Tele-Operated Anthropomorphic Arm and Hand Design
In this project, a tele-operated anthropomorphic
robotic arm and hand is designed and built as a versatile robotic arm
system. The robot has the ability to manipulate objects such as pick
and place operations. It is also able to function by itself, in
Firstly, the robotic arm is built in order to interface with a personal
computer via a serial servo controller circuit board. The circuit board
enables user to completely control the robotic arm and moreover,
enables feedbacks from user. The control circuit board uses a
powerful integrated microcontroller, a PIC (Programmable Interface
Controller). The PIC is firstly programmed using BASIC (Beginner-s
All-purpose Symbolic Instruction Code) and it is used as the 'brain'
of the robot. In addition a user friendly Graphical User Interface
(GUI) is developed as the serial servo interface software using
Microsoft-s Visual Basic 6.
The second part of the project is to use speech recognition control
on the robotic arm. A speech recognition circuit board is constructed
with onboard components such as PIC and other integrated circuits. It
replaces the computers- Graphical User Interface. The robotic arm is
able to receive instructions as spoken commands through a
microphone and perform operations with respect to the commands
such as picking and placing operations.
Tele-operated Anthropomorphic Robotic Arm and
Hand, Robot Motion System, Serial Servo Controller, Speech
Learning Monte Carlo Data for Circuit Path Length
This paper analyzes the patterns of the Monte Carlo
data for a large number of variables and minterms, in order to
characterize the circuit path length behavior. We propose models
that are determined by training process of shortest path length
derived from a wide range of binary decision diagram (BDD)
simulations. The creation of the model was done use of feed forward
neural network (NN) modeling methodology. Experimental results
for ISCAS benchmark circuits show an RMS error of 0.102 for the
shortest path length complexity estimation predicted by the NN
model (NNM). Use of such a model can help reduce the time
complexity of very large scale integrated (VLSI) circuitries and
related computer-aided design (CAD) tools that use BDDs.
Monte Carlo data, Binary decision diagrams, Neural
network modeling, Shortest path length estimation.