Intelligent Robots for Use in Hazardous DOE Environments

David J. Bruemmer, Julie L. Marble, Donald D. Dudenhoeffer,

Matthew O. Anderson, Mark D. McKay

Idaho National Engineering and Environmental Laboratory

 Idaho Falls, ID 83415-3779, U.S.A.

ABSTRACT

In Robotics and Intelligent Machines in the U.S. Department

of Energy: A Critical Technology Roadmap, the DOE has

identified the potential for Robots and Intelligent Machines

(RIM) to greatly reduce cost, improve worker health and

safety, augment product quality and increase overall

productivity. In its long-term vision, the DOE has predicted

that such RIM capabilities will be as pervasive and

indispensable for the DOE and national interests as the PC is

today. However, for this vision to be realized, critical issues

pertaining to the interaction of humans and intelligent

machines must be further explored and new technologies

developed. In terms of time, cost and safety, ‘usability’ may

well prove to be the most crucial component of RIM systems

for remote handling of radioactive and hazardous materials

and a wide variety of other operations. In this paper we

examine the metrics used by the DOE to compare baseline

radiation survey techniques with a teleoperated robotic survey

recently conducted at the Idaho National Engineering and

Environmental Laboratory (INEEL). Further, the paper

discusses the difficulties and limitations of teleoperation

evident from this deployment. To meet the current and future

goals of the DOE, it is absolutely necessary to move beyond

teleloperation and develop robot intelligence that can be

interleaved with human intelligence to mitigate these

difficulties. In response to this need, the INEEL has developed

a mixed-initiative robotic system which can shift modes of

autonomy on the fly, relying on its own intrinsic intelligence

to protect itself and the environment as it works with human(s)

to accomplish critical tasks.

KEYWORDS: Automation, Cognitive science, Human

factors, Intelligent robots, Mobile robot dynamics, Robots.

1. INTRODUCTION

The DOE’s Environmental Restoration and Waste

Management Robotics Technology Development Program

explains that manual work within hazardous environments is

slow and expensive. Worker efficiency is low due to

protective clothing and, in some cases, exposure limits that

require work to be accomplished in several minute intervals.

Even when exposure limits are not an issue, fatigue is often

induced by confined spaces and by the highly repetitive nature

of certain tasks. The cost of a given project is increased

because of the special materials needed to protect workers and

the environment, and because of the additional wastes

generated in the form of contaminated clothing, rags, tools,

etc.. Moreover, time required to accomplish missions in

hazardous environment is adversely impacted not only by low

worker efficiency, but also by the need to prepare the workers

and instrument the site.

Consequently, the United States Department of Energy

(DOE) continually seeks safer and more cost-effective

technologies for use in decontaminating and decommissioning

nuclear facilities. As part of the FY 2000 and 2001

Large-Scale Demonstration and Deployment Projects

(LSDDP), the Idaho National Engineering and Environmental

Laboratory (INEEL) collaborated with the Russian Research

and Development Institute of Construction Technology

(NIKIMT). This collaboration resulted in the development of

the Robotic Gamma Locating and Isotopic Identification

Device (RGL&IID) which integrates DOE Robotics

Crosscutting (Rbx) technology with NIKIMT Russian gamma

locating and isotopic identification technology.

While the new robotic solution offered significant

improvements in terms of time, cost, worker exposure and the

quality of data acquired, the remote nature of this new

technology presented new human-robot interaction challenges.

Humans were required to enter the building to instrument the

environment with cameras and to assist the robot during the

execution of the task. Moreover, during the actual

deployment, the robot was only allowed to move at very slow

speeds due to the limitations of visual feedback to the

operator. In answer to these challenges, the INEEL has

developed a dynamic autonomy architecture for the same

system used in the RGL&IID deployment.

