Transcript
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ROBOTIC ARM AND ITS CONTROL
ABSTRACT
In this project, we design and build a versatile robotic arm system. The arm has the
ability to manipulate objects such as pick and place operations. Firstly, the robotic arm is
built in order to interface with a prosthetic control 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 primarily programmed using assembly programming
language and it is used as the „brain‟ of the arm.
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. The robotic arm is able to receive instructions as spoken
commands through a speech recognition system via a microphone and perform operations
with respect to the commands such as picking and placing operations.
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1. INTRODUCTION
An upper limb myoelectric prosthetic arm is an aid that tries to give a chance of a
better quality of life to disabled people. It tries to give back some of the natural and
fundamental functions of a physiological human arm, even if the movements that is able
to perform are not so deeply similar to those of a natural arm. To control such a device,
several ways are possible. The more traditional one is nowadays the EMG
(electromyography) control, which is based on EMG signals extracted from surface
electrodes of user‟s arm or forearm, while the simplest technique is using buttons or
switches when the electromyography activity of patient muscles is not so good or clear.
Lately, in the last decade more complex ways were explored to widen the range of
possible input sources for the controller of a prosthetic arm, so neuro cortical control ,
foot control with wireless wearable insoles , control with implantable myoelectric sensors
(IMES), with MMG sensors (mechanomyographic) and ultrasonic sensors were
investigated, even if is not clear if these techniques are really used by patients in their
everyday life or if they are just interesting theoretical contributions in the wide field of
prosthetic arm control. All these techniques start from the assumption that the prosthetic
motion is directly “linked” to the human motion, the source being both the EMG activity
or the foot motion or something else. Since the most common control scheme for an upper
limb prosthetic arm is a sequential control (where signals or switches are used to change
control from one degree of freedom to another), it follows that all these techniques have
the same problem: when the patient has to perform a complex task, formed by a
predetermined and precise sequence of movements, he has to do a precise sequence of
contractions/movements, always remembering which motor is selected in every instant of
time. This is not as simple as one can believe, especially when there are more degrees of
freedom (typically three for a transhomerus or a shoulder disarticulated patient: the
flection/extention of the elbow, the prono/supination of the wrist and the opening/closing
of the hand).
A myoelectric prosthesis uses EMG signals or potentials from voluntarily
contracted muscles within a person's residual limb on the surface of the skin to control the
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movements of the prosthesis, such as elbow flexion/extension, wrist supination/pronation
(rotation) or hand opening/closing of the fingers. Prosthesis of this type utilizes the
residual neuro-muscular system of the human body to control the functions of an electric
powered prosthetic hand, wrist or elbow. This is as opposed to an electric switch
prosthesis, which requires straps and/or cables actuated by body movements to actuate or
operate switches that control the movements of prosthesis or one that is totally
mechanical. It is not clear whether those few prostheses that provide feedback signals to
those muscles are also myoelectric in nature. It has a self suspending socket with pick up
electrodes placed over flexors and extensors for the movement of flexion and extension
respectively.
Let consider the following case: a patient has to bring a bottle and pour water into
his glass. The sequence of contractions that he must do, in the case of sequential control
of the motors and thinking about three sources of emg signal, is illustrated in table 1:
TABLE I
From the table above we can understand that even if the motion task seems to be
very easy, the patient has to do a precise sequence of twelve contractions to perform it. If
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we think that now, in some cases, there is the idea to add, besides the elbow, the wrist and
the hand motors, also a shoulder motor group with two motors, one for the intra-extra
rotation and one for the elevation-adduction, (so adding four possible movements, with
other four sources of emg signals) it is clear that controlling the prosthetic device only
with emg signals could become more and more difficult, also because the possible EMG
sources located in the muscles near the amputation line are not utilizable, due to a bad or
insufficient EMG activity. For this reason we thought to another alternative input source,
potentially efficient and easy to be used, and overall disconnected from the human body
motion. In particular we focused on the voice control.
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2. HOW DOES THE MYOELECTRIC ARM WORK?
Electric prostheses use small electric motors to move the replaced limb. These
motors can be found in the terminal device (hand or hook), wrist and elbow. An
electrically-powered prosthesis utilizes a rechargeable battery system to power the
motors. Since electric motors are used to operate hand function, grip force of the hand is
significantly increased in comparison to earlier functional prostheses, often in excess of
20-32 pounds (Motion Control).
