brady king archie kinney luke reisner advisor: dr...
Post on 14-Jul-2020
3 Views
Preview:
TRANSCRIPT
Automated Tissue Scanning
Brady KingArchie KinneyLuke Reisner
Advisor:Dr. Abhilash
Pandya
Problem Statement
� Intraoperative tissue classification is slow and difficult� Human-performed biopsy (20+ minutes)
� Surgeon makes final decisions
� Tissue classification could allow faster, more accurate resections� Better treatment of cancer or other procedures
� Faster recovery time, lower cost
Our Proposal
� Develop a robotic surgery system capable of classifying tissue in near real-time� Use Raman spectroscopy
� Automate the process ofsample collection
� Interface with currentimage-guided surgerysystems
� Develop novel interfaces for configuring scans and presenting results to the surgeon
The Aesop 3000 (bloop-bloop!)
� “Automated Endoscopic System for Optimal Positioning”
� 7 degrees of freedom (4 active joints)� Control schemes: voice, hand,
foot, touch screen, manual, andserial� Moves end-effector through a
pivot point
� Only moves up/down/left/rightand in/out
Objectives
� Objective 1� Integrate a rigid robotic arm, the Aesop 3000, into
our current image-guided surgery system
� Objective 2� Integrate a Raman probe with the robotic system
� Objective 3� Modify the Aesop 3000 to facilitate automated
tissue scans
� Objective 4� Develop a novel interface for both the selection of
scan parameters and presentation of scan data
Objective 1
� Integrate a rigid robotic arm, the Aesop 3000, into our current image-guided surgery system� Reverse engineer the Aesop to
find control pins and voltages� Breakout box
� Develop a system to relay the tracking information to a PC
� Integrate the tracking information with our current image-guided surgery system
Breakout Box
� Fearlessly sliced a $2,500 Aesopcontrol cable in half
� Connected 2 x 55 wires to a PCB� Made a ribbon cable for routing signals� Mounted on a sturdy metal platform� Big thanks to David Sant!
Breakout Box Picture
Pin Determination
� Tested all 55 pins to determine their functions and working values� Power, motor control,
potentiometer feedback, encoder feedback, etc.
9.48 V DC10
…11
Shoulder/elbow CW control9
Shoulder/elbow CCW control8
Ground7
Elbow potentiometer6
Shoulder potentiometer5
Linear potentiometer4
Linear down control3
Linear up control2
Ground1
UsePin
Potentiometer Feedback
� Developed a system to read potentiometer feedback of 5 most important joints� Some joints have broken or no pots
� Used a 12-bit USB A/D converter to send the feedback voltages to any PC� Can be logged to a file
� Had to buffer linear motor feedback to prevent automatic shutdown
Potentiometer System
DH Parameters
� Measured Aesop’s link lengths, joint limits, and other parameters
� Derived the Aesop’s kinematics model using the standard DH notation
0 + (θ7)180°-d707
90° + (θ6)-90°0a66
180° + (θ5)90°d505
90° + (θ4)90°004
0 + (θ3)90°0a33
0 + (θ2)00a22
000 + (d1)01
00d000
θiαidiaii
Kinematics Model
Note: Diagram is more complicated than it looks.
Tracking in Matlab
� Robotics toolbox for Matlab used to implement the Aesop’s DH model
� Program reads logged pot voltages� Function converts voltages to joint
angles/position� Modified plot function displays the robot’s
motion in 3D using forward kinematics
Tracking Demo!
� Enjoy the demo of Matlab tracking the Aesop’s movements
(Note from Luke: If it doesn’t work, it’s Brady’s fault.)
Objective 1 Challenges
� Aesop 3000 is difficult to work with� No documentation
� Disassembly, automatic shutdown, etc.
� Some joints have missing or broken potentiometers� Will use encoders
� Connectors made by different companies� Breakout box was more difficult to make
� David Sant helped us with this
Objective 1 Changes
� Haven’t integrated with image-guided surgery system (yet)�Decided to use encoders�Waiting for motion controller
� Getting motion controller now will save significant time in Objective 3� Integrating potentiometer feedback would
be a waste of time
Intermission
(I spent way too much time on this slide)
Objective 2
� Integrate a Raman probe with the robotic system� Physically attach the Raman probe
� Integrate Raman classification software with our image-guided surgery system
� Perform a human factors study
� Further develop classification software
� At this point, the system will be ready to perform simple point classification
Objective 2 System Diagram
Request Raman
Point
Send Raman Point
Request A
rm
Location
Send Arm Location
Portable Raman Probe
� Acquired portable Raman probe (finally!)
� Needs to be tested on actual tissue� Eventually will be mounted on the
Aesop
Neural Network Classification
� Part of our Raman data classification algorithm to identify cancer, etc.
� Completed C++ implementation of neural network forward pass� Supports a variable
number of neurons andactivation functions
� Will be integrated with theRaman server application
Raman Server Application
� Acquires end-effector location from a robot server (for the MicroScribe)
� Queues and retrieves data points� Sends data points to 3D Slicer clients� Need to communicate with Raman probe� Need to implement Raman data processing
� Pre-processing (noise filtering, background fluorescence subtraction, normalization)
� Peak extraction, neural network identification
Raman Server Diagram
Objective 2 Challenges
� Raman probe still needs more testing� Compare with previous
Raman data
� Certain algorithmsdifficult to implementoutside of Matlab� Bundled Raman probe software may help
Objective 3
� Modify the Aesop 3000 to facilitate automated scanning motions� Physically modify the Aesop 3000 to better
facilitate movement in vivo
�New sensors, motors, etc.�May switch to Zeus if insufficient
� Develop software to control scanning motion
�Model robot dynamics�Order/build motor controller
Motor Research
� Determined what motors are used in the Aesop 3000� Various brush DC motors from MicroMo
� Researched motor specifications� Voltage and power requirements, etc.
� Determined what encoders are used and their specifications� Rotary encoders of various
resolutions from US Digital
Motion Controller
� Ordered motion controller (we think) from Galil that can handle our motors/encoders� Standalone Ethernet device
� 24 V, 12 A power supply� 4-axis, 200 W amplifier to
drive the motors� 16-bit A/D daughterboard for reading pot
feedback of passive joints
Objective 3 Challenges
� Aesop has only 4 active joints (3 useful)� Could enhance Aesop or switch to Zeus
�Current work will transfer over
� Complete control of the robot arm is difficult� People at Intuitive Surgical are jerks
� Have to set up a standalone motion controller
� Automatic shutdown may be an issue
� Positioning accuracy hard to predict
Revised Budget
Free ($120)System for Aesop tracking3.
$20Breakout box for reverse engineering Aesop2.
Free ($50k)Raman probe4.
$2,905Total:
NegotiableTwo graduate students’ tuition, benefits, and stipend (3 years)
8.
$200Robot modifications7.
$2,635Motion controller (with supply, amplifier, A/D)6.
$50Raman probe mounting hardware5.
Free ($60k-1M)Aesop 3000 (or Zeus)1.
CostItem#
Current Progress
7%
0%
10%
20%
90%
0% 20% 40% 60% 80% 100%
Objective 1
Objective 2
Objective 3
Objective 4
Taking Over theWorld
Future Timeline
January ’09Develop novel interfaces for automated scans, human factors
Obj. 4
November ’07Modify the Aesop 3000 to facilitate automated scans
Obj. 3
March ’07Integrate the Raman probe with the robotic system, human factors
Obj. 2
May ’06Track the end-effector of the Aesop 3000
Obj. 1
DateDescriptionTask
Feedback
Any questions, comments, or suggestions?
(Other than the obvious question)
top related