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Daedalus Autonomous Vehicle

June 20, 2002

Team Members:

Nicole Anthony Byron Collins

Michael Fleming Chuck Liebal

Michelle Nicholas Matthew Schmid

Required Statement from Faculty Advisor I, Dr. Charles Reinholtz of the Department of Mechanical Engineering at Virginia Polytechnic Institute and State University, do hereby certify that the engineering design of the new vehicle, Daedalus, has been significant and each senior team member has earned six semester hour credits for their work on this project.

Signed, (Date)

Dr. Charles Reinholtz (540) 231-7820

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Introduction The 2001-2002 Virginia Tech Autonomous Vehicle Team (AVT) and Team Daedalus is proud to present a new

vehicle for entry in the 10th Annual Intelligent Ground Vehicle Competition (IGVC). The Daedalus Autonomous Vehicle

will compete in all three challenges of the competition: Vehicle Design, Autonomous Challenge, and the Navigation

Challenge. The team implemented a structured, systematic design process, and the latest technologies in the design of this

vehicle. Daedalus is named after a mythological Greek inventor who created a labyrinth and was the only one who could

escape the maze.

Design Process

The design process, as described in Product Design and Development (Ulrich 2000), is shown in Figure 1. The

steps in this process are iterative, meaning that one or more steps may be repeated until satisfactory results are obtained.

The mission statement provides a working foundation by outlining the scope of the project. The mission statement

includes a product description, key business and educational goals, market definition, assumptions, and stakeholders. The

product description characterizes the goal without specifying a detailed design concept. Team Daedalus’s product

description is “a vehicle that is able to navigate autonomously and adheres to the rules of the 10th Annual Intelligent Ground

Vehicle Competition”. Some of our key goals include designing a competitive vehicle and effectively using the design

process in a team environment. The team targets two groups of customers: governmental and educational. Our governmental

customers include the United States Military and the Department of Transportation, and our educational customers are our

faculty advisor, Dr. Charles F. Reinholtz and ourselves. Our primary stakeholders are the team sponsors, Virginia Tech, and

the design team.

Identify Customer

Needs Establish Target

Specifications Generate Product

Concepts Test

Product Concept(s)

Set Final

Specifications

Plan Downstream Development

Mission Statement

Development

Plan

Figure 1. Schematic of the design process.

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Figure 2. CAD Rendering of Daedalus Conceptual Design

After defining the mission statement, the team identified a set of customer needs. The first step in this phase was to

interview the primary and secondary customers as defined in the mission statement. Dr. Reinholtz, former AVT members,

IGVC judges, and team members from other universities were consulted. During these interviews, questions were asked

concerning previous vehicle performance, design flaws, and events at previous competitions. The results of these interviews,

as well as information from competition rules, set a foundation for the generation of customer needs.

Each customer need was numerically ranked from least important to most important. This ranking system focused

the team on the most important customer needs. After identifying customer needs, target specifications were set. The first

step in this phase was developing a list of metrics. Metrics are measurable characteristics of a product, such as weight or

maximum speed, which quantify the customer needs.

Once the list of metrics was developed, previous vehicles were benchmarked using the metrics as guidelines for

comparison. On-line design reports from prior competitions provided an excellent reference for gathering competitive

benchmarking information. The following vehicles were studied for their relative strengths and weaknesses: Virginia Tech

Maximus (2001), Virginia Tech Navigator (2000), Virginia Tech Artemis (1999), West Point MAGIC (2001), and Hosei

University Amigo (2001). By comparing the characteristics of these other vehicles, the team was able to set target

specifications for Daedalus.

Conceptual designs were then generated based on target specifications. Figure 2 shows an AutoCAD Mechanical

Desktop 6.0 rendering of one of the early concepts . The team then reflected on the design process and results to ensure

practicality and team agreement. By taking a systematic approach

throughout vehicle development, the team was able to target specific

problems inherent in previous designs and develop solutions for them.

VT Maximus had an unstable frame and unorganized components. VT

Navigator was an oversized vehicle that incorporated older technologies

and components. VT Artemis was difficult to maintain. All of these

vehicles had detached hoods which were cumbersome and heavy. The

conceptual design for Daedalus focused first on creating a stable, easily

accessible, well-organized, and compact vehicle.

Overall cost, feasibility, and adherence to target specifications were some of the criteria used for concept selection.

Concepts were scored and ranked. The highest-ranking concept, a compact three-wheeled design, was chosen for further

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Figure 3. Daedalus accessibility

development. With the concept clearly defined, the team established more detailed cost estimates and began to procure

resources to build and test a prototype. A bill of materials was created, which summarized both fixed and variable costs.