There are many ways to control an electrical prosthesis, one of the more popular
being myoelectric control. Whenever a muscle in the body is contracted, or flexed, a small
electrical signal called an EMG in the range of 5 to 20 microvolts is created by a chemical
interaction in the body (Animated Prosthetics). A typical light bulb uses 110 to 120 volts,
so the signal generated by the body is less than a millionth of the strength of a light bulb
(Animated Prosthetics).
One of the key components of the myoelectric arm is the electrode attached to the
surface of the skin to record the EMG signal. Once recorded, the signal is amplified, then
processed by a controller that switches the motors on or off in the hand, wrist, or elbow to
produce movement and function (Animated Prosthetics).
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Not everyone can wear the myoelectric arm. Users must be able to produce an
EMG strong enough to be recorded and sufficiently amplified. Users must also be able to
separate muscle contractions. Separating contraction means that when one muscle is
contracted, the opposing muscle is relaxed. If both muscles were contracted at the same
time (co-contraction), the controller would receive signals to both turn the motor on and
off at the same time. This would signal the hand to open and close simultaneously,
resulting in no function.
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2.1 ADVANTAGES AND DISADVANTAGES
There are several advantages to wearing an electric prosthesis like the myoelectric
arm. Most people prefer this type of control because non-electric prostheses are often
laborious to operate, whereas simply flexing a muscle can control myoelectrically
powered prostheses. They eliminate the need for the tight harness amputees have to wear
if they choose a non-electric prosthesis. Since electric prostheses do not have to utilize a
control cable or harness, cosmetic skin made of silicon or latex can be applied to the
prosthesis, greatly enhancing the cosmetic restoration (Advanced Arm Dynamics).
Perhaps the greatest advantage of the myoelectric arm is the operational range. It
can be used over the head, down by the feet, and out to the sides of the body. Such
movements are nearly impossible with cumbersome, non-electric prostheses.
Unfortunately, the myoelectric hand is not perfect. One of the major inconveniences
of electrically powered prostheses is the required battery system. Such a system needs a
certain level of maintenance, including charging, discharging, and the eventual disposal
and replacement of the battery. Electrically powered prostheses also tend to be heavier
than other prosthetic options due to the weight of the motor and batteries. However,
advanced suspension designs have minimized the weight greatly.
Another disadvantage is potential malfunction of the arm, resulting in costly
repairs. Wearers also have to be very cautious around water. Severe damage to the motor
and controller can result from water exposure.
Cosmetically there seems to be no disadvantages over traditional prostheses. Yet
under extreme conditions, latex covered prostheses are prone to staining, so several
coverings may be necessary throughout the device's lifetime.
There are several companies that currently produce the myoelectric arm, including
Motion Control, Otto Bock Orthopedic Industry, Hosmer, and Liberty and Technology
Prosthetics and Orthopedics.
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3. BASIC BLOCK DIAGRAM OF THE SYSTEM
MIC
3.1 USING THE VOICE TO CONTROL A DEVICE
Nowadays using the voice to control an electronic device is a quite common
process, and there are several electronic equipments that can be commanded by voice,
such as telephones, surgery robots, wheelchairs, military devices and so on. A voice
recognition system is composed by an input device, typically a microphone, and an
intelligent core that performs the recognition operations, which are, for the most part,
software elaborations of the signal acquired from the input device. Explaining the
recognition techniques is not the aim of the paper, but information can be found in. In this
work we used the voice recognition system in which the core is the HM 2007 IC by
Hualon. The board is connected to an embedded hardware which is the control board of
the prosthetic device, which acquires the inputs voice signals, elaborates them and
performs the motor actions requested by the patient.
The voice recognition process is articulated in two different phases: the first one,
called training phase, where the module is taught with the words that it must recognize,
and a second phase, the standard operation, where one pronounces a word and the module
compares it with the stored words and decide which of them the most similar one is. The
module is programmed in voice dependent mode, so it can recognize a word only if it is
pronounced by the same person that has done the training phase. In this context, the voice
command is not intended to completely substitute the traditional EMG control, but just to
join it, to expand the possibilities of controlling the device, and to simplify the control
process in case of complex and repetitive motion tasks.
