CONTROL SYSTEMS IN MODERN ROBOTICS

Session A2 Paper 1018 

CONTROL SYSTEMS IN MODERN ROBOTICS 

Derek Ebersole (dle13@pitt.edu) and Ben Guise (bag44@pitt.edu) 

Abstract – This paper will focus on the control systems used 

in mobile robotics, possessing an emphasis on controlling 

their stabilization. This paper will touch base on possible 

improvements of control systems from an engineer’s 

standpoint. 

We will initially start with the basics of enabling robots 

with ―brains‖ through the applications of control systems. 

After dissecting control systems, we will discuss how sensors 

help robots get information to make decisions. Once control 

systems and sensors are fully conveyed to the audience, we 

will then look at an example of advanced control systems 

such as BigDog: Boston Dynamic’s robot, which can 

maneuver itself over various terrains without tumbling over. 

Much of what interests us is the ability for new robots to 

detect an imbalance in their weight and make the necessary 

adjustments to withhold from falling. Through the 

application of BigDog, the significance of the continued 

research in the field will be justified. 

Key Words – Control Systems, Data Processing, Robotics, 

Sensors, Stabilization, Legged Robotics 

EARLY ROBOTICS: THE UNREALIZED DREAM 

In late 2001, the research necessary for creating a fully 

mobile intelligent robot was declared complete, and the 

fictional dreams of movies, such as the Star Wars series or 

The Terminator series, seemed to become possibilities. But 

since this time, mobile robots have stayed confined to the 

home. Robot vacuum cleaners, such as Electrolux’s 

Trilobite, possess various sensors and control systems to 

navigate around a room, keeping track of the path taken 

without bumping into anything, while cleaning every 

particle of dust. Similarly, Friendly Robotics’ Robomower 

traverses a yard without hitting any obstructions while 

evenly cutting grass to the owner’s specification [1]. 

While each of these examples possesses the basic form an 

intelligent mobile robot should have, the potential of this 

technology seems to be poorly utilized. In many cases, users 

note the inconsistent cleaning path and the unreliability these 

mobile cleaners possess. Aside from problems in design, 

these robots fail to live up to the expectation of what an 

intelligent robot should be. We still have no Terminator or 

C3PO like in Star Wars: machines that are capable of 

automatically determining the terrain they are traversing and 

acting appropriately. Therefore, the research in the field of 

robotics is not complete; otherwise the world would have 

seen the applications we have been fantasizing about. 

We have failed in reaching the science-fiction dream. 

Mobile robots need to be able to detect data with speed and 

precision, as well as balance themselves while traveling over 

treacherous terrain. Without these abilities and proper 

control systems to input and output data, robots will never 

reach the goal set forth in most science fiction films. 

THE CONTROL SYSTEM 

Types of Control Systems 

Analog systems were the first sign of what we call a control 

system. Each of these very basic systems exports only 

analog signals, generated outputs by another dynamic 

system (such as equipment or a signal generator). The 

information of the system is taken in and outputted. There is 

no computer making decisions off of this data, simply 

signal—like a switch [2]. One of the first examples of a 

control system was the centrifugal governor, an early form 

of cruise control, made by James Watt. 

FIGURE 1 [3] 

His governor used two spheres that would adjust height 

depending on the speed of the output shaft. As the spheres 

changed height they adjusted the input valve of the steam 

engine [4]. Another good example of this type of control is 

the early cruise control in automobiles. The driver chooses a 

speed to act on, and the analog switch monitors the speed. 

Once the speed is above the level, the system decreases input 

to the engine. Once the speed has decreased too much the 

system increases the throttle [5]. In the 1960s the first 

electronic cruise control was invented by Daniel Wisner 

using a microprocessor to monitor the speed of a car and 

adjust throttle levels to keep a constant speed. The advantage 

of his electronic system was the ease of integration into the 

engine control units and other computers becoming 

mainstream in vehicles at the time [6]. Therein lies one of 

biggest advantages of digital control systems—they can be 

University of Pittsburgh Swanson School of Engineering 

Eleventh Annual Freshman Conference April 9, 2011 

1

integrated into other systems and share data, therefore 

making devices more reliable, safer, and often more 

accurate. 

The next step in the complex line of control systems is 

the sampled-data system. Here, analog signals, sampled data, 

and pulse-modulated signals are put together and used 

simultaneously. An advantage to this is the sharing ability 

between equipment and signals [2]. 

