A satellite floating space is diagnosed with a telerobot attached performing maintenance or replacement tasks. This research included three objectives. The first objective was to generate intelligent path planning for a robot to move around a satellite. The second objective was to diagnose possible faulty scenarios in the satellite. The third objective included two tasks. The first task was to combine intelligent path planning with diagnosis. The second task was to build an interface between the combined intelligent system with Robosim. The ability of a robot to deal with unexpected scenarios is particularly important in space since the situation could be different from time to time so that the telerobot must be capable of detecting that the situation has changed and the necessity may exist to alter its behavior based on the new situation. The feature of allowing human-in-the-loop is also very important in space. In some extreme cases, the situation is beyond the capability of a robot so our research project allows the human to override the decision of a robot

Chan, Amy W.

English

NASA Technical Reports Server

N93-11929
INTEGRATION OF TASK LEVEL PLANNING
AND
DIAGNOSIS FOR AN INTELLIGENT ROBOT
Amy W. Chan
UFA, Inc.
335 Boylston street,
Newton, MA 02159
Work performed under Contract# NSA8-38420
Abstract
This paper is to diagnose a satellite floating through space with a
telerobot attached performing maintenance or replacement tasks.
This research included three objectives. The first objective was to
generate intelligent path planning for a robot to move around a
satellite. The second objective was to diagnose possible faulty
scenarios in the satellite. The third objective included two tasks.
The first task was to combine intelligent path planning with
diagnosis. The second task was to build an interface between the
combined intelligent system with Robosim.
The ability of a robot to deal with unexpected scenarios is
particularly important in space since the situation could be different
from time to time so that the telerobot must be capable of detecting
that the situation has changed and the necessity may exist to alter its
behavior based on the new situation.
The feature of allowing human-in-the-loop is also very important in
space. In some extreme cases, the situation is beyond the capability
of a robot so our research project allows the human to override the
decision of a robot.
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This research project is the Phase II SBIR project, "Integration of
Task Level Planning and Diagnosis for an Intelligent Robot". This
research project is an intelligent system developed for multiple
processes running in real time. The intelligent system includes three
parts: AI Path Planner, Diagnosis and the interface with Robosim *1.
Refer to Figure 1-1 for the relationship.
52
https://ntrs.nasa.gov/search.jsp?R=19930002741 2020-03-17T09:50:00+00:00Z
J setup.cmd set up Robosim Window
I DIAGNOSISl_fI°w chart_
I pass commands
knowledge !base update
..I _ updatehelp from the ground
luser inpuzl
I P
ROeOSIM- 1
Figure 1-1
In addition to Robosim window, there are two windows in the
system: the Satellite Window and the Knowledge Window. A
simplified generic satellite model floating through space with a
telerobot attached is simulated in the Satellite Window.
There are two knowledge bases in the system: the Satellite
Knowledge Base and the Diagnosis Knowledge Base. Both are
constantly updated during the simulation and displayed in the
Knowledge Window. The Knowledge Window also displays simplified
physical architecture and logical diagnosis of satellite subsystems.
The intelligent system provides user friendly environment. It is
written in C and runs on a Silicon Graphics Iris workstation with Unix
operating system.
53
AI path Planner
The AI Path Planner includes three parts: a scenario definition file -
"model.dar' and scenario parser; model builder; and Path Planner.
Refer to Figure 1-2 for the relationship.
scenario definition I
_L
_'Scenario Parser }
LT ,o,o,,,,.
Model Builder ]
Figure 1-2
Scenario Definition File and Scenario Parser
A scenario definition file called "model.dar' was created to define the
geometric configuration of a simplified generic satellite model. The
scenario file defines the brief title of each satellite module and
assigns each subsystem, panel, antenna and the robot to a separate
module. The scenario file also assigns a list of parts to some modules
and defines all the possible moves of each module.
54
There are two parsers in the system: the Scenario Parser and Setup
Parser. The Scenario Parser was created to read and store the
scenario definition file to the Satellite Knowledge Base. The Setup
Parser was created to read a setup file for Robosim.
The Scenario Parser also calculates the center of each module,
calculates the distances of each module to all its possible moves, sets
up the constraint condition and Stores them into the Satellite
Knowledge Base.
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The model of a simplified generic satellite was constructed in the
form of three cylinder-like structures piled up together. Each
cylinder-like structure has six sides. The total modules of the
satellite model is the eighteen sides plus the top and bottom of the
model. The Model Builder displays the model with a pair of solar
panels, an antenna and the robot to the Satellite Window.
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The Path Planner determines the best path for the robot to move
from the module which the robot is standing at to any other target
modules. Once a move command is given to the robot, the Path
Planner will search dynamically all the adjacent paths one by one
and generate the best candidate path set. Each path set includes one
or more module elements. The determination of each candidate path
set is a complicated task that involves calculating the total distance
of the path set, avoiding obstruction, paying extra costs and
penalties.
The search algorithm is First Depth plus heuristic and optimal. To be
more specific, in order to get the shortest move, when a target
module is given, the Path Planner calculates and compares all the
distances from the current module to all the adjacent modules one at
the time and get the shortest one.
Once the shortest move is found, the Path Planner pushes the target
module and its total distance into a result stack. At e_ich dead end,
the Path Planner pops the last node off the stack and tries a new
path from that point (backtracking). 55
After the candidate path is determined, the Path Planner pops up the
stack element by the rule of "first-in-last-out" and displays them
into the Satellite Knowledge Base Display in the Knowledge Window.
