A robot control package is described which utilizes on-line structural simulation of robot manipulators and objects in their workspace. The model-based controller is interfaced with a high level agent-independent planner, which is responsible for the task-level planning of the robot's actions. Commands received from the agent-independent planner are refined and executed in the simulated workspace, and upon successful completion, they are transferred to the real manipulators

Biegl, Csaba A.

Cook, George E.

Fernandez, Kenneth R.

Springfield, James F.

English

NASA Technical Reports Server

N90-27298
Simulation-Based Intelligent Robotic Agent for Space Station Freedom
Csaba A. Biegl, James F. Springfield, George E. Cook, *Kenneth R. Fernandez
Vanderbilt University, Dept. of Electrical Engineering,
Box 1824, Sta. B, Nashville, "IN 37235
*NASA Marshall Space Flight Center, Huntsville, AL
Abstract
This paper describes a robot control package which utilizes on-line structural simula-
tion of robot manipulators and objects in their workspace. The model-based controller is
interfaced with a high-level agent-independent planner, which is responsible for the task-
level planning of the robot's actions. Commands received from the agent-independent
planner are refined and executed in the simulated workspace, and upon successful comple-
tion, they are transferred to the real manipulators.
1. Introduction
It is expected that robotic systems will play a crucial rule in the operation of Space
Station Freedom. Various repetitive tasks associated with the maintenance of the Station
are the most likely candidates for robotics and automation applications, since such applica-
tions would free up a considerable amount of crew-time for more useful activities, like
scientific experiments, etc.. Controlling these robots will be a very complex task due to
unusual operating conditions, like tight space, strong interdependence of various subsys-
tems onboard the station, and the unavoidable presence of humans near the robot's
working area. These requirements will necessitate the development of highly specialized
robot control systems for space applications. The goal of the research described in this
paper is to design and implement an autonomous robotic agent which can serve as a
component of such control systems.
[i'High-level_ task planner [
coordinatorIpath planning, grasping ...
ffV
I Hardware-level jointcontrollers
Figure 1. Multilayer Robot Control System Architecture
It is customary to distinguish between three layers in robot control systems, as seen in
Figure 1. _,The bottom layer implements the low-level joint control systems. At this level the
controllers inputs are individual joint position and speed signals, and their output deter-
mines the voltage or hydraulic pressure applied to the joint actuators of the manipulator.
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The middle layer of a hierarchical robot control systemcontains the moduleswhich
are necessaryfor the coordinated motion of the individual manipulator joints. Operations
typically performed at this level include path synthesis,object grasping, and compliant
motion.
The componentsof the highestlayer implement the taskplanning functionalities in the
system.Task planning means breaking down a complex goal into a sequenceof simple
actionssuitable for input at the path synthesislevel.
An agent-independenthigh-level task planner hasbeen describedin [3]. The goal of
the work describedin this paperwas to provide a target environment for this task planner,
which is capableof executingthe planner's action sequencesusingeither a graphicssimula-
tion environment or real robot hardware.The operation of this robotic agent is basedon
the structural, geometrical modeling of the robot manipulators and the objects in their
workspace.The reasonfor this is that the techniquesusedto model solid objectsprovide
the most natural way to describe the robots and the workspaceobjects.Solid modeling
techniquesmight be usedin different areasof the designandoperation of robotics systems,
like:
- Design and testing of manipulators: In such applications the purpose of the modeling is
to study different approaches to fulfill the design specifications of the manipulator.
- Robot action planning: The modeling environment is used to build a representation of
the robot and the objects in the workspace, and to create and validate action plans by
simulating various actions in the model space.
- On-line control of robot manipulators: The modeling tools are integrated into a hierar-
chical robot control system, and the action plans generated and tested in the model
space will be transmitted to the robot manipulators for execution.
2. Related Work -- A Robot Modeling Environment
The Intelligent Robotic Agent is based on a robot simulation and control environment
described in [2]. The main requirements against a geometrical modeling toolkit used in
robotics applications can be summarized as follows [1][4][7]:
- A set of solid primitives (like boxes, cylinders, cones, etc...) which can be used to con-
struct the manipulator and world models.
- A way to build structured objects from the primitives or other previously defined struc-
tured types.
-A set of methods to manipulate the models. These include methods for accessing and
animating objects, collision detection algorithms, and routines for forward and inverse
kinematics calculations, graphics display, and possibly dynamics calculation.
These goals were satisfied by implementing a robot simulation library. The basis for
this development was the ROBOSIM [5] robot simulation package. ROBOSIM in its
original form is a command line oriented graphical robot modeling package. It provides
commands to build the geometrical representation of robots and various objects and to
perform simple operations on these.
To enhance the object manipulation capabilities of the original ROBOSIM package, a