VOICE RECOGNITION SYSTEM PROSTHETIC CONTROL
BOARD
To the
arm Fig 3: BASIC BLOCK DIAGRAM OF THE SYSTEM
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3.2 VOICE RECOGNITION
Speech recognition is the process of converting an acoustic signal, captured by a
microphone or a telephone, to a set of words. The recognized words can be the final
result, as for applications such as command & control, data entry, and document
preparation or retrieval. The basic assumption of the whole word pattern matching
approach is that different utterance of the same word by a particular talker result in similar
patterns of sound. There will be variation in spectrum shape at corresponding parts of the
patterns from the same word. There will also be variations in the time scale of the
patterns, and this will make it difficult to compare corresponding parts.
The basic building block of speech is the phoneme. There is one phoneme for every
basic sound in the language. For example, the word 'cat' is constructed from three
phonemes -'k', 'a' and‟t‟. A Speech Recognition Engine will need to construct the
sequence of the phonemes in the speech, before it can produce the sequence of words.
This is typically carried out in a number of distinct stages.
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ADC PARAMETER
EXTRACTION OUTPUT
DEVICE
TEMPLATE
MEMORY
PATTERN
MATCHING
3.3 VOICE RECOGNITION SYSTEM
Voice recognition involves inputting of information in to a computer using human
voice and the computer listening and recognizing the human speech. Voice recognition is
still being actively researched as problems posed are more difficult than those of speech
synthesis. Thus, successful commercial speech recognition systems are few and far
between the more successful ones are speaker dependent single-work systems. Such
systems operate in one of two modes. In the training mode the user trains the system to
recognize his/her voice by speaking each word to be recognized in to a microphone. The
system digitizes and creates a template of each word and stores this in its memory. In the
recognition mode each spoken word is again digitized and its template compared with the
templates in memory. When a match occurs, the word has been recognized and the system
informs the user or takes some action. The performance of such systems is affected by
speakers not passing long enough after each word, background noise, and how clearly and
carefully the work is spoken. The two important DSP operations in a recognizer are
parameter extraction, where distinct patterns are obtained from the spoken word and used
to create template and pattern matching where the templates are compared with those
stored in memory; see fig.
For most people, voice is the most natural form of communication, being faster than
writing or typing. Thus, in the office environment, voice systems now exist which allows
application programs to be driven by voice commands instead of by keyboard entries.
Systems which will allow the usual office documents, such as letters and memos, to be
Fig 4: BLOCK DIAGRAM OF VOICE RECOGNITION SYSTEM
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created and sent by voice are envisaged. Word recognizers are being incorporated in to
consumers products, such as voice operated telephone dialing systems, and are used in
voice activated domestic appliance for disabled people with limited movement. This
increases their independence by enabling them to perform simple tasks such as turning
on/off lights, radio or TV.
There are of course numerous potential applications of voice recognition. However,
it appears that future advances in this area will rely significantly on artificial intelligence
techniques because of the need for machines to understand as well as recognize speech.
A speech recognition control system capable of controlling the robotic arm using
voice commands is also constructed, where hands-free operation is desired. The ability to
communicate with a robot through speech is the ultimate user interface. When a robot
obtains the ability to recognize words, it is well on its way to becoming a true humanoid.
This speech recognition control circuit to be built provides a simple and effective means
for humans to specify a task for the robot to acquire new skills without any additional
hard coded programming. Robots have become important over a wide range of
applications--from manufacturing, to surgery, to the handling of hazardous materials.
Consequently, it's important to understand how they work, and what problems exist in
designing effective robots.
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3.4 VOICE RECOGNITION CHIP
HM2007
HM2007is a single chip CMOS voice recognition LSI circuit with the on-chip
analog front end voice analysis, recognition process and system control functions. A 40
isolated-word voice recognition system can be composed of external microphone,
keyboard, 64K SRAM and some other components .Combined with the microprocessor,
an intelligent recognition system can be built.
FEATURES
Single chip voice recognition CMOS LSL
Speaker-dependent isolates-word recognition system.
External 64K SRAM can be connected directly.
Maximum 40 words can be recognized forODCchip.
Maximum 1.92 sec of word can be recognized,
Multiple-chip configuration is possible.
A microphone can be connected directly.
Two control modes are supported: Manual mode and CPU mode.
Response time : less than 300 ms.
5V single power supply.
48-pin PDIP, 51 pin PLCC. 48 pad bare chip.