To simulate the intelligence of the system, the following features had
been added to the Path Planner.
, If the robot moves from or to the top or bottom module, a certain
"cost" will be charged. For example, if the robot is located at the
top module - 'S' and going to move to any adjacent modules, or
the robot is standing at any adjacent modules and moving to the
top module - 'S', the Path Planner adds an extra cost to the total
distance.
• There are a list of parts assigned to some modules. After a move
path set is generated, the robot moves one by one according to
the path set. While the robot moves to each module element, the
Path Planner checks whether a part is assigned to the current
module. If there is a part assigned to the current module, the
Path Planner will check whether the robot is carrying that part.
If the robot is carrying that part, it will skip that part. If the
robot is not carrying that part, it will pick up that part.
• If the robot passes some modules with constraints in the center
such as panels or antenna, it will be fined with a certain penalty.
For example, if the robot passes a module with the panel in the
center, the Path Planner will add an extra 15 second penalty to
the total penalty. If the robot passes a module with the antenna
in the center, the Path Planner will add an extra 20 second
penalty to the total penalty.
Unexpected Events
To simulate an unexpected event, an obstruction storm was created.
The obstruction storm includes three obstructions. Each obstruction
has different sizes. The three obstruction were flying in three
different directions in three different speeds in the simulation. The
system can handle the following unexpected events:
56
• An obstruction can be selected to fall on the surface of the
model. If the obstruction is falling on the module at which the robot
is standing, the robot will detect it and move away to avoid being hit.
• If a given target for the robot to move has an obstruction, the
system will detect it , discard the move command and pop up an
error message to the Satellite Knowledge Base in the Knowledge
Window.
• The Path Planner will detect the module with an obstruction
and skip it from the path planning.
Human-in-the-loon
The system allows the human being to take over the control of the
robot during the simulation. The System allows the human being to
input his path set and also allows the human being to review or
change the path set. After the path set is determined, the robot
moves one by one according to the path set. The following error
checking were performed:
• If one module element of the human input path set is not
alphabetic, the system discards it from the path. The new path set
remains valid.
, If one module element of the human input path set cannot be
found in the Satellite Knowledge Base, the system discards the whole
path set and displays an error message to the Satellite Knowledge
Base Display in the Knowledge Window.
• If one module element of the human input path set has an
obstruction, the system discards the whole path set and displays an
error message to the Satellite Knowledge Base Display in the
Knowledge Window.
57
Dia__nosis + Path Planner + Robosim
We have combined the AI Path Planner with the Diagnosis and built
the interface between the new intelligent system with the Robosim.
We pass the Robosim commands through the Unix feature - pipe to
the Robosim.
To setup the Robosim, users have to define the color and view point
in a file called "setup.cmd". Users can also create, translate or rotate
objects in this file. The Robosim initializes the robosim screen
according this file.
The Diagnosis includes space experiment lab and four subsystem
diagnosis. There is an option in the main menu for each subsystem.
Once a particular subsystem is selected, the Diagnosis checks the
Satellite Knowledge Base and finds out which module is assigned.
Once the module is found, the Diagnosis calls the Path Planner to
generate the best path and the robot will move one by one according
to the path set. The Satellite Knowledge Base is updated
simultaneously.
At the same time, a simplified physical architecture chart of that
subsystem is displayed on the left side of the Knowledge Base
Window and the complete diagnosis flow is displayed on the right
side of the window.
The Diagnosis passes the predefined command files to Robosim to set
up the Robosim window simultaneously. Each part of the physical
architecture is drawn as a rectangle. As the Robosim does not allow
displaying words, the labelling of the Robosim is displayed on the left
Knowledge Window.
To cover all the failure scenarios, a random number is generated to
give a positive or negative answer at each determination point
during the diagnosis of each subsystem. After a complete failure
scenario is diagnosed, the system displays it on the right Knowledge
Window.
Once a faulty part is detected during the diagnosis, the Diagnosis
sends a command file to Robosim to command the robot in the
Robosim Window to identify the faulty part. If there are more than
one faulty parts found, the robot will identify them one by one. In
58
some cases, the Diagnosis is not able to find the faulty part, the robot
asks for help by displaying a big "HELP" in the Robosim Window.
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This scientific AI project includes multiple processes and each
process is displayed in a separate window. This artificial program
can integrate with a simulator like Robosim.
The breadth-first search plus heuristic and backtracking strategy
enables this project to provide a shortest path set between any two
modules in the satellite.
This project handles unexpected events and allows human to
override the decision of an intelligent robot during the simulation.
Acknowledgments
I appreciate all of the help that made this project possible and a
success. Many thanks go to Ms. Cynthia A. Coker, the C.O.R of this
research project. I would like to thank Kiet Huang, our previous
project manager, for his accomplishments during Phase I and his
many inputs and ideas at the beginning of Phase II.
I also appreciate Csaba A. Biegl Ph.D. from Vanderbilt University for
his valuable explanation of the ROBOSIM and the integration of our
system with Robosim. Finally, I would like to thank Dr. Harold L.
Alexander from M.I.T. for his valuable consultation of the satellite
subsystem diagnosis.
Reference
1. The initial Robosim was created by Ken Fernandez, Ph.D. of
MSFC, NASA. The Robosim which this research project integrated
with is an intelligent agent of the Robosim developed by Csaba A.
Biegl, Ph. D. from Vanderbiit University.
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Integration of task level planning and diagnosis for an intelligent robot

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