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simulation library hasbeen built which is capableof operating on the internal geometrical
model databasesbuilt up by ROBOSIM's commandinterpreter. This library includes the
following components:
The animation module provides the methods to "operate" the manipulators in the
model space. Its methods include routines for driving the arms' joints, straight line
motion, grasping, etc...
The inverse kinematics module aids the animation by providing the inverse kinematics
solutions for the robot arm(s) in the model space. By default it uses a numerical
method, but if the analytical inverse equations are available, the default method can be
replaced by these.
- The collision detection module is used by the animation module to check the validity of
the steps during a movement. If the step would result in a collision, a report is sent to
the animation module, describing the objects involved in the collision.
The robot interface driver module is used to duplicate the actions taken in the simula-
tion environment in the real robot's workspace. It generates calls to a low-level inter-
face library based on the simulated joint movements. Error reporting is also possible in
case the command fails in the real workspace.
- The graphics display module is used to display the workspace configuration and to
follow the actions in the simulated environment.
The structure of the modeling environment composed of the above modules and the
original ROBOSIM interpreter can be seen in Figure 2.
Model
Editor
Figure 2. Components of the Robot Modeling Environment
3. The Intelligent Robotic Agent
The purpose of the Intelligent Robotic Agent is to provide a user-friendly way to use
the tools of the Robot Modeling Environment described above. The agent was designed to
be able to receive robot action sequences either from a high-level symbolic task planner or
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directly from the user during an interactive session. The main requirements against the
agent are summarized below:
- Symbolic Planner User Interface Protocol." Regardless of the operating mode of the
agent (used by a task planner or by an interactive user), the same command interface
is being used. This command language provides symbolic manipulator and object
identifiers for a more user-friendly programming environment. The interface protocol
also includes extensive error reporting mechanisms.
To planner To robots
IInteractivePlannerInterface Low-level Manipulator
C**ns_r.U°n Interface
I_ronm_t
Coutrol
p.tb_.,ith,_w_.l_ ] Robot Modeling Environment
Agent Shell
Intelligent Agent
Figure 3. Intelligent Agent System Architecture
- Environment Configuration Interface: In order to ensure the consistency of the world
models with the task olanner, the agent includes a set of commands forming a con-
figuration interface wmcn is used by the planner (or by the user in an interactive
session) to build the geometrical representation of the workspace. The agent features
its own modeling language for creating elementary objects, building complex objects
from these, and to perform various transformations. In view of the considerable effort
already invested in creating models of various objects and robots in the ROBOSIM
language, the possibility of using existing ROBOSIM models is also provided.
-Agent Operation Interface: A set of commands is provided to operate the robot
manipulator models in the agent's data base. These include commands for various
types of motion (joint interpolated, straight line, etc..), path planning with collision
avoidance, and for grasping and releasing objects.
- Graphics Display: Probably one of the most useful features of any robot simulation
package is the possibility to view the manipulators in their workspace as they perform
various actions, in a safe, simulated environment. The agent includes a graphics display
feature which is usable during both the environment configuration and the agent
operation phases.
- Interface to Robot Controllers: The agent can operate in either of two modes. In the
first mode the actions received from the command interface are performed only in the
geometrical modeling environment. In this case the execution can be viewed on a
graphics display, and the agent will report any error conditions encountered during the
execution (collisions, joint violations, etc..). In the second mode the successful execu-
206
tion of the above steps is followed by sending commands to a real robot hardware
interfaced to the agent to duplicate the actions taken in the simulated environment in
the real workspace too.
The above goals were satisfied by incorporating the services of the modeling environ-
ment into an interactive shell, and by creating a low-level interface package between trle
simulation library's robot interface driver module and the controller of the manipulator
used in the system. The architecture of the Intelligent Agent can be seen in Figure 3.
The agent shell's task is to interpret the commands sent by the planner. It has a built-in
symbol table for storing the various objects in the workspace and the attributes associated
with these. After parsing the commands, the shell will invoke the appropriate functions of
the robot simulatmn library with an argument structure built usin_ tlae lntormation storeo
in the symbol table. Any results or error codes returned by the library call are translated
into the corresponding message format and sent back to the planner, or printed to the user
during an interactive session.
One important design consideration associated with the agent was the minimization of
the numerical calculations required from the task planner. To accomplish this goal various
attributes are associated with each object. These attributes include the object's current
position, predefined grasping points, and predefined joint parameter vectors for the robot
manipulator objects. These attributes are linked to the object, i.e. any transformation will