The most popular of these types is the digital system, 

which houses the ability to use analog signals, sampled data, 

and digital data. This code represents values in sequences, 

which help determine the actual value of digital information. 

Computers compare data and actually “think” about what the 

best adjustment is for the system. When more complicated 

control must be utilized, the digital direction is the obvious 

choice, as it can keep track of a lot of data and give 

calculated outputs based on logic switches [2]. A good 

example is automatic flight control in airplanes. The system 

must take into account a wide variety of data, such as lateral 

height, vertical height, and various forms of disturbances. To 

account for these, the main control system is a digital 

computer that acquires data from three independent systems 

monitoring these values. This proves the luxury that a digital 

system offers, as it can read many varieties of information at 

once and make smart choices based off of them [7]. 

Control System Thought Process 

All control systems work on the same basic principle: they 

monitor output of a system and alter input to keep output 

consistent. Figure 2 illustrates the thought process behind 

how these systems go about analyzing information. An 

important factor is that this chart is a loop, which means the 

outputs and inputs are continuously monitored. In analog 

systems this is represented by mechanical or electrical 

signals and for digital systems a computer is preset to 

monitor this information at a given rate. The faster this 

monitoring process is performed, the more precise and 

accurate the output adjustments are for the system [8]. 

FIGURE 2 [2] 

A necessary ability control systems must have is quick, 

logical reaction to unusual data. An engineer designs a 

system to be efficient and safe. Safety is important, and if a 

dangerous machine needs to be stopped, the control must 

react quickly and accurately. Consider a robot lawnmower 

once more. If the machine for some reason turns over, the 

Paper 1018 

blades would need to shut down, otherwise someone could 

get hurt. A control system in this scenario would need to be 

told to shut down all systems, otherwise the mower would be 

deemed unsafe for the public. 

SENSORS: ACQUIRING DATA 

The first step in a control system is to input data from the 

system. Sensors allow robots and control systems to see 

what is happening in the environment around them. A 

variety of sensors are available for different types of 

functions including movement, vision, balance and scientific 

data collection. What is relevant for control systems 

handling locomotion are sensors that deal with the first three 

functions of the list [9]. 

Early robots used pegs at fixed points to stop movement. 

This proved inefficient over time as more technical and agile 

robots were made, and servo motors were implemented. 

Servo motors handle movement while also providing 

feedback. These motors read an encoder disk that has slits at 

defined points, as shown in Figure 3. Light sensors see when 

these points pass by and the time increment information 

gathered tells the motor how far it has turned. The robot is 

therefore able to detect the distance a leg or arm has moved, 

send this information to the control system, and make a 

decision to continue or stop motion [9][2]. Encoder disks are 

sometimes used alone as a means to find the angle of 

rotation in a system. In both applications, the more slits that 

are present in the disk, the more accurate the measurements 

are for the angles. An important note here is that in order for 

a robot to determine the change in angle, it must first know 

the number of slits present in the disk, otherwise calculations 

will be incorrect [10]. 

FIGURE 3 [10] 

Potentiometers can also be used for sensing joint 

displacements. A potentiometer functions by comparing an 

output voltage to an input voltage. These devices incorporate 

a resistive track made out of carbon film, conductive 

ceramic, or wound wire. The distance through this track that 

the electricity must travel is changed by the physical location 

of a wiper that slides across the track. This change in 

distance causes a voltage change. Potentiometers are usually 

used as volume controls in stereos and can measure a change 

in angle caused by something rotating. Linear 

potentiometers also exist, which track a distance by moving 



2

a wiper over a straight track rather than around a circle 

[2][10]. 

FIGURE 4 [10] 

Radial potentiometer 

FIGURE 5 [10] 

Linear potentiometer 

Gyroscopes, both mechanical and electrical, are used to 

determine if an object is balanced. Early examples include 

the Sperry autopilot, which use gyroscopes to handle the 

horizontal and vertical pitch of an airplane, enabling the 

pilot to walk safely across the wings without manually 

adjusting the pitch of the airplane [5]. Newer electronic 

gyroscopes are used in commercial products like the Apple 

iPhone to output the orientation of the device [11]. 

