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Oct 31, 2013 (3 years and 9 months ago)

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Final Report

NASA NAG 5
-
3990

Principal Investigators:

Norman I. Badler, University of Pennsylvania

Dimitris N. Metaxas, University of Pennsylvania

Dava J. Newman, Massachusetts Institute of Technology


August 24, 2000


Executive Summary


Our NRA NAG 5
-
3990

project had three overall goals. The first was to investigate
dynamic simulation techniques tailored to microgravity IVA and EVA activities. The
second project goal was to produce and evaluate a human performance model in a
realistic NASA mission situat
ion. The third goal was to develop a representation and
software infrastructure to support the simulation of virtual autonomous crewmembers for
training and task evaluation. This technique would be used to measure and evaluate task
feasibility as well as d
evelop new methods of task assignment in space related activities.
All of these projects would enhance ground
-
based IVA and EVA procedure design as
well as predict payload
-
handling difficulties. Moreover, the potential predictive power of
workload models w
ill enhance our computational understanding of other Earth
-
bound
activities. We developed an efficient human motion planning method based on recursive
dynamics and optimal control techniques, which are subjected to minimum torque
criteria. We also performe
d workload analysis (metabolic load, stamina, and fatigue).
Applying biomechanical modeling and physics
-
based dynamic simulation can establish
analytic and predictive measures for IVA and EVA space human factors. This project
extended existing dynamic, ant
hropometric, and kinematic models for human motion.


Outcomes

Our three project thrusts on NRA NAG 5
-
3990 were:



To investigate dynamic simulation techniques tailored to microgravity IVA and EVA
activities.



To produce and evaluate a human performance model
in a realistic NASA mission situation.



To develop a representation and software infrastructure to support the simulation of virtual
autonomous crewmembers for training and task evaluation.


The first item is reported in Appendix 1 as a paper presented at t
he 1999 US
-
Japan Space Human
Factors meeting. The second item is reported in the discussion below, as it has the most direct
bearing on NASA mission issues. Two papers developed from this task are included as
Appendices 2 and 3. The third item is also s
ummarized in the discussion below, and elaborated
in Appendix 4.


An Evaluation of Human Performance using Dynamics Models

Astronaut performance, specifically extravehicular activity (EVA) tasks, were investigated and
modeled numerically. Contributions inc
luded human factors insights that come through
modeling as well as a physics
-
based astronaut model and a mathematical representation for the
extravehicular mobility unit (EMU), or space suit. Computational multi
-
body dynamics were
used to simulate astronau
t extravehicular activity (EVA) tasks. Two actual EVAs were
simulated: manipulation of the Spartan astrophysics payload on STS
-
63 (large mass handling)
and attempts at capturing a spinning Intelsat VI satellite on STS
-
49. This research effort fills a
cur
rent gap in quantitative analysis of EVA by employing computational dynamics, with
emphasis on Kane’s method, to solve the equations of motion for the dynamics of the astronaut’s
body segments and other interacting objects. The simulation approach can be
divided into six
phases: (1) model design, (2) system description, (3) equation formulation, (4) inverse
kinematics, (5) inverse dynamics, and (6) data display with animation. The large mass handling
simulation was performed using a relatively simple sev
en segment astronaut body model with 6
degrees of freedom and motion restricted to a single plane. Results of the modeling effort reveal
how an analyst might predict difficulties imposed by task specifications requiring violation of
physiological limits, a
nd modify the protocol so that the tasks objectives are humanly achievable.
The more complex Intelsat EVA investigation, using a 12 segment astronaut body model with 31
degrees of freedom, and interacting capture bar and satellite objects, each with 6 deg
rees of
freedom, reveals greater challenges in terms of motion control and numerical integration.
Interaction between the capture bar and satellite is modeled by means of constraint forces
imposed at two contact points and achieves realistic motion of the