The new approach presented in this paper permits the

robot to take initiative to protect itself and the environment. In

fact, the human-robot dynamic has changed from a masterslave relationship to that of a mixed team which allows

interaction between peers. When compared with the recent

RGL&IID technology, the new mixed-initiative system will

remove much of the need for prior instrumentation, remove

the need for expert operators, reduce the total number of

operators, eliminate the need for human exposure and greatly

reduce the time needed for preparation and execution of the

task.
2. TELEOPERATED RADIATION SURVEY

Historically at the INEEL, a radiation control technician

(RCT) and industrial safety personnel first enter a facility in

order to establish accurate conditions for planning purposes.

When performing an initial radiation survey, the RCT uses a

standard Geiger-Mueller pancake probe to gather radiological

information. Once this initial entry has been completed, a

video technician may also be required to enter and collect

video coverage. Finally a team of sampling technicians is sent

into the facility to collect samples used to determine

contamination levels and identify which isotopes are present.

Typically, this data is then used to aide decontamination and

decommissioning (D&D) planning activities (see Figure 1).

Figure 1. Baseline Sample Collection for Laboratory

Analysis.

2.1 RGL&IID Deployment

To prove that remote, robotic systems could improve on this

baseline, the RGL&IID was deployed in July, 2001 at Test

Area North (TAN) 616 (see Figure 2). TAN is located at the

north end of the INEEL, about 27 miles northeast of the

Central Facilities Area. TAN was established in the 1950s by

the U.S. Air Force and Atomic Energy Commission Aircraft

Nuclear Propulsion Program to support nuclear-powered

aircraft research. Upon termination of this research, the area's

facilities were converted to support a variety of other DOE

research projects. TAN 616 was built in 1954 as a liquid

waste treatment facility. As a result of treating thousands of

gallons of liquid nuclear processing waste, there are various

levels of contamination present in the facility.

Figure 2. TAN 616.

Three rooms within TAN 616 were surveyed using the

RGL&IID: the Operating Pump Room, the Control Room, and

the Pump Room. All of these rooms are filled with process

piping and equipment at various levels, making make a

manual survey very difficult and time consuming to perform.

The intent of this demonstration was to gather empirical data

to assess the value of using a remote, robotic system. The

metrics considered included reduction in cost, accelerated

schedule, improvement in safety, and reliability of data.

2.2. Deployment Results

When compared to baseline assessment methods, the most

significant benefit of the RGL&IID deployment was the

quality of the results relative to the safety of the workers.

Although the RGL&IID deployment did not eliminate the

need for workers to enter the contaminated area, it did reduce

the need for human exposure. The RGL&IID was compared to

the following baseline activities: the initial RCT entry, an

entry to collect video, and a final entry to collect sample

information. The RGL&IID was able to collect dose

information, video coverage, and isotopes present in a single

unmanned entry.

Radiation exposure to workers supporting the RGL&IID

deployment was cut by more than a factor of 10 over baseline

activities. During baseline characterization, workers received

82mRem of radiation exposure. During the deployment of the

RGL&IID, workers received 7mRem of radiation exposure. In

addition, the RGL&IID provided radiation survey results

instantly and the complete facility survey was accomplished in

3 days. It took workers using baseline characterization

methods 3 months to accomplish the same results. The

baseline activities began in August of 2000 and were not

complete until November of 2000. Some of the results from

the laboratory analyses were not available until January 2001.

The laboratory radiological analysis confirmed the presence of

Cs-137, Co-60 and Am-241. This same data was available

within minutes after the RG&IILD performed the scan.

The deployment of the RGL&IID did require more

workers than the baseline characterization. However, during

the baseline sampling activities, six entries with as many as

six individuals per entry were made, totaling 60 work hours
spent in the contaminated area. During the RGL&IID

demonstration, only two technicians and one RCT were

required to enter the contaminated facility for a total of 10

work-hours spent in a contaminated area. All others

associated with the project were able to complete the

objectives from outside the contaminated areas. As a result of

workers spending less time in the radiation areas, individuals

involved in the RGL&IID deployment received 10 times less

radiation dose than workers involved in baseline activities.