Eliminating complexity and unnecessary parts, using different materials, and optimizing parts reduced the cost and

complexity of the final design. The team actively sought donations for expensive components as well as solicited price

quotes from multiple suppliers. Using standard components such as aluminum channel, tubing, and angle, rather than

specially machined components, the time and cost required to create these custom components was eliminated. The team

reduced the labor and cost of assembly by making all parts modular and easily accessible.

Innovations

By employing a systematic design approach, Team Daedalus could readily incorporate selective innovations at the

conceptual design phase of the project. The team learned from earlier designs that simplicity and reliability were critical

features of a successful design. Team Daedalus also learned that making the vehicle light and compact facilitated transport

and testing. These features are especially important for the Disney venue

where vehicles may need to be carried or carted from the indoor

conference center to the competition course. The team also learned that

the user interface and accessibility of key components were important

attributes of successful prior designs. While these features individually are

not unique to Daedalus, taken in combination, they represent a level of

refinement not seen in previous vehicles. Figure 3 shows a front view of

the vehicle with the front Lexan enclosure open, allowing access to the

electrical box and batteries.

Another innovative feature of Daedalus is wireless Ethernet communication with the on-board computer through PC

Anywhere. This provides two significant advantages. First, it allows the operator or programmer to monitor the running

vehicle remotely and in real-time. This makes testing easier, and debugging the vehicle code more efficient. The second

advantage of wireless Ethernet is that it eliminates the space, weight and power consumption associated with an on-board

monitor and keyboard, which in turn reduces the size of the battery pack for a given run time. This helps to make Daedalus a

lightweight and compact vehicle. In larger production runs of a commercial vehicle, this configuration would also allow a

customer to program and control multiple vehicles from a single base station. This would in turn lower the production cost of

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Figure 4. Debugging from a remote base station

Figure 5. Four-bar linkage operation illustration

each vehicle. In retrospect this approach seems obvious. Why include an

operator interface on a vehicle that is intended to run without an operator?

Figure 4 illustrates testing and debugging performed from a remote base

station using a wireless Ethernet “Ad Hoc” network.

Another small, but helpful, innovation is the use of a four-bar

linkage to guide and support the rear weather-resistant cover of the vehicle.

Most of the vehicles entered in previous competitions used a latch or bolt-on

cover that needed to be completely removed from the vehicle to allow access

to the interior components. The linkage-guided cover on Daedalus rests above the vehicle in the open position and closes the

hood securely during operation. Previous competitors cited space limitations as an important issue in critical situations, such

as the work areas provided at competition or when the vehicle was in the queue waiting to compete. Figure 5 illustrates the

operation of the linkage.

An important, but invisible, innovation is the navigation software implemented on Daedalus. This software is a

variation of the vector field histogram approach that was used by the Virginia Tech Navigator team last year. Rather than

writing separate algorithm code for the Autonomous Challenge and the Navigation Challenge, Daedalus uses the same

approach and code for both competitions. A target point is set, either using computer vision to locate a point on the course or

by using differential GPS to target the next waypoint. The navigation system then tries to move the vehicle in the general

direction of the target point, modifying the path with weighting functions to avoid nearby lines, potholes or obstacles. In the

absence of a clearly defined target point, the vehicle tries to move straight without crossing a boundary or hitting an obstacle.

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The navigation code treats lines, potholes and barrels as obstacles to be avoided; hence the vehicle will always be viewing an

obstacle during navigation along the course.

Frame

The vehicle frame is compact at 2’ wide x 2’ high x 3’ long, and is constructed using lightweight aluminum. The

frame members are welded together forming a solid, stable platform that eliminates the added weight and complexity of

bolted joints for fixed components . Sensitive components are encased in a weatherproof Lexan cover. The front “door” of

the vehicle locks in a closed position and easily swings open to access the electrical box and to replace drained batteries. The

rear portion of the cover connected by a four-bar linkage, swings up and away allowing access to the computer, payload, and

electronics, and locks in open and closed positions. To eliminate excessive wiring and complexity, electronic components

are well-labeled and confined to a central drawer that easily slides fully out of the vehicle. This modular design isolates

components, allowing for easy access and maintenance. Two drive motors are mounted under the frame to lower the center

of gravity, thus improving the vehicle’s stability and mobility.