two objects. This work has
resulted in a recent publication [Sch00]. Another contribution of this research effort was to
develop a dynamic model of the extravehicular mobility unit (EMU), or current NASA space
suit. The EMU model incorporates three key s
uit parameters, namely, mass, inertia and
performance for EMU components including the portable life support system (PLSS).
Replicating the Spartan EVA simulations while including the space suit model reveals that the
astronaut does nearly an order of mag
nitude more work to produce the same results when the
initial conditions are such that the lower body is fixed. Allowing for a compliant lower body
astronaut model with the suit model results in the suited and unsuited condition requiring similar
amounts
of work by the astronaut. An interesting result is that the initial conditions from which
the astronaut starts the task greatly affects the results (i.e., astronaut neutral body posture versus
EMU neutral suit posture in microgravity). Finally, a series
of simulations was performed to
assess the effect of a space suit on an astronaut engaged in repetitive motions over a long time
representative of future International Space Station (ISS) tasks. The accumulation of additional
work to overcome space suit p
roperties might lead to accelerated muscle fatigue during these
simulations. This work was most recently published and presented
20

at the International
Conference on Environmental Systems (ICES 2000).

Background

Motivation

This work fills a current gap in
quantitative analysis of EVA by solving the equations of
motion for the manipulated objects and a multisegment human model. The application of
computational dynamic simulation to EVA was prompted by the realization that physical
microgravity simulators ha
ve inherent limitations: viscosity in neutral buoyancy tanks;
friction in air bearing floors; short duration for parabolic aircraft; and inertia and friction
in suspension systems. Existing useful computer programs either produce high resolution
three
-
dim
ensional computer images based on anthropometric representations [Pri94,
Bad93], or empirically derived predictions of astronaut strength based on lean body mass
and the position and velocity of the body joints [Pan92], but none provide dynamic
analysis of

EVA tasks using the equations of motion of a multibody system model.


Several classical methods of formulating the equations of motion of a multi
-
segment
system exist. These include the methods of Newton
-
Euler, Lagrange, D'Alembert,
Hamilton, Boltzmann
-
Ha
mel, Gibbs
-
Appell, and Armstrong [Hoo65, Rob66, Wit79,
Sil82]. In fact, one of the early applications of multi
-
segment dynamic analysis to human
motion involved astronaut body orientation in weightlessness [Sch69]. Since this early
study was performed i
n 1962, computational methods had not yet been developed and it
took the analysts weeks to derive the equations of motion by hand. Other novel methods
have been developed more recently [Ram80, Fea83, Arm85]. Even more recently, some
researchers have star
ted to explore the power of computer graphics and animation in
multi
-
segment dynamic system analysis [Wil88, HODGINS????? Hua00, Lo, Hua].


A particularly efficient multibody dynamics equation formulation method has been
developed by Kane and his associate
s [Kan83a, Kan83b, Kan83c, Sch88]. An outgrowth
of their work was the development of highly efficient algorithms for multibody analysis
[Ros86] that have been incorporated in the computer program SD/FAST [Hol94]. The
simulations discussed herein use SD/F
AST to formulate the equations of motion for the
dynamic system being modeled.

Eventually, the goal of our dynamic simulation effort is to analyze EVA tasks before they
are performed in space to obtain numerical estimates of expected dynamics parameters,
s
uch as astronaut joint angular excursions and torque requirements, and to identify
possible difficulties that can be further examined using physical simulators. In this stage
of development, it is advantageous to compare simulation results with the actual
EVA as
a means of validating the computational simulation program. Unfortunately, there are
few, if any, means of obtaining dynamically relevant numerical data on astronauts
performing EVA in microgravity, therefore, it is not yet possible to make direct
c
omparisons of dynamics parameters.


The result of the first year’s research effort was a 7 segment astronaut model with a
limitation that the astronaut model did not account for the mechanical influence of the
space suit on performance. The second year’s

research effort has resulted in an enhanced
three dimensional 12 segment astronaut model and a dynamic space suit model described
in detail in following sections.


Research Objectives

Certain specific objectives were established to guide the research effo
rt and are listed
below:




Develop a convenient means of modeling the dynamics of an EVA
astronaut.




Transform the description of the dynamic system into equations of
motion represented in computational form.




Develop computer code to drive simulations of t
he dynamic
system under a variety of conditions.




Explore methods of prescribing the motions to be performed in a
task
-
oriented form, the way that an astronaut or trainer might think
of the operation, without the need to explicitly specify the
kinematics (
positions, velocities, and accelerations) of each
segment. In other words, perform an inverse kinematics analysis,
given only the motion of the endpoint of the system.