In addition, the two technicians and one RCT who did

enter the facility during the demonstration did so only to assist

the movement of the RGL&IID up and down a flight of stairs

and to check air quality prior to entering the facility. These

individuals maintained as much distance between themselves

and the highest contaminated areas as possible. In contrast,

the baseline samplers were required to come in direct contact

with the contaminated material in order to collect

representative samples.

The financial cost of collecting the radiation

measurements using the RGL&IID was about half the cost of

the baseline technology. In addition to the benefit of

significant cost reductions, this technology also generates

significantly more data. For example, whereas the baseline

survey included 10 point samples, the RGL&IID collected

about 20 scans. Each scan covers as little as one square foot

or as much as several square feet and may have as many as 64

point measurements. Altogether the RGL&IID deployment

resulted in over 200 point measurements that covered over

100 square feet of wall and floor area. The RGL&IID has the

capability of providing 100% coverage if needed.

2.3 Limitations to the Teleoperation Approach

Although the 2001 robotic deployment offered a means to

reduce human exposure, it did not fully remove the human

from the hazardous environment or make it possible for a

single human to control the robot. In fact, the baseline survey

required only three people, whereas the RGL&IID required

six. If robotic systems are to be truly cost-effective and

efficient, this ratio of six humans to one robot must be

reduced.

Moreover, the data presented above says nothing about the

inherent limitations and risks of teleoperation. Teleoperation

requires high-fidelity video, reliable, continuous

communication, and costly, dangerous efforts to instrument

the environment a priori. As a mechanical ‘subordinate,’ the

robot was dependent on continuous, low-level input from a

human and was poorly equipped to cope with communication

failures or changes in operator workload. In fact, while

training within a mock-up facility, operators lost control of the

vehicle due to a communication failure. Since the last

command received by the robot before communications were

lost had been a forward acceleration command, the robot

continued to accelerate across the room and actually ran right

through the walls of an adjacent test bed environment. As a

result, the robot’s control system was immediately changed to

have a “watchdog” system that halted the robot once it

recognized that communications had failed.

Even so, communication proved to be the limiting factor

governing human-robot interaction during the teleoperated

deployment. Thick concrete shielding, typical to radiological

controls, made it extremely difficult for high-bandwidth

communication to support the strictly teleoperated system. As

a result, it was necessary for a human to physically place a

large antenna directly into the opening of the TAN 616

building. As the robot traveled further from this antenna, the

possibility of communication dropouts increased. In fact,

operators completely lost contact with the robot at one point

during the deployment when the robot traveled out of range.

The robot stopped after several seconds once it recognized

that communication had been lost. Since the robot was merely

a passive tool, it was unable to reorient itself or attempt to

reestablish communication. If humans had been unable to

enter the environment, the robot would have been lost forever

and unable to complete its task. Fortunately, a human was able

to move the antenna slightly further into the doorway of the

building and communication was reestablished.

The 2001 RGL&IID deployment required weeks of

preparation including training operators in mock-up

environments. Early on, these training exercises indicated that

cameras positioned on the robot would not be sufficient to

support teleoperation. The camera could not see the immediate

obstacles surrounding the wheels – the very obstacles that

posed the greatest threat. As a result, it was necessary to

instrument the environment a priori with elevated cameras.

These cameras were tethered to allow sufficient bandwidth for

high resolution video and were set up in the environment by

humans. Human placement of tethered cameras is a common

practice in nuclear remote inspections throughout the DOE

complex. This drawback to teleoperated approaches is further

pronounced by the fact that these cameras must be bagged and

produce additional contaminated waste once the operation is

complete.

Although the cameras were deemed sufficient for the task,

operators explained that such a strategy is inherently limiting.

The first limitation is that adequate lighting is required to

support vision-based teleoperation. Secondly, such cameras

are usually unable to provide complete visual coverage. In

fact, operators reported blind spots when using the same

robotic system and cameras within a different, larger building

at the site. In one instance, as the robot rounded a corner and

left the visual field of one camera, the last thing the operators

saw was the robot begin to tip over. Fortunately, the robot

righted itself and was able to complete the task successfully.

