ISU Lunabotics Autonomous Navigation System

Draft Plan

MD4: ISU Lunabotics

Alan Bentley

Clarence Boright

Chen Wen

Allison White

Zihao Zhao1

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

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

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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.

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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

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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

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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

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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

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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 3-

D 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.

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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

appropriate for navigation and obstacle avoidance on

ART-E IV is the Laser Ranging and Object Detection

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 could be used as a secondary

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Figure 1 – Stanford University's Autonomous Vehicle “Stanley” from: http://cs.stanford.edu/group/roadrunner//old/index.html

Figure 2: SICK AG LMS - 1xx Series Laser Scanner, from https://www.mysick.com/saqqara/wrapper.aspx?id=im0025864

failsafe incase the positioning 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.

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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)

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System Description

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

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Figure 3: Functional Diagram

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

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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

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Table 3: Project Schedule

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