Determine the joint torques required to drive the system in
performing a particular mot
ion by using the calculated segment
kinematics in an inverse dynamics analysis.




Enhance the 7 segment astronaut model to provide realistic
motion using a 12 segment model.




Develop a dynamics model of the EMU in conjunction with the
enhanced astronaut mod
el.


Methods

The dynamic simulation approach used in this study may be divided into six phases: (1)
model design, (2) system description, (3) equation formulation, (4) inverse kinematics,
(5) inverse dynamics, and (6) data display with animation.


In the f
irst phase, model design, the analyst develops a conceptual model that represents
the dynamic system to be analyzed in sufficient detail to ensure that the desired accuracy
is obtained. This includes determination of the system geometry (number of segment
s,
their dimensions, how they are linked, and the degrees of freedom of the joints linking
the segments) and the mass properties of each segment (mass, moments of inertia, and
products of inertia when necessary).


The second phase, system description, invo
lves writing a system description file. Code
words and numerical values that fully describe the relevant geometry and mass properties
of the dynamic system are included in this file in a format that can be interpreted by a
computer program capable of formu
lating the equations of motion of the system (we use
SD/FAST).


In the third phase, equation formulation, the formulation program is executed using the
system description file as input. The output is a set of functions (subroutines) in C code
that implicit
ly represent the equations of motion of the system and aid in the analysis of
the dynamics of the system.


The fourth phase, inverse dynamics, combines the equation formulation code with user
-
written code that performs the actual simulation run. Our code

does this operation in two
parts. The first part prescribes the motion of a few selected segments (usually the
astronaut’s hands), representing a ‘task’, and solves for the motion of the remaining
segments subject to any external or internal loads (forces

and torques). If there are more
degrees of freedom than constraints in the system, the solution is found using a linearized
least squares solver. The second part prescribes the motion of the entire system, using
the results from the first part, and then

calculates the joint torques required to achieve the
prescribed motion.


The fifth and sixth phases, data analysis and animation, involves interpretation of the
results using graphical methods. Simulation data, usually position and torque time
histories,

are plotted on two
-
dimensional graphs and then evaluated for range of motion
or strength extremes, efficiency and comfort of task performance [Hua99]. Three
-
dimensional rendered animation is a powerful tool in evaluation of results. It allows the
analys
t to quickly determine whether the starting configuration and subsequent motion
fall within reason. It is also extremely helpful in determining the cause of errors or
anomalies in the simulation.


In creating the initial model of a dynamic system, it is a
dvisable that the analyst start with
the simplest possible model capable of exhibiting the required dynamic characteristics
with a reasonable degree of accuracy. Once a working model has been obtained, the
complexity (number of bodies and degrees of freed
om) can be expanded incrementally to
study other effects or increase realism. Simulations to illustrate the modeling efforts are
presented. The relatively simple seven
-
segment “astronaut” model with an additional
eighth segment representing a large payloa
d being manipulated (Spartan astronomy
satellite) was an initial step. The enhanced twelve
-
segment astronaut model with a total
of 31 degrees of freedom is modeled representing full three
-
dimensional movement
capability. The astronaut interacts with two
additional segments, each with 6 degrees of
freedom, bringing the total number of degrees of freedom to 43. Also, a dynamic
representation of the EMU is modeled 43.


A Dynamic Model of the EMU

A data
-
driven dynamic model of the extravehicular mobility uni
t (EMU) has been
constructed based on three key suit parameters: mass, inertia, and performance. Mass
data for the individual EMU components, including the PLSS, were obtained from
Hamilton Standard mass properties reports. Various moments of inertia were

then
computed based on suit dimensions, and entered as parameters in a system description
file in the SD/FAST dynamic simulation environment. Similar to existing files created
for previous simulations, these system descriptions were then used to generate

the
(passive) equations of motion for the various suit segments. These suit segments are then
attached directly to our new 12
-
segment human model, delivering the inertial loads of the
suit to the astronaut’s limbs.