Nonetheless, the incident emphasizes the need for the robot to

provide better feedback and, ideally, to be able to take

initiative to protect itself in critical situations.
3.0 MUTUAL INITIATIVE CONTROL

Throughout the DOE complex, teleoperated systems have

often failed to address the limitations of telepresence inherent

to current communication technologies. On the other hand,

attempts to build and use autonomous systems have failed to

acknowledge the inevitable boundaries to what the robot can

perceive, understand, and decide apart from human input.

Both approaches have failed to build upon the strengths of the

robot and the human working as a cohesive unit. In response

to limitations of both approaches, research efforts at the

INEEL have developed a novel robotic system that can

leverage its own, intrinsic intelligence to support a spectrum

of control levels. We submit that rather than conceive of

machines as mere tools or, on the other hand, as totally

autonomous entities that act without human intervention, it is

more effective to consider the machine as part of a dynamic

human-machine team. Within this team, each member is

invested with agency – the ability to actively and

authoritatively take initiative to accomplish task objectives.

Within this schema, each member has equal responsibility for

performance of the task, but responsibility and authority for

particular task elements shifts to the most appropriate

member, be it human or machine. For instance, in a remote

situation, the robot is usually in a much better position than

the human to react to the local environment, and consequently,

the robot may take the leadership role regarding navigation.

As leader, the robot can then “veto” dangerous human

commands to avoid running into obstacles or tipping itself

over.

The resulting robotics system, pictured in Fig. 3.,

including hardware, software, and interface components, can

slide between roles of ‘subordinate,’ ‘equal’ and ‘leader.’ The

ability of the robot to change its level of autonomy on the fly

supports changing communication, cognitive, perceptual and

action capabilities of the user and robot. With the new system,

communications dropouts no longer result in the robot

stopping dead in its tracks or, worse, continuing rampant until

it has recognized that communications have failed. Instead, the

robot may simply shift into a fully autonomous mode.

 For this system to meet its goals, we must provide robust

mechanisms which allow the robot to protect itself and the

environment. To do so we fuse a variety of range sensor

information including inertial sensors, compass, wheel

encoders, laser range finders, computer vision, thermal

camera, infrared break beams, tilt sensors, bump sensors,

sonar, and others. The robot does not assume that these

sensors are working correctly, but rather continuously

evaluates its own perceptual capabilities and behavior. Novel

sensor-suites and fusion algorithms enhance capabilities for

sensing, interpreting, and "understanding" environmental

features. Also, a great deal of work has focused on providing

situation awareness to the user that can appropriately support

the current level of interaction. With the new system we are

not limited to visual feedback. Instead, the robot is able to

abstract information about the environment at many levels

including terse textual descriptions of the robot’s local

surroundings.

Given the desire to employ robots in hazardous, critical

environments, the ability to shift a robot in and out of the

leadership role presents a conundrum. The user comes to rely

on the self-protective capabilities of the robot and yet, at

times, must override them to accomplish a critical mission.

For instance, when faced with an unknown boxobstructing the

path, the user may shift the robot out of the leadership

responsibility for navigation, but grant the robot the “right” to

refuse human commands when the physical resistance to

motion is beyond a certain threshold. This allows the human

to attempt to push the box out of the way without exerting

dangerously high force on the robot. For other tasks, the user

may need to drive the robot to where it is touching an obstacle

in order to take a sample. The user can curtail the robot’s

collision avoidance initiative and yet customize a “last resort”

channel of initiative based on bump sensors and short-range

infrared break beams.

Ideally, we need control systems that allow the user to

configure the autonomy of the robot on the fly, activating

“channels of initiative” that crosscut broad categories. The

roles of each team member are bounded by a complex and

changing web of capabilities and limitations to which each

member must adapt and respond. The ability of the human to

develop accurate understanding of robot behavior is essential

if this adaptive role switching is to work effectively. One of

the most fascinating areas of future work is the need for the

robot to be imbued with an ability to understand and predict

human behavior.