These gyroscope sensors are applications of Newton’s 

second law for rotation, which states that net torque equals 

angular acceleration multiplied by the moment of inertia. 

= Ι 

EQUATION 1 

A disk spun about a frictionless axis is suspended in a 

gimbal or a cage, which will retain its orientation. Because 

this gimbal is mounted freely in a rigid body, one can 

measure the angle between the gimbal and the moved body 

to find the pitch or roll of the object being stabilized [2]. 

Often robots require force sensors for grasping objects or 

for determining the weight of an object. This information is 

useful for finding the amount of weight supported by each 

leg of a robot. Load cells are used to determine the 

magnitude of force. These are made with a dielectric 

material between two plates. As a force acts on this system, 

the distance between the plates increases or decreases, 

inversely changing the capacitance [10]. 

C = 

EQUATION 2 

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These sensors become especially important for robots with 

legs or arms, as this allows the robot to see how much 

weight is being supported by each arm or leg and react 

according to weight shifts or imbalances. 

Vision sensors come in a variety of types from simple 

light detectors to cameras. Basic light detectors differentiate 

between light and dark; images and color are not sensed. 

These are often used with the aforementioned coding disks 

or for simple light detection. More complex image sensors 

can produce a black and white image, which can then be 

passed on to a computer for data analysis. The most 

important area for complex vision is the sensor placement 

and data processing rather than the types of sensors used 

[5][10]. 

The topic of robot vision is complicated because, while it 

is easy to input visual data, interpreting it for actual use is 

difficult and requires software. In humans, the retina 

converts light into electrochemical energy, which is then 

sent to the brain for processing. In robots a common vision 

sensor used today is a charge-coupled device, or CCD 

camera. These are ideal for robots because they are 

lightweight and small, consume little electricity, and have a 

high sensitivity over the light spectrum. A CCD array is 

made using an array of metal oxide semiconductor fieldeffect 

transistors, or MOSFETs, as seen in Figure 6. 

FIGURE 6 [12] 

MOSFET array 

The basic idea behind this type of vision sensor is that an 

array of MOSFETs stores a series of charges that correspond 

to the light intensity at that point. Positive semiconductor 

nodes are placed in a negative substrate. A depletion region 

separates each positive node, and then the entire array is 

covered in silicon dioxide. Light photons hitting the 

depletion region free electrons, which create a net positive 



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charge. Each individual charge is transferred the next cell by 

a clock pulse. This electrical signal then comprises the video 

signal [12]. 

Stereo vision, which is being employed by more robots, 

is useful because it allows for the possibility of two and 

three dimensional vision. By comparing the picture output 

by two cameras, it is possible to see depth and further 

analyze the environment [10]. However, a large part of what 

makes robot vision work is the software behind it. Any robot 

can input a picture of a tree, but it takes software 

programming for the robot to know what a tree really is. 

With all of these sensors inputting much information into 

the control system, one could argue that robots should easily 

have human like abilities in balancing, motion, and vision. 

One can give a control system all kinds of information but 

without a fast computer to interpret this information, this 

data is rendered useless. Furthermore, advanced software is 

required to enable a computer to handle these functions and 

correctly process input. 

CONTROL SYSTEMS IN OUTER SPACE 

Imagine a bullet fired from a gun in outer space. This bullet 

will continue traveling in the same direction until it enters a 

gravitational field from other masses. Now add a small 

control system whose goal is to keep it on its initial path. It 

detects motion traveling away from the path and fixes it. 

These fixes are done every now and then, but there is not 

necessarily a continual need for them. 

In outer space, once you let it go, it must fix itself; there 

is no more human directional interaction. There is an 

element of simplicity and an element of complexity in this. 

The National Aeronautics and Space Association (NASA) 

has dealt with these issues for years. Voyagers 1 and 2 are 

examples of NASA engineering at its finest, given that they 

have been operational for thirty-four years without any 

serious human interventions or breakdowns. Both of these 

probes are on a path to interstellar space and have very 

infrequent path corrections because in space, once an object 

begins to move in a certain direction, it will continue on that 

path almost infinitely. When corrections are necessary, there 

is usually enough time to perform calculations to fix the 

problem; mainly because the potential threats are detected 

long before they are encountered. All of this means that 

space probes require little computational power to operate. 