Suit
-
Imposed Torques

The most profound
effect of the EMU on astronaut performance involves the imposed
torques, or “springback forces,” in the joints; these forces are generated when the suit
fails to maintain a constant volume during movement, and the astronaut does work to
change the suit pos
ition. Also, the multi
-
layer soft shell construction of the suit behaves
much like a giant winter coat: as the limbs move back and forth between joint limits, the
suit fabric tends to bunch up, and eventually the sheer volume of material compressed
into a

small volume creates a firm limit on the maximum flexibility of a joint.

EMU design, of course, attempts to minimize these effects in order to maximize suit
flexibility and astronaut mobility. For the most part, suit designers have succeeded; joint
rest
raints are quite effective in maintaining a nearly constant joint volume through the
most frequently used areas of an astronaut’s workspace, and innovative designs such as
flat
-
patterned mobility joints allow easy flexing of joints without substantial twis
ting,
bunching, or stretching of thermal micrometeorite garment (TMG) material. These joints
are basically folded pleats or tucks in the outer layer of the TMG which allow layers of
the suit to fold over themselves in an orderly fashion, rather than crump
ling in a less
efficient way.

The effects being modeled, then, are not the major suit forces associated with early space
suit designs that lacked these improvements. Rather, we attempt to model the EMU’s
deviation from an “ideal” suit, one, which would ex
ert no forces to counteract astronaut
movement. In practice, subtle changes in the orientation of joint restraints during a
particular motion can lead to minute variations in the restraint forces applied to EMU
joints. Additionally, regardless of excepti
onal joint designs, TMG fabric is thick and
bulky, and it will contribute some small countertorque to astronaut movement. These
variations lead to slightly varying joint volumes during particular sequences of arm and
leg motion, and increasing fabric bunc
hing toward the joint limits; the result is the
characteristic hysteresis joint angle vs. suit torque curve of Figure 3, obtained from
torque measurements of a pressurized suit elbow over its range of motion. NASA
personnel at both JSC and Ames Research C
enter have been instrumental in providing
this data.

It is clear from the description above that an analytical model for the “error” in slightly
non
-
optimal suit joints would be extremely difficult, if not impossible to derive from
physical principles. Th
is modeling effort has therefore focused on developing a dynamic
model of the EMU from existing suit, which presumably represents some combination of
the effects that various suit imperfections have on suit performance. Although mass and
inertial properti
es provide a more complete model, these springback torques represent the
critical element for assessing detriments to human performance in the EMU.

Hysteresis model

The hysteresis space suit model results in the torque experienced by a suited astronaut
per
forming representative tasks. The plot exhibits a significant degree of hysteresis,
revealing that suit
-
applied torques are dependent on the direction of arm motion. As with
any hysteretic system, this means that the magnitude of the torque applied by th
e suit at
any moment in time depends uniquely on the particular history of arm movements up to
that point. The soft EMU joints have a “memory,” storing the sequence of bunchings and
expansions that work the TMG into a particular orientation and energy sta
te; that unique
state then dictates the amount of energy required to move the joint to another position.

Hysteresis is a key property of the suit joint, and must be carefully modeled. The
approach is to develop a variant of the classical Preisach model fo
r hysteresis [Pre35] that
represents the hysteretic system as a superposition of simple hysteresis operators. This
allows us to represent the three dimensional (3
-
D) joint workspace as a field of values,
each representing the joint torque associated with
changing direction of motion at a
particular point in the workspace. Integration over the recently visited areas of the
workspace then builds a magnitude for the total torque currently delivered by the suit.
These computations were performed for the elbow
, knee, and shoulder EMU joints.

Conclusion and Recommendations

In cases where the modeling approach appears sound, we have been able to show how the
analyst might explore alternative EVA task scenarios to achieve the task objectives
without violating phys
iological limits, and this is an eventual goal for contributions to
EVA task analysis and training. The large mass handling simulation provides an example
of how the analyst is able to recommend changes in the manner in which EVA task is
performed to avoid

violation of joint range of motion and astronaut physical strength.
For instance, when it was observed that the initial task protocol would have resulted in
wrist angular excursions that exceed the ulnar deviation limit, astronaut’s starting body
configu
ration was changed to bring the angular excursion to within the physiological
range. Since the wrist strength limits were also exceeded by the first simulation run, the
size and speed of the manipulation trajectory were reduced until the required torques
fell
within the physiological range.