At the base of the vehicle, two beams and a plate support the computer and motors. Eight batteries rest in two levels

at the front of the vehicle. With this configuration, the center of gravity is located slightly behind the front drive axis. A

center beam, made from aluminum box tubing, provides stability, supports the hood, and provides a connection for the

electrical box. The hood of the vehicle is made from durable Lexan, which provides a sturdy, weatherproof protection for

sensitive interior components. A four-bar linkage connects the rear hood to the center beam, while a hinge assembly

connects the front “door”. The materials and layout of the beams provide a lightweight, versatile design, which allows for

quick and easy access to interior components while providing excellent support.

Drive System

Daedalus uses two Bodine 24 volt DC, 15 amp, 0.45 hp, brush-type servomotors to power the drive wheels. These

motors are attached to the drive wheels via two 90-degree, 33:1 reduction gearheads. Along with a rear caster wheel, this

differential drive system provides an excellent balance of mobility and stability. This differential drive system allows for

zero-radius turns with minimum wheel slip. The hard drive wheels also contribute to reducing slip, thus reducing error

during navigation. This allows the vehicle to navigate accurately using dead reckoning, which is used during the GPS

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Challenge portion of the competition. Aluminum hubs connect the drive shaft and the wheels. The motors include integral

optical encoders and fail-safe brakes that stop the vehicle when no power is available.

Electrical System

Overview:

The team’s desire to construct a safe, reliable, efficient, and serviceable electrical system drove Daedalus’s electrical

design. The electrical layout was developed and refined in CAD to reduce wiring time and to provide documentation for

future Virginia Tech teams.

Power System:

Rechargeable 24-volt DeWalt NiCad batteries are used to power all systems on Daedalus. A total of eight batteries

are used, three for the motor bus and five for the computer bus. In normal operation, the vehicle operates for 3 hours on a

fully charged set of batteries. A “hot-swap” battery system was implemented for replacement of drained batteries without

having to shut down the entire vehicle. In our interviews with students from earlier competitions, this ability was cited as a

significant advantage on the day of competition. Teams with “hot-swap” batteries were able to practice or wait in a queue for

their turn to run without extension cords or fear of running low on power.

Low loss diodes have been added in series to each battery to prevent back-charging. In addition, metal

polypropylene capacitors and fuses are placed at each battery and motor to reduce the effects of current spikes and noise

which may damage onboard electrical equipment.

Relays placed in series with each battery allow temporary

bus disconnection for checking battery voltage levels. This is

accomplished using a National Instruments PCI-1654 board to

sequentially trigger eight relays and check the voltage of each

battery as well continually monitor the motor and computer bus

voltages. Labview 6.1 has been used to develop the battery check

software interface as shown in Figure 6. This software checks

battery voltages on command or at timed intervals and estimates

remaining battery life.

Figure 6: Battery check software

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Encoder

Amplifier

Motor

Control Board

PC Motor

Figure 7: Control System

Figure 8: Electrical housing enclosure

Figure 9: Rear electrical housing enclosure

Control System:

Daedalus uses a closed loop PID (Proportional Integrated Derivative) control system to drive each wheel as shown

in figure 7. A Galil DMC-1030 motor controller capable of multi-axis control sends a +/- 10 V analog signal to each of two

Advanced Motion Controls amplifiers. The amplifiers

in turn send a corresponding current between 0-15 A to

the drive motors. An encoder integrated into the motor

reads the velocity of the motor and sends it back to the

motor control board. Based on the encoder reading, the

motor control board sends an updated signal to the

amplifiers and the cycle repeats.

Electrical Enclosure:

To ensure that Daedalus’s electronics are easily serviceable and

protected, a 17”x 17”x 4” aluminum enclosure houses key electronics as

shown in Figure 8. This innovative packaging protects electronic

components and allows them to be removed for servicing in less than a

minute. In our prototype vehicle, this allowed easy bench-testing and repair

of electronic components. In a production vehicle, this arrangement would

have the significant advantage of modular replacement of on-board

electronics, which would permit rapid re-deployment and off-line diagnostics

and repair.

Twelve labeled connectors on the electrical enclosure

provide a quick disconnect and clean appearance. These include

connections to the motor, laser rangefinder, computer, and power

as shown in Figure 9.

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Safety

Safety was our most important concern in all aspects of design, fabrication, and operation of Daedalus. Since this

topic is so pervasive in the design process, discussion of safety is distributed throughout this report. Especially important at

competition are the fail-safe emergency brakes and E-stop interlocks. The vehicle is hardware limited to approximately 5.8

miles per hour. Software is used to limit the top speed to 5 mph to conform to competition rules. As part of the design

program at Virginia Tech, safety is also taught and emphasized in all student machine shops, welding shops and laboratory

work. All students who performed machine work were machine shop certified through Virginia Tech.