3.1. Theory of Robot Behavior

The need for human and robot to predict and understand one

another’s actions presents a daunting challenge. For each level

of robot initiative, the user must develop a unique set of

expectations regarding how the robot behaves, that is, an

understanding or theory of the system’s behavior, here after

referred to as a theory of robot behavior (TORB). By TORB

Figure 3. Current robot configuration.
we mean that the human operator is able to quickly and

accurately predict:

1. Actions the robot will take in response to

stimuli from the environment and other team

members;

2. The outcome of the cumulative set of actions.

In our research we are not concerned with developing a

formal model of robot cognition, but rather require that the

human understand and predict the emergent actions of the

robot, with or without an accurate notion of how intelligent

processing gives rise to the resulting behavior. When a

human team member is faced with a robot that can orchestrate

task elements, the critical issue will not be how the robot or

machine “reasons,” but rather whether the human team

members can accurately predict robotic responses and

understand how cumulative actions and responses converge to

fulfill task objectives.

Many applications require the human to quickly develop

an adequate TORB. One way to make this possible is to

leverage the knowledge humans already possess about human

behavior and other animate objects, such as pets or even video

games, within our daily sphere of influence. For example,

projects with humanoids and robot dogs have explored the

ways in which modeling emotion in various ways can help (or

hinder) the ability of a human to effectively formulate a

TORB [1],[2].

Regardless of how it is formed, an effective TORB allows

humans to recognize and complement the initiative taken by

robots as they operate under different levels of autonomy. The

ability to predict and exploit the robot’s initiative will build

operator proficiency and trust. The development of a theory of

robot behavior will also allow the user to switch between and

configure the robot’s levels of initiative to suit the needs and

components of the task at hand.

3.2. Theory of Human Behavior

Just as the human develops a theory of the robot’s behavior,

the robot must be able to understand and predict the human

members of the team in order to adapt to their needs. This is

not to say that machines must possess complex mental models

or be able to discern our intentions. Rather, it is necessary to

raise the level of interaction between the human and robot

based upon readily available, non-intrusive workload cues

emanating from the operator. The robot’s theory of human

behavior may be a rule set at a very simple level, or it may be

a learned expectation developed through practiced evolutions

with its human counterpart. The robot must possess some

means to infer the need for intervention. Currently, accurate

and non-intrusive collection of these cues is difficult at best,

and those measures that have been used are unreliable at worst

[3].

The answer to this dilemma is to reduce the human signals

down to a prescribed set of channels, which are available as an

integral part of the interaction of the human with the machine,

and which the machine can use to configure its behavior and

level of initiative. Interaction between the robot and human

may be through direct communications (verbal, gesture, touch,

radio communications link) or indirect observation (physically

struggling, erratic behavior, unexpected procedural deviation).

Interaction may also be triggered by the observation of

environmental factors (rising radiation levels, the approach of

additional humans, etc.). The robot’s expectations must allow

it to recognize human limitations and anticipate human needs

without second-guessing the human’s every move. When

robots do intervene with their human counterparts, the

human’s TORB must be able to explain why the robot has

stepped in and what this shift in control means for the task at

hand.

3.3 Dynamic Role Changing

The benefits of allowing the team members to change roles

within the team significantly increases team flexibility and

reliability in task performance. However, if the interface and

human-robot system are not designed in accordance with

critical principles of human factors in mind, dynamic role

changing may result in mode confusion, loss of operator

situation awareness, loss of operator confidence in assuming

supervisory control, and degraded and potentially catastrophic

performance [4]. Systematic human-centered design is

necessary to insure that the robot autonomy conforms to the

ways in which humans assign and manage tasks.

Appropriate feedback is required when roles and levels of

initiative change. Failure to inform the operator when the

robot has overridden commands will lead to distrust of the

system, unless the behavior is beneath the level of operator

concern. This phenomenon has been studied within the airline

industry with pilots and the automatic pilot mode of operation.

[5]. Feedback from the robot should not only include the mode

change, but also an indication of the reason for the change.

For optimal performance of the team, the human must be able

to develop expectations regarding when and why the robot

will be motivated to initiate a new level of initiative. In order

for the human’s theory of system behavior to comprehend and

exploit robot initiative, the robot’s autonomy should be

structured hierarchically such that at any given time, the user

will know the bounds on what initiative the robot can take.

