Mobile Robot Navigation on Partially Known Maps Usign the a Star Algorithm-muntean Paul

8/3/2019 Mobile Robot Navigation on Partially Known Maps Usign the a Star Algorithm-muntean Paul

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Mobile Robot Navigation on Partially

Known Maps using the A* Algorithm

Paul-Ioan MUNTEAN

Technical University of Cluj-Napoca, RomaniaFaculty of Automation & Computer Science, Department of Automation & Applied Informatics

http://aut.utcluj.ro

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Outline:

1. Introduction

2.Testing the A* Algorithm

3.Developping the Path Planning application

4.Testing the Path Planning app. On the P2 simulator

5.Testing the Path Planning application on P2

6.Future improvements of the Path P. Algorithm

7.Conclusions

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Introduction

Pros and contras for using the A* Algorithm.

Cons:

it uses huge amounts of memory to

keep the data of proceeding nodes.

Limited regarding path planning in

3D space but optimized it copes even

with this problem.

A* must insert nodes into a sortedOpen List (the cost of the insertion

can be logarithmic in the length of the

list).

Pros:

Offers advantages of Dijkstra

algorithm and Best-First-Search.

It has many optimized variants

like the bidirectional A* or D*.

It can be easilly abstracted and

be used in many fields of studylike computer games ex: Counter

Strike ,bots path planning.

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Introduction

A* Algorithm description.

The A* algorithm uses a best-first search and finds the least-cost path from a

given initial node to one goal node (out of one or more possible goals).

The path-cost function, which is the cost from the starting node to the current

node (usually denoted g(x)) and an admissible "heuristic estimate" of the distance

to the goal (usually denoted h(x)). F=G+ H (1)

Starting with the initial node, it maintains a priority queue of nodes to be traversed,

known as the open list.

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Offline Testing of the A* Algorithm.

Characteristics of the application

Guarantee high accuracy using Kernel32.dll

The results have four decimals

The path from start to goal position is the shortest

Figure2. Path Determined from start to goal

Testing the A* algorithm by using different algorithm parameters.

One cell equals ten centimeters

Figure1. GUI of the A* testing application

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Testing the A* algorithm by using different algorithm parameters.

T.N. H D F. P. F. T [sec]

1 0 y yes m 0.0975

2 1 y yes m 0.0302

3 2 y yes m 0.0104

4 3 y yes m 0.0033

5 4 y yes m 0.0034

6 0 y yes M(x,y) 0.1025

7 1 y yes M(x,y) 0.0494

8 2 y yes M(x,y) 0.0275

9 3 y yes M(x,y) 0.0173

10 4 y yes M(x,y) 0.0110

11 5 y yes M(x,y) 0.0108

12 6 y yes M(x,y) 0.0087

13 7 y yes M(x,y) 0.0075

14 8 y yes M(x,y) 0.0078

15 0 y yes D. S. 0.108

16 1 y yes D. S. 0.0286

17 2 y yes D. S. 0.0114

18 3 y yes D. S. 0.0034

19 4 y yes D. S. 0.0037

20 0 y yes E 0.1120

21 1 y yes E 0.0499

22 2 y yes E 0.0254

23 3 y yes E 0.0205

24 4 y yes E 0.0146

25 5 y yes E 0.0153

26 0 y yes SQR 0.1235

27 1 y yes SQR 0.0155

28 2 y yes SQR 0.0012

29 3 y yes SQR 0.0011

30 4 y yes SQR 0.001531 0 y no m 30

32 1 y no m 1.3443

33 2 y no m 0.0124

34 3 y no m 0.0322

35 0 y no M(x,y) 10.685

36 1 y no M(x,y) 2.7441

37 2 y no M(x,y) 30

38 0 y no D. S. 30

39 1 y no D. S. 1.3152

40 2 y no D. S. 0.0358

41 3 y no D. S. 0.345342 0 y no E 25.305

43 1 y no E 2.2041

44 2 y no E 30

45 0 y no SQR 25.966

46 1 y no SQR 30

Table 1.Test Results for A* vs A* Fast Table 2.Test Results for A* vs A* Fast

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Implementation of the Path Plannig program