The Voyager probes use three different computers, each 

with a redundant pair, for a total of six computers in each 

probe. The total memory for all of these computers is about 

sixty-eight kilobytes, which is much less than a common 

flash drive or even a standard three and a half inch floppy 

disk [13][14]. A more recent example is Sojourner, a Mars 

rover utilizing a 100-kilohertz Intel 80C85 processor with 

512 kilobytes of memory, which was first produced in 1977 

[15]. Clearly, this is not a very powerful computer. Because 

Sojourner moves with six wheels, a very simple system, it 

Paper 1018 

does not need a complex computer. This illustrates an 

important factor in the continued development of robotics— 

robots with complex motion systems must be able to 

perform calculations to adjust movement in varying terrains 

using smaller, more powerful computers so that outside 

computing is not necessary, as this would increase the size 

and response time of a robot. 

FIGURE 7 [14] 

Depiction of the Voyager space probe 

These space-bound computers are not capable of massive 

amounts of data processing. However, any serious 

calculations could be performed on Earth and beamed up to 

the satellites given that we would have advance notice of a 

probe headed towards a large planet or other celestial body. 

One of the problems with this is the time necessary for 

transmission. A signal traveling to or from Voyager 2 

currently takes about thirteen hours to transmit one way 

[16]. This means that NASA cannot intervene quickly if 

something goes wrong; therefore, probes must be selfsufficient 

to a degree and very reliable (as the Voyager 

probes have proven to be over their thirty-four year 

missions). The problem of communication latency was 

especially frustrating in the recent incident with Voyager 2, 

where a single flip of a bit in memory from a zero to a one 

corrupted data being sent back to Earth. Because of this, it 

took NASA engineers weeks to diagnose and fix the 

problem [17]. Thanks to redundant computers and alternate 

operation modes, the spacecraft was able to continue 

operating through this situation. This shows that control 

systems and computers used in space probes and 

autonomous robots alike need to be reliable and powerful 

enough—whether that power comes through software or 

hardware—to handle even the most catastrophic system 

failures or unexpected circumstances. Furthermore, space 

probes and vehicles have been an area where low power, 

small form factor, and high reliability computers—an almost 

ideal combination for robotics—have been implemented for 

decades. 



4

STABILIZATION ON EARTH 

Gravitational Woes 

Outer space is a unique environment to work in, as there is 

no gravity. On Earth, what goes up must come down. 

Therefore, continuous fixing of direction is needed, as we 

are constantly fighting a force keeping us from hovering 

above ground. Imagine, once again, the bullet situation, only 

this time shot from a gun parallel to the ground. Even though 

this bullet is travelling at an immense speed, it is being 

pulled downward, and will hit the ground. Now imagine a 

tiny control system with rockets directed opposite gravity. 

When it detects a fall in height, the rockets push the bullet 

upwards and then let off once the original height is reached 

again, so that the bullet stays in a straight line. The 

difference between these bullet scenarios is an example of 

the continuous re-fixing that needs to be done on Earth. 

Gravity forces a repositioning of the system at almost a 

constant rate, meaning its control system needs to be fast 

enough to process the data and act quickly. Achieving 

mobility in robots is consequently made much more 

difficult, as robot body parts must be constantly adjusted to 

keep from falling to the ground, especially on ice or a 

crumbling hill [18]. 

Benefits of Robots with Legs 

What makes the gravity problem even more difficult is the 

necessity for legged robots rather than treaded or wheeled 

ones. Early robots moved on treads, which are successful in 

simple motion, but fail on steep hills and blunt objects, such 

as dragon’s teeth, shown in Figure 8. Robots with legs have 

proven to be more mobile, as their abilities to move are far 

more enhanced. Legs offer an active suspension, allowing 

the center of mass to shift around to handle various 

movements of the robot. A good example of this is a car 

with wheels. Cars will travel very quickly on a paved road, 

however in even slightly rough and unstable terrain vehicles 

with treads such as tanks are more agile. But given a steep 

cliff or constantly changing terrain such as a field of bricks 

in the aftermath of an earthquake a legged vehicle has the 

advantage because it can step over obstacles, jump, and even 

climb over changing terrain. A mountain goat can maneuver 

in a rocky environment with large boulders, but a tank or a 

car would be rendered useless. This complex system means 

that stabilizing the robot is much more difficult [18]. 