Fuses added at each battery and on each power bus protect against power surges in the electrical system. An

emergency stop control, located at the rear of the vehicle, opens two relays (one for each motor) disconnecting power to each

motor. In addition, the motor brakes are automatically triggered once power is disconnected from the motor, stopping the

vehicle immediately. With a range of approximately 30 feet, a remote emergency stop feature is programmed onto a wireless

remote joystick. When activated, the software detects that an emergency stop command has been activated and sends zero

state power to the motors.

Software

Data Acquisition, Manipulation, and Interpretation:

To command the drive system to follow lanes and avoid obstacles, raw data must be acquired, processed, and fused

by software running on a computer onboard Daedalus. Reliable, modular base software, developed by previous Virginia

Tech students and tailored to fit the needs of Daedalus called Navigation Manager, is used to perform this task. To acquire a

visual, two-dimensional image of the immediate area ahead of the vehicle, a Sony 8mm color camcorder is used. Using a

Sick LMS-100 Laser Rangefinder, three-dimensional obstacles are detected that may not be interpreted from camera data.

Both sets of data are then fused together into a vector field histogram. Statistics is then used to determine the best vehicle

path. During the Navigation Challenge portion of the competition, a differential GPS system combined with dead reckoning

provides the means to navigate between waypoints. In combination with the vector field histogram, obstacles preventing a

straight path between waypoints are determined and avoided. This section describes how Navigation Manager interprets data

from the sensors described above, and based on those interpretations, commands the vehicle to follow painted lanes, avoid

obstacles, and seek waypoints.

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Figure 10. Navigation Manager Interface

Navigation Manager Software:

The Daedalus team adapted the modular Navigation Manager software to fit needs set forth at the onset of design.

Using Microsoft Visual C++ under Windows 98, complex

elements of past navigation schemes are eliminated, such as

simultaneous frame grabber processing for two cameras, while

fine-tuning and tailoring software parameters to fit Daedalus

hardware. Built -in, real-time access to sensor and operational

data, available through on-screen displays, aided the team in its

customization and testing processes. Figure 10 shows a sample

interface of the software, illustrating its autonomous mode

operation options.

Lane Following and Obstacle Detection:

To navigate between a set of yellow or white painted lines, solid or dashed, Navigation Manager applies image

processing to frame grabber-converted digital images acquired by the camera. This processing includes running through

algorithms that convert the image to grayscale, blur the image, apply threshold, and then decimate the image. This

processing is performed to search for the relative light and dark features in the newly acquired image, while ignoring

everything else. The navigation software interprets the brightest pixels as two-dimensional obstacles, such as lanes and

potholes and will command the motors to steer to avoid them. If no lanes or potholes are discovered, the vehicle continues to

move forward in a straight path. If an entirely bright image is acquired, the vehicle interprets this as a sand trap and moves

forward until another lane or obstacle can be distinguished. Image acquisition and processing to recognize a bright white

lane in a grassy field is shown in Figure 11.

Figure 11. Image Processing To Detect Lanes and Obstacles

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To further improve accuracy in obstacle detection, a Sick LMS-100 laser rangefinder scans for three-dimensional

objects in front of the vehicle over a 180-degree range in half-degree increments. This scanning improves the accuracy of

navigation decisions and overall safety by detecting obstacles that may be path inhibitive, but not bright in color. When a

three-dimensional obstacle is detected, such as a barrel, the rangefinder returns the distance and angle to the obstacle and

Navigation Manager can calculate the placement of that object within the vehicle’s field of view. Information gathered from

the laser range finder and camera is passed to the Navigation Manager software, which then fuses all data and makes

informed navigation decisions.

The fusing of both camera and laser rangefinder data is accomplished by an algorithm that combines the collected

obstacle data into a composite Vector Field Histogram. This polar plot of obstacle density at all angles within the vehicle’s

field of view allows Navigation Manager to select the “best path” for Daedalus to traverse. If a path suitable for forward

travel is not found, the vehicle pauses due to possible glare or noise. If after a short period of time the glare does not subside,

a trap is recognized and escape function called based on the data. A sample of the Vector Field Histogram data is shown in

Figure 12. In this screenshot of the vehicle running in autonomous mode, the “best path” based on fused data is shown in

red.