Joint range of motion limits were built into the more complex Intelsat simulation by
means of joint stops modeled by stiff springs. This simulation extended the task space to
three dimensions and the astronaut model c
losely approaches the number of degrees of
freedom in the true human body (without counting the degrees of freedom in finger and
toe joints). With the extension in versatility and complexity came added complications in
the control and numerical computatio
n of the simulation.

This research effort focusing on EVA simulations is aligned with NASA’s Critical Path
(
http://criticalpath.jsc.nasa.gov
) and points to important human factors issues that should
be take
n into account during EVA design. Living and working in an isolated, confined
and closed environment could jeopardize crew health and safety as well as mission
objectives. The risk factors in the areas of human/system interface and EVA are pertinent
to our

research effort. An example that arises comes from the postures suggested by the
large mass handling simulations, which indicate that ankle support may be a necessary
part of future EVA foot restraints. Alternatively, different boot designs could be
inco
rporated into the model for rapid
-
prototype analysis. The Spartan simulation also
aids in task definition, restricting the available workspace by identifying regions of
increased work and decreased task efficiency. These simulations can be used to develo
p
a more comfortable working environment for the EVA astronaut, and help identify
specific EVA tasks, which might lead to unanticipated problems. The EMU model
enhances the realism and validity of 6
-
dof dynamic simulations involving the execution
of EVA t
asks.

Creating Real
-
Time Autonomous Embodied Agents

Background


A challenging research area is virtual reality systems suitable for training interpersonal
interactions. In such a system, at least one person is the VR participant while one or
preferably se
veral more virtual human agents are engaged in activities in the same virtual
space [Cap99]. The participants, whether live or virtual, should interact as if all were
real. This means that the virtual agents must have several characteristics afforded to
real
people:



They should exhibit sufficient situation awareness to precipitate relevant and
appropriate decision
-
making. They should have human
-
like attention and, if
possible, perceptual capabilities. At the minimum they should be able to ``see''
the ac
tions of others and make plausible assessments of behavior, action, and even
intention.



They should understand verbal instructions for action, the cessation of action, or
the behaviors appropriate to future situations (standing orders). Training for
leader
ship will require issuing orders, and the virtual agents must be
knowledgeable about the content, meaning, and applicability of those orders.
This is partly a natural language communication problem, but more importantly, a
resolution of an interpreted ins
truction against the situational context of the virtual
agent.



They should exhibit gestures, movements, and facial expressions appropriate to
the primary actions they are engaged in, and these should appear credible and
situationally meaningful to the live

participant.

These three requirements demand significant improvements over embodied avatar
representations used in today's VR systems. While superficial appearance (shape, attire,
equipment complement) is certainly important to the live participant's pe
rception of the
other virtual beings, their appropriate behavior is arguably more crucial to training
success. An approach to action selection (and execution) through situation awareness
and on
-
line instructions is the research area we deem most critical
to the next generation
of VR training systems.

Needless to say, these capabilities must execute in real
-
time for effective training.
Authoring training scenarios must therefore not tax the abilities of subject matter experts
and trainers to change the cou
rse of the simulation as it is running. Capabilities (1) and
(2) are the keys to on
-
line behavior modifications of virtual agents. Capability (3) lets us
manifest internal states (observation, deliberation, and decision
-
making) of the virtual
agent into
human
-
like external actions. In this NASA project we developed an
architecture for real
-
time embodied agents which addresses the creation and simulation of
autonomous behaviors.

Methods


In Appendix 4 we describe the Parameterized Action Representation (P
AR) architecture
and the system that implements it. Here we will only give an overview.

In [Bin00, Bad99, Bad00] we describe an architecture for authoring the behaviors of
interactive, animated agents using natural language instructions. In that architect
ure we
were able to give both immediate instructions and conditional instructions to the agents.
The
Actionary

is the core component of our system. It contains persistent, hierarchical
databases of agents, objects, and actions. The agents are treated as s
pecial objects and
stored within the same hierarchical structure as the objects. Actions are represented as
PARs (Parameterized Action Representation). Each PAR can either be uninstantiated
(UPAR), containing only default properties for the action or be
instantiated (IPAR),
containing specific information about the agent, objects, and other properties. All the
UPARs are stored hierarchically within the Actionary. PAR schemas are similar to
UPARs, except that their hierarchy is derived from natural langu
age verbs and semantics,
whereas the UPAR hierarchy is derived from motion semantics within the animation
domain.

