ISU Lunabotics Autonomous Navigation System
Draft Plan
MD4: ISU Lunabotics
Alan Bentley
Clarence Boright
Chen Wen
Allison White
Zihao Zhao
1
Table of Contents
Executive Summary................................................................................................................................... 3
Problem Statement ................................................................................................................................... 4
Project Importance ................................................................................................................................... 4
Customer Requirements ........................................................................................................................... 4
Opportunity Statement............................................................................................................................. 5
Deliverables............................................................................................................................................... 5
Market Research ....................................................................................................................................... 5
Customers ................................................................................................................................................. 6
Performance Criteria................................................................................................................................. 6
Solution Development .............................................................................................................................. 7
Design Concept ......................................................................................................................................... 9
Trade Studies .......................................................................................................................................... 11
Sensor tradeoffs .................................................................................................................................. 11
Robot Tradeoffs .................................................................................................................................. 11
Algorithm Tradeoffs ............................................................................................................................ 11
System Specifications.............................................................................................................................. 11
Inputs/outputs .................................................................................................................................... 11
System Description ................................................................................................................................. 12
System Interactions/Interfaces ........................................................................................................... 12
Key Technical Challenges .................................................................................................................... 13
Plan ......................................................................................................................................................... 13
Work Breakdown Structure ................................................................................................................ 13
Resource Requirements ...................................................................................................................... 14
Milestones and Schedule .................................................................................................................... 14
2
Executive Summary
ISU Lunabotics is in need of an autonomous navigation system that will recognize and avoid
obstacles in the LunArena to remain highly competitive in the annual NASA Mining Competition. In the
past, moving along now, and into the present, valuable time and resources are invested into the ISU
Lunabotics initiative. The commitment to achieving the implementation of this autonomous navigation
will provide an outstanding competitive advantage at the competition.
To achieve this competitive advantage, the ISU Lunabotics has provided requirements by which
they desire as well as what is confined by the NASA Mining Competition rules and guidelines. These are
detailed in more depth reading on but just to name a few they would include: abiding by the NASA
Mining Competition rules and guidelines, obstacle recognition, design considering weight constraints,
and provide an affordable solution that performs under dusty conditions.
Performing at a high level at the NASA Mining Competition not only brings recognition to Iowa
State University but also provides opportunity for the ISU Lunabotics to continue innovation. The
awarding’s strived for include the Joe Kosmo’s Award for Excellence trophy, plaques and certificates,
$5,000 team scholarship, up to $1,000 travel stipend, and KSC launch invitations.
The project intentions are to develop an autonomous navigation system that lines up with all
outlined requirements both by NASA and ISU Lunabotics that is fully implementable by ISU Lunabotics. If
the project is not completely at this stage upon the project deadline, a planned approach will be
outlined for ISU Lunabotics to follow moving forward.
There are numerous colleges and universities that compete at the NASA Mining Competition
and they are detailed later. The customers have been identified along with the systems performance
criteria integrating with the robot. Up to date solution development is described which has concluded to
using either a laser scanner or infrared (IR) sensors. Favoritism up to this point has been given to the
laser scanning technology because of the capabilities that it provides in comparison.
Tradeoffs depending on which solution is deemed most fitting have been recognized as well as
creation of a function diagram showing the requirements of the autonomous navigation performance.
System interactions and how it will interface is another described part of the solution for the project. A
Quality Function Deployment is included to show what is considered most important while meeting
customer requirements. Additionally, challenges, a work plan, and a schedule have been provided.
3
Problem Statement
ISU Lunabotics Club needs an autonomous sensor system for navigation to remain highly
competitive in next year's Lunabotics mining competition. The system needs to be able to scan the
LunArena, recognize what the obstacles are to be avoided, and create an effective route to the mining
area. After the mining operation is performed the robot will then be required to safely return to its
original area where the dumping site is located to dispose of the gathered regolith.
Project Importance
Valuable resources and time is continually invested in the ISU Lunabotics. The purpose of such
activity is to continue advances in LunaCy. In this particular instance, it will come in the form of
implementing an autonomous navigation system which will provide a dramatic competitive advantage in
the NASA Mining Competition. This advantage comes in the form of scoring a substantial amount of
points for having autonomy built into the robot.
Customer Requirements
Through discussions with ISU Lunabotics they have provided guidelines and requirements to
follow while developing an autonomous navigation system. Not only do the needs of ISU Lunabotics
need met but also those needs of NASA. The established requirements in no particular order of
importance are as follows:

