Download presentation

Presentation is loading. Please wait.

Published bySally Griffith Modified over 2 years ago

1
O PTIMAL P ATH P LANNING FOR M OBILE R OBOT -T RAILER S YSTEMS Team 22: Siwei Wang Xin Yu Xi Li

2
O UTLINE OF P ROJECT Introduction of project (mainly on task description, approach) Explain on GA & Dubins Path. Explain how to group the waypoints, analysis on the experiment. Simulate the whole project with Visual Studio.

3
T ASK D ESCRIPTION

4
B ASIC A PPROACH TSP( Travelling Salesman Problem) GA (Genetic Algorithm) Group the points Dubins Paths

5
T RAVELLING S ALESMAN P ROBLEM Random Path Optimal Path

6
T HE G ENETIC A LGORITHM Global searching method that mimics the natural evolution process to optimize the searching problem. Provide efficient, effective techniques for optimization and machine learning applications Widely-used today in scientific and engineering fields

7
C OMPONENTS OF A GA A problem to solve, and... Encoding technique ( gene, chromosome ) Initialization (creation) Fitness function (environment) Selection of parents (reproduction) Genetic operators (crossover, mutation) Parameter settings (practice and art)

8
S IMPLE G ENETIC A LGORITHM { initialize population; evaluate population; while TerminationCriteriaNotSatisfied { select parents for reproduction; perform crossover and mutation; evaluate population; }

9
GA FOR T RAVELING S ALESMAN P ROBLEM The Traveling Salesman Problem: Find a tour of a given set of waypoints so that each waypoint is visited only once the total distance traveled is minimized

10
E NCODING Permutation Encoding: An ordered list of waypoint numbers. WaypointList1(3 5 7 2 1 6 4 8) WaypointList2(2 5 7 6 8 1 3 4)

11
F ITNESS F UNCTION Reciprocal of the total length L: fitness = 1 / L One individual is more fit than another one if fitness1 > fitness2.

12
S ELECTION Elitism Selection Roulette Wheel Selection

13
Heuristic Crossover Parent1 (3 5 7 2 1 6 4 8) Parent2 (2 5 7 6 8 1 3 4) Child (2 _ _ _ _ _ _ _) C ROSSOVER

14
Heuristic Crossover Parent1 ( 3 5 7 1 6 4 8 ) Parent2 ( 5 7 6 8 1 3 4 ) Child (2 5 _ _ _ _ _ _) C ROSSOVER

15
Heuristic Crossover Parent1 (3 7 1 6 4 8) Parent2 (7 6 8 1 3 4) Child (2 5 7 _ _ _ _ _)

16
Heuristic Crossover Parent1 (3 1 6 4 8) Parent2 (6 8 1 3 4) Child (2 5 7 1 _ _ _ _)....... C ROSSOVER

17
Heuristic Crossover Parent1 (3) Parent2 (3) Child (2 5 7 1 6 8 4 3) C ROSSOVER

18
Reversion mutation Before: ( 5 8 7 2 1 6 3 4) After: (5 8 6 1 2 7 3 4) M UTATION

19
Reciprocal exchange mutation Before: (5 8 7 2 1 6 3 4) After: (5 8 6 2 1 7 3 4)

20
A LTERNATING A LGORITHM - AN ESTABLISHED TECHNIQUE

21
Goal: connecting the waypoints Details: C onnect points in the optimal order ; O dd-numbered edge - straight line; E ven-numbered edge - Dubins-path;

22
E XAMPLE :

23
E XAMPLE ( CON.)

24
W ITHOUT G ROUP W AYPOINTS

25
G ROUP W AYPOINTS

26
Goal: Cover all points ( with suitable circle) Details: E ach circle is independent; A standard circle C r. (according to the trailer) T est whether the current point belong to the last circle

27
A LGORITHM AND R ESULT

28
E XPERIMENT Different algorithm under low waypoint density

29
E XPERIMENT ( CON.) Different algorithm under high waypoint density

30
Q UESTION ?

Similar presentations

OK

►Search and optimization method that mimics the natural selection ►Terms to define ٭ Chromosome – a set of numbers representing one possible solution ٭

►Search and optimization method that mimics the natural selection ►Terms to define ٭ Chromosome – a set of numbers representing one possible solution ٭

© 2018 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Ppt on 2 stroke ic engine efficiency Ppt on alternative sources of energy Ppt on general provident fund Ppt on techniques to secure data Ppt on life of amelia earhart Ppt on symbols of french revolution Ppt on queen victoria Maths ppt on real numbers class 10 Ppt on national stock exchange in india Ppt on the parliament of india