During the initialization phase of a simulation, the Database Manager loads relevant parts
of the Actionary into the World Model. The model
is constantly updated during the
simulation, recording any changes in the environment or in the properties of the agents
and objects.

The user inputs natural language instructions for a specific agent through a GUI. The
NL

transducer

parses the instruction
s, translates them into situation calculus expressions
encapsulating references to PAR schemas, and sends them to the
Agent Process
.

Within the
Agent Process
, these expressions are stored as a list of
Desired Situations

along with other situation calculus
expressions generated from previous instructions. The
Desired Situations

set collectively represents the desired future behavior of the agent.
The
Rule Manager

uses the
World Model

to evaluate the expressions in the
Desired
Situations

set and sends them

to the
Goal Manager

when there are any changes. All
actions successfully completed by the agent are stored as a list of
Experienced Situations
.
The
Goal

Manager

uses the
Desired Situations

and the
Experienced Situations

to
determine new goals and sends
them to the
Planner
. The
Planner

further evaluates the
situation calculus expressions and retrieves the PAR schemas from them. For each PAR
schema, the
Planner

needs to retrieve the set of all relevant UPARs from the
World
Model
. The
Action Filter

firs
t eliminates some of the UPARs from the set based on the
agent's capabilities, and sorts the remaining UPARs based on the agent's characteristics. .
The ordering of actions is based on the agent's role, meta
-
motivational state, perception
of the situati
on, culture, personality, and emotions. The
Planner

solves an abstract
planning problem where the initial state comes from the
World Model
, the goal state from
the
Desired Situations
, a preference order of available actions from the
Action Filter
,
parame
ters to select plan structure from
Plan Strategy
, and constraints, also from
Desired
Situations
, are used to eliminate possible plans from consideration. These IPARs are
processed by the
Queue Manager

and
Process Manager

and then sent to the
Motion

Generat
ors

for the actual execution of the action. After successful completion of the
action, the
Process Manager

adds the relevant information about the action to the list of
Experienced Situations
. The multiple agents in the environment communicate with each
o
ther through message passing.

Conclusion and Recommendations

The PAR and its implementation architecture has been developed for reuse of action
definitions across various human performance scenarios and applications. This NASA
project has generated severa
l components of the architecture, including the processing
and animation of prepositional phrases describing movement [Xu00], fast inverse
kinematics for human arm positioning movements [Dee00], and zero
-
gravity locomotion
dynamics [Hua00]. As noted in
http://criticalpath.jsc.nasa.gov

“real
-
time workload
analysis and planning … [and] expert decision
-
making and VR systems” are critical
needs for long
-
duration NASA spaceflight missions. The PAR system is a maj
or step
toward real
-
time modeling, controlling, and experimenting with crew activities and
behaviors in microgravity environments. We recommend that NASA continue to develop
real
-
time multi
-
agent simulations so that VR training for future missions, whethe
r on
ground or in
-
flight, can be done with a high degree of face
-
to
-
face human and virtual
agent interaction.

References

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for the purposes of animation”. The Visual Computer,
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[Bad99] Badler, N., M. Palmer, R. Bindiganavale. “Animation control for real
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Gravity Activities”. International Space Human Factors Workshop,
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tions for
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AIAA and SAE International Conference on
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, 23(2): 367
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369, March
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April 2000.

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5, 2000, pp. 33
-
39.

Students Supported

Universit
y of Pennsylvania, Graduate Students:



Suejung Huh



Gang Huang



Janzen Lo, “Recursive Dynamics and Optimal Control Techniques for Human
Motion Planning,” PhD in Meachnical Engineering and Applied Mechanics,1998.



Harold Sun



Christian Vogler



Yilun D. Xu

Univers
ity of Pennsylvania, Res. Assoc. (PostDoc): Ambarish Goswami

MIT, Graduate Students:



Rahn, David, “A Dynamic Model of the Extravehicular Mobility Unit: Human
Performance Issues During EVA” Master's Thesis, June 1997.