Abide by NASA Mining Competition rules and guidelines

Develop an autonomous navigation system

Recognize three randomly placed obstacles in the LunArena

Avoid the three recognized obstacles

Effectively create a route to reach the mining area to mine and return to the dumping area

Reduce lag time between start, move, and dig

Maximize digging and dumping time

Improve system reliability and operation ease

Consider the weight impact the system will have on weight goal for the entire robot

Provide a system that is affordable

Limit the amount of power required to run the system

The system must be able to operate reliably under dusty conditions
4
Opportunity Statement
By competing in the NASA Mining Competition, recognition is gained for Iowa State University
and the caliber of engineers the school produces. Further, to achieve a reputation as a competing school
at a high level requires not only beating the competition but also staying ahead from year to year. Other
than national recognition there are other awards and incentives. Below outlines what these are:

Joe Kosmo’s Award for Excellence trophy

Plaques and certificates

$5,000 team scholarship

Up to $1,000 travel stipend

KSC launch invitations
Deliverables
The team’s goal is to design a feasible autonomous navigation system for the ISU Lunabotics
Club that follows the NASA Mining Competition rules. The team will work throughout the semester to
complete all assignments on time, follow the schedule in the DMADVR toolbox, and meet all milestones.
If the full design and implementation cannot be completed by the end of the semester the team will
have a planned approach for the club to follow for implementation. The necessary resources will be
made available for the Lunabotics Club to carry out the team’s work for the autonomous navigation
system. Resources will include a report and presentation at mid-term and the end of the project.
Market Research
ISU Lunabotics is finding multiple ways to remain on top of the annual NASA Mining Competition
by keeping their competitive demands high. Annually they are improving their robotic system to
increase their chances of placing top three by scoring as many points as possible. A huge breakthrough
to scoring a large amount of points is to build an autonomous navigation system for the robot. However,
while they are trying to accomplish this there are many other identified competitors that have their
sights set on achieving this as well. To not be lapped or passed by them, it is a desired must to
implement such technology. Knowing there are many feasible technologies in the market, they have the
obstacle of making sure the chosen technology is not only affordable but can perform under extreme
conditions. These extreme conditions consist of a population of dust in the mining arena. This
implementable technology needs to remain functional under these conditions. It has been discovered
that there are acceptable solutions on the market. Some of these include: RFID technology, radar
5
technology, and radio triangulation. The competition in the market has been considered and is as
follows:

Milwaukee School of Engineering (MSOE)

