uniform.
- Covers well areas around the base.

- Disadvantages:
- Can go on “wrong path” when probabilities are slightly larger than the surrounding.
- Does not work well when large probabilities concentrated far from base

Given:

G – Graph.

V - Vertex in graph G

K – number of edges

Goal:

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

lightest path.

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))

}

- K-Longest path algorithm has major drawback – the path it finds can get stuck on local maxima.
- The variation we use solves this problem by “remembering” all the vertices which were visited on heaviest path to each vertex.

- Non formal definition of the STMU problem:
Given:

Same inputs as for STSU problem

N – Number of UAVs

Goal:

Find path for each UAV, so that the mean sum of distances all UAVs pass until finding the target islowest.

- Our solution to STSU problem concentrated on finding subsets of :
- The extension for multiple UAVs is trivial – UAVs run on those subsets in parallel.

Non formal definition of the MTMU problem:

- Given:
Same inputs as for STMU problem

P(T2|T1) - conditional probability function for second target location given first target location.

- Goals:
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.

- Using Bayes formulas we can build new probability map:
- Then normalize P’, and supply it as a legal probability map input to the STMU algorithms.

- MTMU problem solved in 2 steps:
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:

- In this project we have reviewed different solutions for the problem of finding targets, given their probabilities map.
- K-Longest path approach in most of the cases gives better results than greedy algorithms
( it finds targets earlier ).

- There is still place for improvement of the solution of the original problem since the problems we solved are far from the “ideal solution” for the problems we looked for.
- It seems to be possible to adjust the K-Longest path algorithm for dynamic targets
- Hybrid of K-Longest path and greedy algorithms can be used to try solving the problem better.