Wu, Rex, “Human Readaptation to Normal
Gravity Following Short
-
Term
Simulated Martian Gravity Exposure and the Effectiveness of Countermeasures,”
Master's Thesis, September 1999.



Jackson, Dana Kessler, “Development of Full
-
Body Models for Human Jump
Landing Dynamics and Control,” Doctoral disse
rtation, June 1997.


Presentations and Publications



D. Chi, M. Costa, L. Zhao, and N. Badler. “The EMOTE model for Effort and
Shape.” Proc. ACM SIGGRAPH '00, New Orleans, LA, July 2000.



N. Badler, M. Costa, L. Zhao, and D. Chi.“To gesture or not to gestur
e: What is the
question?” Proc. Computer Graphics International, Geneva, Switzerland, June 2000.



N. Badler, M. Palmer, R. Bindiganavale. “Animation control for real
-
time virtual
humans.” Communications of the ACM 42(8), August 1999, pp. 64
-
73.



N. Badler, D
. Chi, and S. Chopra, “Virtual human animation based on movement
observation and cognitive behavior models.” Proc. Computer Animation Conf.,
Geneva, Switzerland, May 1999, pp. 128
-
137.



G. Huang, S. Huh, A. Goswami, D. Metaxas and N. Badler. “Dynamic Simula
tion for
Zero
-
Gravity Activities”. International Space Human Factors Workshop, Tokyo,
Japan, June 1999.



N. Badler, R. Bindiganavale, J. Allbeck, W. Schuler, L. Zhao, S.
-
J. Lee, H. Shin, and
M. Palmer. “Parameterized action representation and natural langua
ge instructions for
dynamic behavior modification of embodied agents.” AAAI Spring Symposium
2000.



J. Shi, N. Badler, and M. Greenwald. “Joining a real
-
time simulation: Parallel finite
-
state machines and hierarchical action level methods for mitigating lag

time,” Proc.
9
th

Conference on Computer Generated Forces, Orlando, FL, May, 2000.



R. Bindiganavale, W. Schuler, J. Allbeck, N. Badler, A. Joshi, and M. Palmer
“Dynamically altering agent behaviors using natural language instructions.”
Autonomous Agents 20
00, Barcelona, Spain, June, 2000.



N. Badler. “Key problems for creating real
-
time embodied autonomous agents.”
Fourth International Workshop on Autonomous Agents: Achieving Human
-
Like
Behavior in Interactive Animated Agents, Barcelona, Spain, June 2000.



J.

Allbeck, R. Bindiganavale, K. Kipper, M. Moore, W. Schuler, N. Badler, A. Joshi,
and M. Palmer. “Authoring embodied agents' behaviors through natural language and
planning.” Fourth International Workshop on Autonomous Agents: Achieving
Human
-
Like Behavior

in Interactive Animated Agents, Barcelona, Spain, June, 2000.



N. Badler, L. Zhao, M. Costa, C. Vogler, and W. Schuler. “Modifying movement
manner using adverbs.” Fourth International Workshop on Autonomous Agents:
Communicative Agents in Intelligent Virtu
al Environments, Barcelona, Spain, June,
2000.



L. Zhao, M. Costa, and N. Badler. “Interpreting movement manner.” Proc. Computer
Animation 2000 Conference, IEEE Computer Graphics Society, Philadelphia, May 3
-
5, 2000, pp. 112
-
120.



Y. Xu and N. Badler. “Algor
ithms for generating motion trajectories described by
prepositions.” Proc. Computer Animation 2000 Conference, IEEE Computer
Graphics Society, Philadelphia, May 3
-
5, 2000, pp. 33
-
39.



N. Badler, R. Bindiganavale, J. Allbeck, W. Schuler, L. Zhao, M. Palmer
“Parameterized action aepresentation for virtual human agents.” In J. Cassell, J.
Sullivan, S. Prevost, and E. Churchill (eds.),
Embodied Conversational Agents
, MIT
Press, 2000, pp. 256
-
284.



J. Allbeck and N. Badler. “Autonomous agents.” To appear in
The
Handbook of
Virtual Reality
, Kay Stanney, Editor.



H. Sun and D. Metaxas. “Animation of human locomotion using sagittal elevation
angles.” Proc. 8
th

Pacific Conference on Computer Graphics and Applications, Hong
Kong, October 3
-
5, 2000.