West Virginia

Embry-Riddle Aeronautical University, Prescott

Laurentian

Colorado School of Mines

University of North Dakota

John Brown

Alabama

Polytechnic Institute of NYU
All of these colleges and universities are competitors at the NASA Mining Competition.
Customers
The goal of this project is to serve the needs and desires of the customers involved with the
outcome of the project. Customers for this project can be classified into two categories: primary and
secondary. Primary customers would be ISU Lunabotics, NASA, and the NASA Mining Competition rules
and guidelines. The secondary customers are identified as the sponsors consisting of Vermeer,
Caterpillar, and PPI.
Performance Criteria
Table 1: Specifications
6
Solution Development
The team first considered the 3-D visualization method to fulfill the requirement. The theory
was inspired by last year’s design. Last year, the ISU Lunabotic Club used a camera to capture the
environment and guided the robot after analyzing data. However, this common camera faced a serious
problem being the heavy dust caused by mining. Following this thought of avoiding dust, the team came
up with this 3-D visualization solution.
A team member mentioned in a team meeting that he knew Professor Song Zhang had recently
developed a completed 3-D visualization solution package. He thought that the team could ask Professor
Zhang for his help. The team contacted Professor Zhang and setup a meeting.
While waiting for the meeting with Professor Zhang’s, the team discussed the possibility of using
RFID technology with beacons to locate and guide the robot. The team found out that the RFID
technology was affordable and required low power consumption. Upon further investigation, it was
discovered that if beacons were left in the LunArena they would have to be picked up. This would cause
a major structure change of the robot to retrieve them. Additionally, the RFID sensor would need to be
on top of the robot to receive complete signals. The team decided these changes were not worthy of
modification.
Professor Zhang informed the team that his device was not appropriate for the project because
of its complexity and cost. He said by his estimation the installation of his 3-D visualization device would
at least take a year. Also, another reason that the team cannot use his device was that the size and the
power consumption of his 3-D visualization device were too high.
The team discussed the usage of electromagnetic wave technology to solve the problem. It
turned out that this technology appeared to be the most promising solution so far. The team researched
a suitable commercial radar product for the project and it seemed that most of the products were sonar.
The environment the robot is to perform in is a vacuum, therefore sonar was deemed unsuitable. The
team decided to go to the Electrical Engineering department (EcpE) at Iowa State University for help on
radar product researching.
After establishing contacts within the EcpE department, there are four EcpE professors who are
able and willing to help us. The team first met with Professor Jiming Song and he told the team that
there were not a lot of feasible commercial electromagnetic wave sensors on the market because sonar
7
sensors are cheaper and easier to use. He suggested the team research police speed detecting gun since
that is an available electromagnetic product. He also suggested that an infrared (IR) sensor might work
as an alternative.
In a meeting with Professor Zhengdao Wang, he suggested the team look for the police sensor
and IR sensor. He mentioned that laser senor was another alternative worthy of researching. He told us
that Professor Mani Mina is quite knowledgeable of laser products.
In a meeting with Professor Mani Mina, he introduced the team to Professor Koray Celik who is
more of an expert on laser products. Concurrently, another team member discovered the autonomous
navigation system used on Stanley. Stanley is Stanford University’s 2005 DARPA Grand Challenge entry.
This system uses laser scanners and appears to fit the project’s needs. The team is still trying to acquire
one.
In meeting with Professor Koray Celik, he was a valuable resource. He told the team the initial 3D visualization method had a major flaw of signal drift even if another suitable 3-D visualization device
was found. He told the team that based on the current market and customer requirements, a laser or IR
sensor would work best. Furthermore, he recommended a de-dust device on the sensor and suggested
static electricity as a method. Another recommendation was Sharp’s IR sensor and Hokugo’s laser
products.
8
Design Concept
Given the breadth of research conducted so far, it seems that the best possible solution is a
simplified version of the autonomous navigation system used on the Stanford entry in the 2005 DARPA
Grand Challenge, nicknamed and referred to henceforth as “Stanley” (Details of this system can be
found at: http://cs.stanford.edu/group/roadrunner//old/index.html ). The navigation system onboard
Stanley used quite a bit of computational power to
combine and analyze data from a wide variety of
sensors onboard Stanley, some of which would not
be appropriate in the LunArena due to constantly
increasing airborne dust issues.
The key item used on Stanley that is
Figure 1 – Stanford University's Autonomous Vehicle
appropriate for navigation and obstacle avoidance on “Stanley” from:
ART-E IV is the Laser Ranging and Object Detection
http://cs.stanford.edu/group/roadrunner//old/inde
x.html
device from SICK AG. Stanley featured five of them
and they can be seen on the vehicle to the right as the grey and black devices fanned out over the width
of the front of the roof. Stanley used a large array of them and combined the received elevation data
with the image from a live forward looking camera to determine an acceptable speed. This will not be
feasible in the LunArena, due to a combination of dust interference with the images from the camera
and very little contrast between obstacle laden areas and navigable terrain. The use of a laser scanner
however, is a very promising prospect, and if the final device selected were from the selection of
products on the market that use multiple echo technologies in tandem with their laser ranging schemes,
the interference from dust and other environmental factors could be greatly reduced.
The ideal device as currently determined is from SICK
AG’s LMS-1xx line of sensors, and an outdoor model to
prevent issues arising with the device due to ingress of harsh
dust from the regolith within the LunArena. It would play a
central role in object detection and would allow ART-E IV to
build a map of the LunArena during the course of operation
and would no longer need to be active after the first pass out
and back, to the mining area of the LunArena. In this case it
Figure 2: SICK AG LMS - 1xx Series Laser Scanner,
from
https://www.mysick.com/saqqara/wrapper.aspx
?id=im0025864
could be used as a secondary failsafe incase the positioning
9
system of ART-E IV were to detect possible errors in the positioning data within the onboard navigation
system. Then the laser scanner’s data could be used to determine the approximate position of the robot
relative to walls etc. and could be used for secondary navigation.
In addition to the use of the laser scanner for object detection and avoidance, the project team
has been looking into accelerometer navigation schemes to determine the displacement of ART-E IV for
establishing position within the arena. One scheme for this is to use the accelerometer to determine
the displacement, while ART-E navigates with only the laser scanner’s input to make sure that no
obstacles cause stoppages. Once ART-E is in the mining area, it will have an internal map of the arena
that it can use to plot an optimal path back to the regolith repository. The return and subsequent trips
across the LunArena will then use an optimized path, based on a complete map of the arena using
elevation data obtained from the laser scanner. In addition to just the ranging information from the
scanner, ART-E will use its accelerometer and gyroscopic features to determine how it sits relative to the
floor of the LunArena and can then adjust the data points coming back from the laser scanner
accordingly.
In terms of finding and depositing regolith in the regolith repository, the optical reflective
square finding method should be sufficient, since it will really only be needed to find the bin while on
the opposite end of the arena from where most of the dust is generated. Once ART-E enters the
dumping area of the LunArena, the camera system will become active and will find its way to the bin
backwards. The accelerometer will still be active, but the position of the robot in the LunArena model it
contains can be reset, to help control error in the navigation system’s positioning data, once the robot
has backed itself up to the bin and both of the existing (ART-E III) bumpers are in contact. At this point
ART-E will assume it is at 0, which can be checked from the laser scanner, but all navigation will resume
from this location and the model will be used to set way points and establish a safe course for the
robot’s subsequent trips.
10
Trade Studies
Sensor tradeoffs