Paper 1018 

FIGURE 8 [19] 

Dragon’s Teeth—a defense structure used in World War II to stop tanks 

Animals have many attributes enabling them to balance 

themselves when standing, walking, or running, and these 

are characteristics that would enhance a robot greatly. 

Important things to note about animals are their symmetric 

body, their flexible limbs, and their coordination. Applying 

these skills to a robot is quite difficult, but the better it is 

done, the more stable and agile a robot can be. Symmetry 

does not only apply to standing, but also to walking and 

running. Flexibility does not simply mean stretching and 

shrinking, but also twisting and bending. Coordination 

between legs when walking and running is one of the most 

important factors, and keeping these things synchronized in 

robotics requires much more work than a robot with treads 

would [18]. 

Therefore, legs are inherently more complicated than 

wheels because the movements of a leg are extremely 

complex, as a wheel simply turns. As an example, wheel 

movement can be measured with an encoding disk, as 

mentioned before, while a leg requires force and traction 

sensors. Mass amounts of calculation need to be done, and 

an extremely fast control system is necessary to keep the 

robot from collapsing. 

THE COMPUTER’S POWER 

Initially, computers were too slow to be used as robot 

controllers. But once electrical and computer engineers 

switched from vacuum tubes to transistors, computers began 

to become small and powerful enough to function as robot 

controllers [9]. As we have recently seen on “Jeopardy!,” 

computers are now powerful enough to answer questions in 

under three seconds. International Business Machines, or 

IBM, created the supercomputer named Watson for this task. 

It was built using ninety IBM Power 750 servers with a total 

of sixteen terabytes of memory and 2,880 processor cores. 

This immense computing power is necessary for Watson to 



5

achieve its three-second-answer time for every “Jeopardy!” 

question. If Watson were to use one processor, it would take 

about two hours for it to answer just one question. What 

allows Watson to achieve this speed is a combination of 

hardware and software. IBM used the Apache Unstructured 

Information Management Architecture to sort through the 

large amount of information that Watson has stored on its 

hard drive. This architecture allows Watson to spread the 

processing load across all 2,880 of its processor cores at 

once. 

Watson has proven that a computer is able to interpret 

text and use data that it already knows to answer a question 

given the right hardware and software, which is great for 

business and computing applications. This is also promising 

for the field of robotics in that robots could benefit from the 

ability to process this much data all at once and to interpret 

visual commands and search an internal database to 

determine the best course of action. One way to make robots 

more capable is to enable them to make decisions on how to 

use their hardware based on the task at hand. For example, 

traversing over snow, ice, mud, and sand all require different 

movement techniques and if a robot were to move through 

this terrain without compensating for the instability of snow 

or the slickness of ice, it could get stuck or fall and become 

damaged. 

While Watson is a good example of computing methods 

for rapidly inputting information and finding the best 

solution, the fact that it takes up over 180 cubic feet and 

consumes as much power as a laundromat makes computer 

processing at this scale impossible for mobile robots. Ideally 

the parallel processing power and statistical analysis of 

Watson would be combined with the size, power 

requirement, and reliability of Voyager’s computers. 

Nonetheless, lessons such as building computers for a 

specific purpose and using distributed computing could be 

applied at a smaller scale, making robots more autonomous 

[20]. 

THE PERFECT EXAMPLE: BIGDOG 

What is BigDog? 

Finally, after discussing control systems, sensors to give 

them information, and the benefit of further engineering 

research in the speed of computers, we come to a vital 

example that exhibits all of these: BigDog. Released in 

2008, BigDog is Boston Dynamics’ four-legged robot. The 

BigDog project’s goal is to create a robot that can travel 

anywhere an animal or human can go. BigDog’s control 

system must balance, navigate, and regulate all forms of 

movement. Three areas were emphasized in the area of 

control: supporting the body through bouncing, controlling 

attitude of the body with torques, and key placement of feet 

to keep the body symmetric. By utilizing this state of the art 

technology with a complex quadruped, BigDog exemplifies 

Paper 1018 

the correct direction in which intelligent robotics research 

should be going [21]. 

FIGURE 9 [22] 

BigDog’s Sensing Ability 

BigDog has the ability to sense its surroundings and react to 

them in order to keep from falling. It also has the ability to 

detect future movements, such as good foot placement. 