Consequently, the INEEL has developed a control system that

supports four clearly distinct levels of human intervention.

4. INTELLIGENT AUTONOMY

Within the last five years, researchers have begun in earnest to

examine the possibility for robots to support multiple levels of

user intervention. Much of this work has focused on providing

the robot with the ability to accept high level verbal, graphical,

and gesture-based commands [6], [7], [8]. Others have

implemented robots that understand the limitations of their

autonomous capabilities and can query the user for appropriate
assistance [9], [10]. Goodrich et al. [11] have performed

experiments which involve comparing the performance of

human-robot pairs using different modes of human

intervention. However, very little work has emphasized true

peer to peer interactions where the robot is actually able to

shift modes of autonomy as well as the user. Sholtz [12]

discusses the need for this kind of peer-peer interaction, and

provides categories of human intervention including

supervisory, peer to peer and mechanical interaction (e.g.

teleoperator). Our research to date has developed a control

architecture that spans these categories, supporting the

following modes of remote intervention:

1. Teleoperation;

2. Safe Mode;

3. Shared Control;

4. Full Autonomy.

For each of these levels of autonomy, perceptual data is

fused into a specialized interface (shown in figure 4) that

provides the user with abstracted auditory, graphical and

textual representations of the environment and task that are

appropriate for the current mode. Currently, this interface is

used on a touch screen tablet PC made by Fujitsu Corp..

Within this interface, blockages are shown as red ovals and

resistance to motion is shown as arcs emanating from the

wheels. The robot relays a great deal of synthesized, highlevel information (including suggestions and requests for help)

to the user in a textual form using the feedback textbox within

the image window. Also note that the robot provides textual

reports on environmental features at the bottom of the map

window and reports on communications status at the bottom

of the robot status window. The robot status window provides

a variety of information about the status of the robot including

pitch and roll, power, heading, speed and a fusion of this

information into a single measurement of “health.”

The user can move the robot by touching the arrows or

may use a joystick or other game controller. It is possible to

pan and tilt the camera automatically by touching regions of

the visual image. Currently, we are still working to integrate

Figure 4: Current interface used for mixed-initiative control of the robot
the on-the-fly mapping capabilities with the interface shown in

figure 4. As we continue this task, the interface will allow a

number of autonomous tasks (e.g. searching a specified region

or going to a goal location) to be issued by interacting with the

map itself.

4.1. Teleoperation

We have taken the interaction substrate used in previous

INEEL teleoperated robotic systems and revamped it through

feedback from people who have deployed such systems.

Within teleoperation mode, the user has full, continuous

control of the robot at a low level. The robot takes no initiative

except to stop once it recognizes that communications have

failed.

4.2. Safe Mode

Within safe mode, the user directs the movements of the robot,

but the robot takes initiative to protect itself. In doing so, this

mode allows the user to issue motion commands with

impunity, greatly accelerating the speed and confidence with

which the user can accomplish remote tasks. The robot

assesses its own status and surrounding environment to decide

whether commands are safe. For example, the robot has

excellent perception of the environment and will stop its

motion just before a collision, placing minimal limits on the

user to take the robot’s immediate surroundings into account.

The robot also continuously assesses the validity of its diverse

sensor readings and communication capabilities. The robot

will refuse to undertake a task if it does not have the ability

(i.e., sufficient power or perceptual resources) to safely

accomplish it.

4.3. Shared Control

The robot takes the initiative to choose its own path, responds

autonomously to the environment, and works to accomplish

local objectives. However, this initiative is primarily reactive

rather than deliberative. In terms of navigation, the robot

responds only to its local (~ 6-10 meter radius), sensed

environment. Although the robot handles the low level

navigation and obstacle avoidance, the user supplies

intermittent input, often at the robot’s request, to guide the

robot in general directions. The problem of deciding how and

when the robot should ask for help has been a major line of

HRI enquiry and will be a major issue in our upcoming human

subject experiments.