G. Huang, D. Metaxas

and J. Lo. “Human motion planning based on recursive
dynamics and optimal control techniques.” Proc. Computer Graphics International
Conference, Geneva, June 2000.



D. Metaxas, D. Newman and N. Badler. “Noninvasive video motion capture.” Proc.
13
th

IAA Hu
mans in Space Symposium, Santorini, Greece, May 20
-
26, 2000.



J. Lo and D. Metaxas. “Simulating human task motion using recursive dynamics and
optimal control techniques.” Proc. 11
th

International Conference on Mechanics in
Medicine and Biology, Maui, Hawai
i, April, 2000.



J. Lo and D. Metaxas. “Efficient human motion planning using recursive dynamics
and optimal control techniques.” Proc. 2
nd

Symposium on Multibody Dynamics and
Vibration of the 17
th

Biennial Conference on Mechanical Vibration and Noise, Las
Vegas, NV, Sept 12
-
15, 1999.



D. K. Jackson and D. J. Newman, “Adaptive effects of space flight as revealed by
short
-
term partial weight suspension.” Aviation Space and Environmental Medicine
71(9), September 2000.



R. Wu and D. J. Newman, “Astronaut adaptat
ion across the spectrum of gravity.”
Proc. of the 13
th

Humans in Space Symposium, Santorini, Greece, May 2000.



G. Schaffner, D. J. Newman and S. Robinson, “Computational simulation of
extravehicular activity dynamics during a satellite capture attempt.”
AI
AA Journal of
Guidance, Control, and Dynamics
23(2): 367
-
369, March
-
April 2000.



D. J. Newman, P. Schmidt, D. B. Rahn, D. Metaxas and N. Badler. “Modeling the
Extravehicular Mobility Unit (EMU) space suit: Physiological implications for
Extravehicular Activ
ity (EVA).” AIAA and SAE International Conference on
Environmental Systems, Toulouse, France, July 2000.




Other N. Badler presentations (without papers)



Virtual Human Summit, Aspen, CO, November 1998.



Digital Media Futures, Bradford, UK. Meeting sponsored
by the British Computer
Society, April 1999.



Colloquium, University of Edinburgh, Scotland, April, 1999.



Colloquium, Dartmouth College, Hanover, NH, April, 1999.



Jack User Conference, Detroit, MI, May 12, 1999.



International Space Human Factors Meeting, To
kyo, Japan, July 8
-
10, 1999.



ACM SIGGRAPH Course: Smart(er) Animated Agents, Los Angeles, CA,
August, 1999.



Army TACOM Industry Logistic Data Symposium, April 2000.



Colloquium, University Jaume
-
I, Castellon, Spain, June 1, 2000.



Human Modeling and Animatio
n Workshop, Seoul, South Korea, June 2000.



Tutorial Speaker, Computer Animation 2000, Philadelphia, PA, May 2000.



Digital Human Modeling Conference, Society of Automotive Engineers, Warren,
MI, June 2000.



Computer Graphics International, Geneva, Switzerlan
d, June, 2000.



ACM SIGGRAPH Course: Smart Animated Agents, New Orleans, LA, July,
2000



Chinagraph 2000, Hangzhou, P.R.China, September 2000.




Other D. Metaxas presentations (without papers)




NSF/ARO Workshop on Algorithmic Issues in Modeling Motion, Duke U
niv.,
August 6, 2000.




Computer Graphics International 2000 Conference, Geneva,Switzerland, June
19
-
23, 2000. (Invited Talk on modeling Humans using Physics based modeling
methods)



Thayer School of Engineering, Dartmouth College, Hanover, NH, May 2, 2000



D
epartment of Mathematics and Computing Science, Eindhoven University of
Technology, Eindhoven, The Netherlands, April 27, 2000.


Appendices

1. Dynamic Simulation for Zero
-
Gravity Activities

2. Modeling the Extravehicular Mobility Unit (EMU) Space Suit: Phy
siological
Implications for Extravehicular Activity (EVA)

3. Computational Simulation of Extravehicular Activity Dynamics during a Satellite
Capture Attempt

4. Animation Control for Real
-
Time Virtual Humans