TC8860F
The voice recognition chip used for processing the input speech is TC8860F. It is a
single chip LSI with onchip circuits and functions required for voice recognition including
analog circuit, registration RAM, and pattern matching function. It is possible to construct
a voice recognition system only by externally connecting a microphone and keyboard to
this LSI. The chip can be operated in manual/CPU mode. In manual mode of operation, a
4 x 3 keypad matrix is used for inputting the commands to the chip. The chip has a 4Kbit
volatile built in SRAM.
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FEATURES OF TC8860F:
Single chip voice recognition LSI
Speaker dependent word recognition system
Linear matching system
No. of words that can be registered: Max 10 words
Response time is Max 0.60sec, average 0.35sec
Input voice time length allowed: 0.16 ~ 0.96sec
Built-in 4Kbit RAM for registration
A microphone for inputting the voice
Built-in 800KHz oscillator circuit
5V single power supply
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3.5 COMPARISON OF DIFFERENT VOICE RECOGNITION-
CHIPS
Chip Manufacturer SNR in dB Cost
TC8860F Toshiba 30dB 15$
HM2007 Huilon <10dB 25$
RSC-64 Sensory devices 15dB 20$
TMS-320C2X Texas Instruments 25dB 30$
Table 2: Table for comparison of different chips available
The comparison of different speech recognition chips yields us the information that
HM2007 is having an average signal to noise ratio. Apart from the cost of the chip, its
availability was given more importance compared to its counterparts. This is the reason
why it is selected for designing the voice controlled prosthetic arm project. The pin out of
the HM2007 IC is given in the figure below.
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Fig 5: PIN OUT OF HM 2007 IC
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4. MOTION SYSTEM USING VOICE RECOGNITION
CONTROL
The speech recognition circuit functions as a standalone circuit and it works
independently. Words are recognized through interrupt operations whereby the
recognition line is connected to the robot‟s interrupt lines. This is much better than using
polling operation that causes CPU overhead. The main component of the circuit is the HM
2007 speech recognition chip. The HM 2007 chip is a CMOS voice recognition chip with
voice analysis, recognition process and system control functions. The other major
components are the 64K CMOS Static RAM chip, microphone, 12 button-keypad and
74LS373 chip. Data can be written and read from the SRAM chip and the 74LS373
functions as a latch with 3-state outputs. There are also two BCD to 7-segment converters
used to display the output the words recognition. It functions as an indicator to user as the
circuit is working properly. The circuit is a speaker dependent system whereby it is only
able to recognizing the individual that train the circuit. It a capable of providing high
functioning output as high as 95% accuracy. However, there is constraint of the circuit
Fig 6: CIRCUIT DIAGRAM FOR SPEECH RECOGNITION SYSTEM
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concerning the style of speech it can recognize. For example, it can only recognize words
that spoken separately with pause in between each word.
It is programmable to recognize 40 unique words of 0.96s length and a maximum of
20 words of 1.952s length. The length of the words affects the number of words able to be
store in the 8K x 8 static RAM chip. The circuit is able to detect voice as far as one foot
from the microphone. This speech circuit provides many advantages compared with other
circuits as the response time is less than 300 ms, it requires only a 5 V DC power supply
and it can support CPU mode and manual mode whereby the manual mode is connected
to a keypad and CPU mode is connected to a microcontroller
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4.1 TESTING AND TRAINING OF SPEECH RECOGNITION
CIRCUIT
The circuit shown in Fig. 6 was constructed into on breadboard for testing and
training. Testing and training the HM 2007 chip in manual mode requires the keypad and
microphone. When the circuit is powered on, the HM 2007 checks the static RAM and
display “00” on the 7-segment and also lights the LED. The system is in ready state and
ready to be trained. Training procedures of the circuit includes:
1. Press “01” and the 7-segment will display “01”. Led will turn off.
2. Then press “train” and Led will on again.
3. Hold the microphone close to user and say training word.
4. If word is recognize by circuit, Led will blink.
5. Repeat the training word and “01” will be display if word is accepted.
6. Continue training with other words and train from 02” to a maximum of “40”.
The output is connected to a PIC microcontroller to read the all the 8-bit outputs
from the circuit. The 8 outputs are taken from the output of the 74LS373 latch. The PIC is
then connected to the serial servo controller circuit and control the movement of the 8
servo motors. The circuit was constructed a few times and troubleshooting was done by
ensuring all connections are correct and all necessary pin connections are connected.