4.4 Full Autonomy

The robot performs global path planning to select its own

routes, requiring no user input except high-level tasking such

as "follow that target" or "search this area” specified by

drawing a circle around a given area on the map created by the

robot. This map is built on the fly and uses frontier-based

exploration and localization to perform searches over large

areas including multiple rooms and corridors. The user

interacts with the map to specify tasks and can guide the robot

and infuse knowledge at an abstract level by selecting areas of

interest and identifying sensed environmental features, which

then become included within the map.

 These levels of operator intervention can greatly improve

on the opportunities provided to the operators of a strictly

teleoperated system such as the one used in the RGL&IID

deployment. The human user can switch between these modes

to cope with different components of the task. For instance,

when a user wishes to move into a new room s/he simply

points the robot at a door and then allows the robot to guide

itself through the doorway – a task that reportedly took

teleoperators many minutes of trial and error.

The latest development, and perhaps the most innovative

aspect of our project to date, is that we have imparted a

"theory of human behavior" within the robot's intrinsic

intelligence, which allows the robot to assess human

performance. Before we implemented this theory of human

behavior, the robot was already able to use its knowledge of

the environment and its own proprioception to take initiative

and refuse to accept dangerous commands. However, the level

of robot initiative was always controlled by the human. The

“theory of human behavior” allows the robot to switch modes

when the robot recognizes that the human is performing very

poorly. This theory of human behavior is based primarily on

the frequency of human input and the number and kind of

dangerous commands issued by the user. For instance, if the

human has repeatedly placed the robot or the environment in

danger, or if the human has been unsuccessful in extricating a

robot from a cluttered area, the robot will step in and take over

from the operator. Although the human can ultimately

override this capability, it provides a means for true peer-peer

interaction.

5. PERFORMANCE METRICS & OPERATIONAL

IMPACT

The Department of Energy’s Robotic and Intelligent Machine

(RIM) Initiative has set forth a number of functional

objectives to be achieved using robotic and intelligent

systems. Some of these metrics include:

- Reduction in exposure to specific hazardous materials;

- Reduction in monitoring costs;

- Secondary waste reduction;

- Productivity increase;

- Production defect reduction. [13]

While these metrics represent increases in “performance”

they do not necessarily reflect the full impact of inserting an

“intelligent” system into an operation over existing humancentered tasks. Several additional areas that must be

considered which are evident within the testing conducted at

the INEEL include:

- Operator trust;
- Operator job satisfaction;

- Revised group organization;

- Task skill set adjustment;

- Training re-alignment

- Preparation;

- Consequence of asset loss and contingency / recovery

plans.

Consider the reduction of personnel exposure within the

INEEL experiment, decreasing from 82mRem to 7mRem and

the reduction of total survey time from 3 months to 3 days.

These represent dramatic performance gains. Now consider

the fact that although remote controlled, the operation of the

RGL&IID actually required more personnel, albeit in different

roles. The RGL&IID deployment did not eliminate the

necessity to utilize a human element for compartment entry,

but changed his task from conducting a survey to that of

helping the robot ascend and descend a flight of stairs. Also,

specific training was required in a mock environment to

support the operator’s new task of teleoperation.

In addition to the work that the INEEL is doing in remote

characterization, some of these same impacts can be seen in

the U.S. Air Force and its deployment of the Predator UAV.

While Bosnia and Afghanistan have proven the worth of the

Predator in remote sensing and ordnance delivery, the Air

Force is currently trying to adjust to its operational and

organizational impact. Currently being assessed by the Air

Force is the proper skill mix for pilots, the correct crew ratio,

and training. [14] Additionally, consider the aspect of job

satisfaction for a pilot used to flying a plane in the midst of the

action now confined to monitoring a Predator control panel

miles from the front.

The introduction of an intelligent system be it a mobile or

embedded system must be view not only in terms of specific

task performance, but also in relation to the overall impact that

the system imparts upon how “business” was done in the past.

While this change in not necessarily bad or good, it must be

examined.

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