Figure3. Overview of the s. implementation Figure4. ArRobot task tree

Real RobotCommunication protocol

ArRobot Class ,(send control Commands and receive

sensor data (odometry, sonar) from the robot)

Control Algorithm

UI

SIP (Server Information packet)

The branches priority

Timeout

Reality two more(action handling and

state reflection) (Action handling handles theinternal actions which can be added to this branch andstate reflection updates the robots internal information. )

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From Code to Class Diagram to Path Planning app.A* algorithm pseudo code

Components of the Class Diagram

Componets of the GUI

Loaded Map,Partialy known.

Next Step Simulation with the Pioneer Simulator.

Implementation of the Path Plannig program

Figure7. GUI of Path P. ApplicationFigure6 . Class Structure of p.p. app.Figure5. A* algorithm

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Path Planning with the Pioneer Robot Simulator.

Figure 8. Map loaded in the

Path Planning Application.

Figure 9.First Map loaded in the

Pioneer Robot Simulator

Figure 10.Second Map loaded in

the Pioneer Robot Simulator

7.55x8.46meters , 1:120 scale7.55x8.46meters , 1:120 scale 7.55x8.46meters , 1:120 scale

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Path Planning with the Pioneer Robot Simulator.

Two unknown environments.

Handling partially unknown maps.

Real world environment.

Test run Fast Mode Map Time [sec]

1 Yes S.Map 40

2 No S.Map 45

3 Yes F.Map 61

4 No F.Map 47

Table 3.Test results for the path planning application.

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Online Testing of the A* Algorithm.

Path Planning with the real Pioneer 2 Robot.

The path planning application communicates directly with the real Pionner 2 Robot.

As testing area the C12 laboratory was used with an obstacle which was not present on the map loaded in

the simulator.

The starting position of the robot is the left lower corner of the room where we consider to have 0 on the x

axis and 0 on the yaxis.The initial coordinates of the robot when it starts are the same so it is of big

importance to have an agreement betwen the starting coordinates of the robot and the (0,0) coordinates of

the map.

The robot is set there and after selecting the starting position on the map which coresponds to the actual

position of the robot we select the desired position on the map where we want the robot to go.

Along his path it is placed a 15 x 50 x 50 box which represent the unknown obstacle. The robot folows the

path and when it reaches the obstacles after a few turns and action needed to avoid it passes the obstale

and continuos on the path to the final destination.

The goal possition is reached and then the robot stops.

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Figure11. C12 Laboratory on Dorobantilor Street. Positionof the robot.

Start and Stop positions.

Red line, path.

The PC Desktop.

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Figure 12.Path planning with the Pionner 2 Robot Figure 13.Going between the desks

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Path Planning with the real Pioneer 2 Robot, other tests.

Figure 14.Going to the entrance dor. Figure 15.Going from start to goal position and back

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Possible Improvements of Path Planning

A distrubuted path planning algorithm

Figure16. Map composed of cells

Characteristics of the Multi Cell Area

BSCHandshake procedure

Controller knows only his area

Vehicle moves across the cells

Figure17. Control S. with Coordinator.

Figure 18. GUI of Robot Com

Result:The Pioneer 2 can be controlled in a

multi distributed controll manner.

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Conclusions

The process of developing a path planning application it is a gradual process

starting with algorithm understanding ,determining the limits of the A*,simulating

the path planning algorithm on the Simulator, simulation on the real robot andcontinuous tuning of the application;

Using more advanced sensors and methods of interpreting raw data from them

could be the key for obtaining a system that is capable to cope with more

demanding terrains.

Sensor fusion which can be helpful in obtaining a better result.

Future improvements can be made by using the Distributed Control application.

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Special Thanks:

To: supervisor Radu Robotin.

-forcontinuous help andguidance.

Department of Automation.

Thank You for Your Attention!

Paul I. Muntean

Mobile Robot Navigation on Partially Known Maps Usign the a Star Algorithm-muntean Paul

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