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5. PROSTHETIC CONTROL BOARD
The output from the speech processing board is allowed for controlling of servo
motors. The programmable Interrupt Controller (PIC) is used for actuating the motors.
The PIC is being programmed using MPLAB-IDE. Assembly language is written in this
workbench and written into the PIC using a hardware called INCHWORM
PROGRAMMER provided by MICROCHIP (Manufacturer of PIC). The programmer
hardware is shown below.
An ordinary human arm consists of the following parts:
Upper arm
Elbow
Wrist
Fingers
The robotic arm is designed to be similar with a human arm with nine degrees of
freedom where each part of the arm is actuated with servo motors.Our objective is to
attain all the degrees of freedom. This is accomplished using high torque motors. Among
different types of DC motors available, servo motors have higher torque capacity. This
enables the prosthetic arm to do jobs in close relation with an ordinary arm. The table
Fig 7: INCHWORM PROGRAMMER
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shown below gives a clear idea about the relation between output combinations of speech
processing system with that of the arm movements. The robotic arm is controlled by the
control board which is based on the PIC 16F877A, a type flash programmable controller.
The main objective of designing using a microcontroller is that a large amount of
electronics needed for certain applications can be eliminated.
BIT
COMBINATIONS
B3 B2 B1 B0
ARM MOVEMENTS
0 0 0 1 Shoulder motor right
0 0 1 0 Shoulder motor left
0 0 1 1 Elbow motor up
0 1 0 0 Elbow motor down
0 1 0 1 Wrist motor right
0 1 1 0 Wrist motor left
0 1 1 1 Plunger for finger movement (IN)
1 0 0 0 Plunger for finger movement (IN)
(Fig 12 Truth table for arm movement Designing)
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5.1 PIC 16F877A
This is an 8-bit controller with programmable flash memory.
PERIPHERAL FEATURES:
Timer0: 8-bit timer/counter with 8-bit prescaler \
Timer1: 16-bit timer/counter with prescaler, can be incremented during Sleep via
external crystal/clock
Timer2: 8-bit timer/counter with 8-bit period register, prescaler and postscaler
Two Capture, Compare, PWM modules
- Capture is 16-bit, max. resolution is 12.5 ns
- Compare is 16-bit, max. resolution is 200 ns
- PWM max. resolution is 10-bit
Synchronous Serial Port (SSP) with SPI™
(Master mode) and I2C™ (Master/Slave)
Universal Synchronous Asynchronous Receiver
Transmitter (USART/SCI) with 9-bit address detection
Parallel Slave Port (PSP) – 8 bits wide with external RD, WR and CS controls
(40/44-pin only)
Brown-out detection circuitry for Brown-out Reset (BOR)
ANALOG FEATURES:
10-bit, up to 8-channel Analog-to-Digital
Converter (A/D)
Brown-out Reset (BOR)
Analog Comparator module with:
- Two analog comparators
- Programmable on-chip voltage reference
(VREF) module
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- Programmable input multiplexing from device inputs and internal voltage
reference
- Comparator outputs are externally accessible
SPECIAL MICROCONTROLLER FEATURES:
100,000 erase/write cycle Enhanced Flash program memory typical
1,000,000 erase/write cycle Data EEPROM memory typical
Data EEPROM Retention > 40 years
Self-reprogrammable under software control
In-Circuit Serial Programming™ (ICSP™) via two pins
Single-supply 5V In-Circuit Serial Programming
Watchdog Timer (WDT) with its own on-chip RC oscillator for reliable operation
Programmable code protection
Power saving Sleep mode
Selectable oscillator options
In-Circuit Debug (ICD) via two pins
CMOS TECHNOLOGY:
Low-power, high-speed Flash/EEPROM technology
Fully static design
Wide operating voltage range (2.0V to 5.5V)
Commercial and Industrial temperature ranges
Low-power consumption
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CONCLSUION
During the first phase of our project we were able to finish the speech recognition
part and a part of the prosthetic control board. The construction of the arm and interfacing
the servo motors are the major task before us for the second phase of our project.
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REFERENCE:
Tele-Operated Anthropomorphic Arm and Hand Design
Namal A. Senanayake, Khoo B. How, and Quah W. Wai
Controlling a prosthetic arm with a throat microphone
Elena Mainardi, Angelo Davalli
Development of a prosthetic arm: experimental validation with the user and an
adapted software
V. Artigue, G. Thomann
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