When a human walks over ice, they do so carefully, looking 

for good footing while not shifting too much weight onto a 

foot. A robot can hardly know exactly where to place a foot, 

and therefore the ability to foresee future movements are 

vital. 

BigDog has been equipped with close to seventy sensors. 

About fifty of these sensors are located on the hips, knees, 

ankles, and legs, measuring aspects such as joint 

dislocations, force approximations, and servo current. 

Without these necessary sensors, BigDog would collapse 

when traversing even the easiest terrain. Aside from leg 

sensors, six others are gyroscopes used to measure the 

body's balance, while three more are used for vision 

approximations, such as avoiding obstacles, measuring the 

ground slope, or finding patterns in the motion of the 

surroundings [22]. 

BigDog has been proven to have good stability, even 

when walking across ice or up an unstable hill. These 

sensors are a large portion of the reason why the machine is 

so successful, as the data sent to the control system must be 

accurate. But, even with an extreme amount of information, 

if the computer cannot process and make decisions quickly, 

the machine as a whole will still fail. 

BigDog’s Stabilization 

As previously stated, a legged robot can travel on more 

difficult terrain than a treaded one due to its ability to 

compensate for small mounds or ditches, but the difficulty of 

balancing these robots is great. BigDog's torso weighs about 



6

100 pounds and is able to carry over 300 pounds, meaning 

that each leg must do a lot of work to hold it up, especially 

when the body is majorly off balance and certain legs are 

given much more weight to hold. In order for these things to 

happen, symmetry, coordination, and flexibility must be 

utilized so that BigDog does not plummet to the ground [21]. 

BigDog's legs are syncronized in movement. The 

machine is extremely symmetrical, taking the form of an 

actual dog: If one of the legs were made slightly different 

than the rest, the machine would be inherently off balance 

and the model would fail. When fixing its balance, the legs 

move at the same time so that any lapse in weight being held 

is alleviated. As BigDog stumbles, it knows to lower its 

body onto its knees simultaneously, lowering the center of 

mass and regaining stability, before lifting up again once 

balance is achieved. Since the legs act like the limbs of an 

animal with numerous amounts of joints, these legs are able 

to reach wherever they need to in order to provide good 

footing when balance begins to fail. 

Each of these abilities is only possible through the 

computer's power. Without ample speed for the control 

system to decide to tell the legs to bend down on the knees 

after processing data indicating the need to do so, correction 

would not happen, and BigDog would fall. Therefore, the 

most important part of the machine is the control system, as 

it must tell which body parts to move after retrieving, 

deciding, and reacting at extremely fast rates based on data 

[21]. This illustrates the importance of further engineering in 

the control system field, making their thought process faster 

and more reliable to make machines more capable. 

BigDog’s Control System 

BigDog's control system exemplifies the reasons why 

improvements in control systems are needed. Through all of 

the analysis that has been covered on sensors and stability, 

the control system has an important job in legged robots. 

BigDog's onboard computer is small and must perform 

control, sensing, data collection, communications, and the 

electric power distribution. Put together, BigDog's control 

system must basically do two things: coordinate kinematics 

to ground movement processed while correcting instability 

[21]. 

The system uses sensors from the joints to determine the 

amount of load each foot should have with each and every 

step. Posture is controlled by relating the kinematics of each 

leg to the forces of the legs in contact with the ground. With 

kinematics in relation to ground forces, BigDog's control 

system can regulate body roll, pitch, and its height above 

ground, which allows BigDog to adapt to variations in 

terrain [21]. 

BigDog tackles steep terrain by utilizing the ability just 

explained. When the path gets steeper, the control system 

leans the body forward to account for an off-based center of 

mass, as well as leaning back when the path steepens 

Paper 1018 

downward. More impressively, as BigDog runs along the 

side of a hill, the control leans the body towards the hill, 

minimizing the torque on the bottom legs, keeping the body 

more stable [21]. The ability to shift weight front to back 

and side to side proves the necessity of legged robots, as a 

treaded one would fall due to its off balance center of mass 

on steep slopes. 

