1. Design and Implementation of
Artificial Neural Networks in Semi-
autonomous Robotic System
John Lau Kah Soon (7D3A1916 / 12753189)
Supervisor: Terence Tan END

2. Summary
• Problems Identification
• Methodology
• Solutions & Achievements• Solutions & Achievements
• Conclusions & Future Direction
END

3. Problems Identification
Problems
How to build a semi-autonomous robotic system ?
Most ANN implementation Software
Can ANN be implemented in Hardware ? How ?
Problems
How to design ANN for robotics application ?
How to embed ANN into semi-autonomous robotic system ?
Embedded system  General-purpose computer ?
END

4. Methodology
Pseudocode
MATLAB
Hardware Setup
[1] Boe-bot (BS2, BoE)
[2] eb500
Java (SE 5.0)
[1] Communication APIVHDL
Start
Microcontroller
Technology
[1] Communication API
[2] FreeTTS API
[3] StandardSerialPort.java
[4] Utils.java
PBASIC
ANN Controller Chip on
Xilinx Spartan-3E board
VHDL
Schematics
Heimdrix.bs2, Heimdrix_Type_0.jar
Heimdrix Type 0 System
Future R&D
PLD (FPGA)
Technology
UDM
Incremental
Process
Model
END

5. Heimdrix Type 0 System
Basic Stamp 2
(Heimdrix.bs2)
Mobility
Subsystem
IR Object
Detection
Subsystem
[2], [3]
Wireless
Communication
Subsystem [1]
Bluetooth
USB
Alarm
Subsystem
Embedded
System
CMM
Perceptron
RICMMRICMM
IR Object
Detection
Subsystem
Mobility
Subsystem
Neural Networks
Subsystem
[a], [c], [d]
[a], [d]
SOFTWARE
ANN
HARDWARE
ANN
Conclusion
Keyboard
USB
Dongle
General-purpose
Computer
(Heimdrix_Type_0.jar)
Heimdrix.java
NeuralNetworks.javaStandardSerialPort.java
Utils.java Keyboard.java
CMM.vhd
CustomCustom--mademade
ANNANN
ControllerController
ChipChip
‘3’ ‘7’[1] [2] [3]
Telemetry
[a] Wireless Comm. Subsystem (Software)[a] Wireless Comm. Subsystem (Software)
[b] Concurrent GUI[b] Concurrent GUI--based Command Subsystembased Command Subsystem
[c] Error Detection Subsystem[c] Error Detection Subsystem
[d] Self[d] Self--repair Subsystemrepair Subsystem
[e] Speech Subsystem[e] Speech Subsystem
Association with
Semi-autonomous
Robotic System
[b]
[a], [b], [c], [e]
Logging &
Conversion
purposes
END

6. Definition BACK
END

7. RICMM BACK
0.0081 0.9575 0 1 0000 1111ˆ
0.9339 0.0198 1 0 1111 0000
− −     
= ≈ =     − − −     
M
RICMM CMM Hardware representation
END

8. ANN Controller Chip BACK
END

9. Conclusions & Future Direction
Conclusions
ANN + Robotics
Concurrency
RICMM 6 gates
XOR, Random
Software ANN
Hardware ANN
Efficient
Methodology
Rigorous
END
Future
Direction
ANN Scalability
Rigorous
Mathematical
Analysis
More Sensors
And Servos
Upgrade
Fuzzy Logic
Genetic Algorithm
Space ExplorationSpace Exploration
Rescue OperationsRescue Operations
Oil & Gas IndustryOil & Gas Industry
Mining IndustryMining Industry
ServicesServices
SecuritySecurity

10. The End
Q & A Session
1
2
3
4
5
6
7
8
9
Cover Page
Summary
Methodology
Heimdrix Type 0 System
Problem Identification
Definition
RICMM
ANN Controller Chip
Conclusions & Future Direction