Figure 12. Fused Vector Field Histogram Showing “Best Path” in Red

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Global Positioning System (GPS):

Many considerations were given to select a method of determining Daedalus’s position for the Navigation

Challenge. The two most promising methods incorporated using dead reckoning analysis or a Global Positioning System.

Dead reckoning analysis is the method of determining position points used by former vehicles. This method

calculates the position of a vehicle by using the velocity and direction data that is used to drive the motors. The accuracy of

this method degrades with the occurrence of wheel slip on the driving surface. While a portion of wheel slip occurs during

straight path travel, a great magnitude transpires while the vehicle turns. During last year’s competition this error was

significant as the challenge took place on grass, which provides poor wheel traction. The 10th Annual IGVC competition will

occur on pavement, which has a much higher coefficient of friction. While some wheel slip is inherent on any surface, Team

Daedalus expects the magnitude of dead reckoning error due slip on pavement to be low.

In past competitions, Global Positioning System (GPS) was utilized in the Navigation Challenge. GPS receives

signals from multiple satellites and triangulates the data to determine the location of the vehicle. This year, IGVC rules allow

teams to use a Differential Global Positioning System (DGPS). Such a system receives a differential correction signal sent

by land beacons to improve data acquired by GPS. These signals are statistically analyzed to provide optimum positioning.

By utilizing the advantages of both approaches to determine position, Daedalus is expected to complete all

waypoints in the Navigation Challenge. Dead-reckoning and DGPS is combined using a weighted algorithm embedded in

the control code. As shown in Figure 13, the estimated error for the dead reckoning analysis is in red, while the DGPS

system, in blue, holds a constant sub-meter error. Also shown in Figure 13 in green is the approximate algorithm used to

combine the two methods. The Differential Global Positioning System that Daedalus uses is an all-inclusive Trimble

AgGPS-132. The algorithm favors dead reckoning analysis at the beginning of travel. As dead reckoning accumulates error,

the algorithm will transfer weight to the DGPS unit.

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System Postion Error

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Displacement

Err

or

Dead Reckoning Analysis

Differential GPS

Combined Algorithm

Figure 13: Position error with displacement using different methods of waypoint navigation.

Testing

The results of several tests on mock Autonomous Challenge and Navigation Challenge at Virginia Tech show

favorable results in Daedalus’s performance. An obstacle course was set up to include 8’ lanes with solid and dashed

boundary lines. Obstacles placed within the lanes included a painted pothole, cardboard boxes, a barrel trap, and several

construction cones. The vehicle’s camera position, angle with the horizontal, and amplifier gains were optimized during

preliminary tests.

Further observations during testing confirmed the need to move the laser range finder from 2’ to an adjustable 2” to

16” above the ground. Once these changes were made, the vehicle successfully traversed 9 out of 10 laps in the mock course.

GPS testing has been accomplished in parallel with autonomous mode tests to aid in the development of the

aforementioned control algorithm for the Navigation Challenge. Testing has given Team Daedalus an idea of a few problem

areas that the team will work to resolve before competition such as the vehicle’s handling of a zero-possibility situation. In

repeated tests, whenever the vehicle had no “best path” to follow, the vehicle would stop. The team will continue to optimize

the vehicle’s many adjustable parameters, and will continue to perform more controlled tests before competition.

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Table 1. Estimated Retail and Academic Costs for Daedalus

Cost

Table 1 lists the estimated retail and the actual academic costs incurred by Team Daedalus in the construction of its

autonomous vehicle.

Component Retail Cost Academic CostLaser Range Finder, Sick Optics 5,000.00$ 2,000.00$ Computer, Industrial Computers 2,500.00$ -$ Motor Controller, Galil 2,000.00$ -$ (2) Drive Motors, Bodine 1,500.00$ -$ Batteries, DeWalt 800.00$ -$ (2) Amplifiers 380.00$ 380.00$ Alumimum Frame 350.00$ 350.00$ 8mm camcorder, Sony 300.00$ 300.00$ Electrical comments 200.00$ 200.00$ Lexan housing 200.00$ 200.00$ Battery mounts, DeWalt 150.00$ -$ (3) Wheels 100.00$ 100.00$ Camera Mount 50.00$ 50.00$

Total 13,530.00$ 3,580.00$

Team Information

Nicole Anthony – Mechanical Engineering Senior

Byron Collins – Mechanical Engineering Senior

Michael Fleming – Mechanical Engineering Senior

Chuck Liebal – Mechanical Engineering Senior

Michelle Nicholas – Mechanical Engineering Senior

Matt Schmid – Mechanical Engineering Senior

Total Hours: 1400