Minimalistic Robot for Mapping and Coverage
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Minimalistic Robot for Mapping and Coverage Supervisors: Dr. Amir Degni Mr. Koby Kohai Students’ names: David Shallom Guy Greenhouse Date: 10/25/2012 Control and robotics laboratory
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Minimalistic Robot for Mapping and Coverage. Supervisors: Dr . Amir Degni Mr. Koby Kohai Students’ names: David Shallom Guy Greenhouse Date : 10/25/2012 Control and robotics laboratory. The Mission. - PowerPoint PPT Presentation
Transcript of Minimalistic Robot for Mapping and Coverage
Minimalistic Robot for Mapping and Coverage
Supervisors: Dr. Amir Degni Mr. Koby Kohai
Students’ names: David Shallom Guy Greenhouse
Date: 10/25/2012
Control and robotics laboratory
The Mission
Mapping and reconstructing an unknown map, using a minimal amount of sensors.
So, how many sensors are required for this
task?
Let’s see how Roomba does it!
IRobot Roomba
Roomba’s Structure
Can we do better?
The project probably won’t save livesBut for some, cleaning might be a nuisance.
Progress so Far
Comprehensive market research. Formulation of three major algorithms.Development of a Matlab GUI simulator that
can emulate the robot unique mechanism.Examination of linear and angular errors’
impact on the quality of coverage, and statistics collection.
First steps towards creating the robot.
The Main Algorithm’s InspirationBranch Prediction
The Main Algorithms
Algorithm 1 – simple trend-keeping movement.
Algorithm 2 – partitioning the map into several connected convex hulls using a trend-shifting movement.
Algorithm 3 – beginning with the second algorithm and after stabilizing – continuing with a random movement (Not implemented yet).
The GUI
The First Algorithm
The Second Algorithm
The Third Algorithm (Hypothesis)
Random lengthRandom angle
Angular error [%]
Angular error [%]
Angular error [%]
Angular error [%]Angular error [%]Angular error [%]
Simulation Results – Angular Error
* Each point in the graph represents an absolute deference between the compared parameter averaged over 100 measurements.
Simulation Results – Linear Error
0 1 2 3 4 5 62.5
3
3.5
4
4.5
5
5.5
6
6.5
7
7.5x 10
4 Area Error
0 1 2 3 4 5 60.002
0.004
0.006
0.008
0.01
0.012
0.014
0.016
0.018
0.02
0.022Eccentricity Error
0 1 2 3 4 5 620
25
30
35
40
45
50
55EquivDiameter Error
0 1 2 3 4 5 625
30
35
40
45
50
55MajorAxisLength Error
0 1 2 3 4 5 612
14
16
18
20
22
24
26
28MinorAxisLength Error
0 1 2 3 4 5 60
0.5
1
1.5
2
2.5Orientation Error
Linear error [%] Linear error [%] Linear error [%]
Linear error [%] Linear error [%] Linear error [%]
Simulation Challenges
Numeric precisionBeing able to determine if a certain corner is
convex or concave.
When do we consider the job as done?Map reconstruction based on the robot’s
memory trace.Maps and polygons’ comparison.
Robot Sketch
Bump sensors
Practical Challenges
The main challenge is to develop a reliable mechanism that matches the simulation and theory as much as possible.
Learning how to interface with the Arduino.
Creating a trusty error-immune system.
Being able to compensate the lack of sensors with extra mechanism.
Plans for the Next Semester
Implement the third algorithm in simulation.Dive into statistics collection over a larger database of unknown maps.Continue developing the robot. Configuring the Arduino micro-processor (“the
brain of the robot”).Creating a relevant error model for the specific
mechanism and updating that model in simulation.Writing an article for IEEE.
Thank You!