Laser radar vs. Infrared vs. Accelerometer

How to locate the obstacles walls

How to obtain relative position

How to measure distance

How to deal with dusty environment
Robot Tradeoffs

Accuracy and Precision and Timing

Power input

Weight of sensor

How to find the mining area (digging conveyor, bucket wheel)

How to find target to disposal (disposing conveyor, dumping hopper)
Algorithm Tradeoffs

How to avoid the Obstacles (forward, turn vs. go through)

How to calculate waypoints ( to set up a routine)
System Specifications
Inputs/outputs

Inputs: data from laser measurement sensor

Outputs: motor control, hopper control, auger control, telemetry (coordinate position)
11
System Description
Figure 3: Functional Diagram
System Interactions/Interfaces

Host computer: interacts with laser sensor to start program

Operational computer: interacts with laser Sensor to calculate waypoints and set up a tontine
for motor movement

Code: after calculations, sensor interacts with robot directly to move motors

Accelerometer: create better calibration to monitor robot movement
12
Table 2: Quality Function Deployment
Key Technical Challenges
Our key technical challenges are to set up this laser sensor to work through dust and no
atmosphere, which can run fully autonomous and location the mining area. Real-time output of
measurement data of the system will also be a challenge, as we have to come up with an effective
operational code system, which can be compatible with laser sensor and robot to guarantee a given
deadline for certain actions, such as obstacle avoidance, waypoints’ calculations, target detection and a
fast and accurate sensor’s reaction speed.
Plan
Work Breakdown Structure
Team members and areas of responsibility:
Alan Bentley — Team leader, maintain organization
Allison White — Limited programming, maintain organization, and testing
Chen Wen — Programming, design, and assembly
Clarence Boright — Programming, design, and research
Zihao (Terry) Zhao — Analysis, review, and testing
13
Resource Requirements
The following is a breakdown of possible items needed:

Laser – SICK Laser Scanner LMS – 1xx Series

Infrared sensors

Accelerometer
Milestones and Schedule
The Milestones throughout the semester include:

Define / Measure Review: September 24, 2012

Analyze Review: October 1, 2012

Design Review: November 12, 2012

Verify Review: November 26, 2012

Report Due: December 12, 2012
14
Table 3: Project Schedule
15