All of this is just one step of a four step process. First the 

control system must retrieve data from the sensors, then 

process the data, make a decision of action, and finally tell 

BigDog's parts what to do. If this process is not performed 

fast enough, BigDog falls. On top of all of this, the control 

system must do this over and over again, as it is constantly 

fighting gravity and occasionally dealing with unstable 

terrain. What is truly remarkable about BigDog is how fast it 

can repeat this process. BigDog's sensors gather information 

about 1,000 times every second, and the control system 

performs this four step process about 200 times every 

second. The engineering behind the control has sped up the 

system’s ability to correct itself, which is ultimately the 

reason for BigDog's success in not falling [22]. 

A SUSTAINABLE SYSTEM 

Sustainability in a broader sense can be environmental in 

regards to conservation or it can involve changing the 

environment in a beneficial way. Robots such as BigDog are 

showing their value in this latter definition. Through 

application of advanced sensors, computer hardware and 

software, robots such as iRobot’s Roomba and Segway’s 

line of vehicles have come to fruition. The Roomba can 

clean houses without any more input than turning it on. This 

means that it can vacuum floors, giving its owner the time 

for other tasks. This saved time means not only a more open 

schedule for humans but also a cleaner living space since a 

Roomba can clean more frequently than a human with a 

vacuum could on his or her own. 

Also improving the human condition are Segway’s 

products, which allow people to travel through buildings, 

cities, and even nature trails with little effort. The creators of 

BigDog hope that it will benefit humans in a similar manner 

by carrying equipment through varying and difficult terrain 

that wheeled and treaded robots could not handle. Without 

the added mobility and stability enabled by the sophisticated 

control systems and computers, BigDog could not handle 

snow, steep terrain, and elevation changes present in rubble 

and rocks as easily as it does. Boston Dynamics has already 

proposed that BigDog would be useful for soldiers who 

often have hundreds of pounds of supplies and equipment to 

carry. 

Computers utilizing smaller, faster control systems can 

also make buildings more efficient, improving conditions for 

their occupants while also reducing unnecessary energy 

usage. Control systems define how economically efficient a 

process is, as it controls what is turned on and what is turned 



7

off. Companies have switched to a digital control system, 

giving them the ability to control multiple processes at the 

same time. These central computers provide companies with 

the ability to utilize more than one factor in computation 

than just one like an older system. Aspects such as heating 

and cooling systems, ventilation systems, lighting, etc. can 

all be constantly monitored to find the most economical 

setting that the company is looking for. By implementing 

faster control in smaller spaces, multiple apparatuses do not 

have to be implemented into control processes, saving 

energy [23]. 

The faster these control systems are, the more complex 

information these systems can have. Their ability to 

determine the most efficient solution at a fast pace to 

problems given will save time, money and resourses. This 

will also help to reduce the large amount of waste disposal 

some manufacturing processes posses [23]. Improving the 

speed of control will improve human life and save mass 

amounts of energy. 

Control systems have allowed for reduced waste and 

resources in manufacturing processes and thermal control in 

buildings, but the control systems themselves are also 

representing a reduction in waste. Figure 2 illustrates one of 

the first control systems used for controlling an engine’s 

speed, which was replaced by a small computer chip. 

Changes such as this from analog to digital have made 

products more environmentally friendly as they use less 

material for their internal systems. This is an oftenoverlooked 

benefit of these modern control systems. 

POSSIBILITIES FOR THE FUTURE 

The control system is an extremely powerful device, giving 

users the ability to manipulate robots and make them do 

what they want. Through the addition of advanced sensors 

giving control systems vital information for making 

decisions, robots are able to have legs. With legs, robots like 

BigDog have much greater mobility than any robot we have 

ever seen before, traveling up slopes, over ice, and through 

tough terrain. These advanced sensors and appendages 

combined with more powerful computer hardware and 

software will make robots of the future more capable, 

reliable, and autonomous than ever. Robots like BigDog 

show that we are close to having mobile robots that can 

handle some of the toughest terrain and recover from slips 

and falls without any human intervention, indicating how 

close we are to achieving the ultimate science fiction goal: 

having a C3PO or iRobot that is capable of inputting 

environment data and acting accordingly. 

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[13] (1987, April). “Voyager-The Flying Computer Center”. NASA. 

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Issue 7730, p46-48 

ACKNOWLEDGEMENTS 

We would like to thank our parents for inspiring our interest 

in the field of robotics. Also, we would like to thank our 

session co-chair Caitlin Magley and session chair Greg 

Wunderley for supporting us during the research process. 



8

CONTROL SYSTEMS IN MODERN ROBOTICS

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