update their values. This means, that for example the task planner does not have to com-
pute numerical coordinates to move a robot manipulator to an object's grasping point, but
_t can use the symbolic grasping point attribute whose value is always linked to the object's
current position.
The agent shell also includes a path planning algorithm, which is used to synthesize a
collision-free path between two locations in the workspace. This algorithm relies on the
collision detection feature of the simulation library and utilizes a simple search strategy to
find an alternate route in case a collision is detected during a motion segment. First the
endpoint of the failed motion segment is checked to see if there is any collision when the
manipulator reaches that point. If this is the case then the search fails, since the motion
segment can not be completed regardless of the route taken. Otherwise the search algo-
rithm will subdivide the failed motion segment by inserting an intermediate point selected
using a set of heuristic rules, and recursively repeat the above process. There may be
situations when this method will fail to find a path even if such a path exists, but the algo-
rithm described above has been observed to find a solution in the majority of the situations
examined so far.
The agent shell also supports the parallel operation of robot manipulators. An ex-
tended command line format makes it possible to enter commands for every manipulator
in the system at the same time, and these commands will execute parallelly.
4. An Application Example
The Intelligent Agent has been used as an interface between a robotic task planner
and a PUMA 560 manipulator. The architecture of the whole system can be seen in Figure
4. The low-level robot interface between the agent shell and the PUMA manipulator's
controller has been implemented in a distributed fashion, partly on the workstation running
the agent shell, and partly on an IBM PC AT. The reason for this solution was that parts of
this interface package have to meet certain real-time requirements.
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The confi_uration describedabove hasbeen used to executetask plans for repairing
electronic eqmpment. The operations included setting various switches and dials on the
front panels of the racks housing the equipment, and replacing faulty circuit boards. A
graphicsdisplayof the simulatedworkspaceof this application canbeseenin Figure 5.
Explorer Graphics IBM PC AT PUMA 560
AI Workstation and Controller
Workstation (SGI}
/o/ \
\ / k.___/ \ / \_1
Task Agent Low-level Robot Manipulator
Planner Shell Interface
Figure 4. System Configuration for Intelligent Agent Application Example
208
Figure 5. Workspace Configuration for Intelligent Agent Application
5. Summary and Future Research
An intelligent agent has been developed for the execution of robotic task plans gener-
ated by a high-level planner. The agent uses a well defined protocol for receiwng environ-
ment configuration and manipulator commands from the planner and to send back status
reports. After translation, the agent uses the services of a robot simulation and control
environment to carry out the high-level commands of the planner. This configuration has
been used to examine the possibilities of using robots for repairing electronic equipment.
The advantage of this approach is its flexibility. It is easy to adapt the system to dif-
ferent workspace configurations or to different robot manipulators by changing the environ-
ment configuration commands sent to the agent. The well-defined planner-agent interface
simplifies the development and verification of new task planners, because the task plans
can be tested for validity by the agent, and the user can also see the effects of the planner's
output on the graphics display.
The most important area of planned future research is the upgrading of the low-level
robot manipulator interface. The current interface uses the PUMA manipulator's com-
mand language (VAL II [8]) to execute the robot movement commands, but a new version
is currently being developed which will bypass this command language and use the PUMA
controller's "real-time path control" feature to achieve an even better quality of joint
control. This new version will be built using the so called Multigraph Architecture [6],
which provides a dynamically configurable distributed macro-dataflow computational
environment. This will allow the selection of the joint control scheme and its parameters
from the agent shell, resulting in a highly optimized controller performance for every
application.
6. Acknowledgements
William S. Davis and Ray Carnes (Boeing Aerospace Company, Huntsville, AL), and
Professor Janos Sztipanovits (Vanderbilt University, Nashville, TN) provided valuable
technical information and criticism during the development of this work. Some of the
equipment necessary for the work was provided by the Hewlett Packard Company and the
Boeing Aerospace Company.
7. REFERENCES
1. B. Bhanu, C. C. Ho: "CAD-Based 3D Object Representation for Robot Vision",
Computer, Vol. 20, No. 8, Aug. 1987, pp. 19-35
. C. A. Biegl, J. Springfield, et. al.: "Adaptive Control of a Dual-Arm Robot Manipulator
Using On-Line Graphical Simulation", in Proceedings of the ISA Forth Annual
Workshop on Robotics and Expert Systems, (August 1989, Palo Alto, CA) vol. 4, pp. 253-
259.
3. W.S. Davis: "Robotic Task Planning: Independent of Agents but Dependent on Time",
in Proceedings of the 1989 IEEE Conference on Robotics and Automation, (May 1989,
Scottsdale, AZ) vol. 2, pp. 690-695.
° E. Dombre, P. Borrel, A. Liegeois: "A CAD System for Programming and Simulating
Robots' Actions", in Computing Techniques for Robots, ed I. Aleksander, Chapman and
Hall, New York, 1985, pp. 222-247.
209
oo
.
K. R. Fernandez: "Robotic Simulation and a Method for Jacobian Control of a Redun-
dant Mechanism with Imbedded Constraints", Ph.D. thesis, Vanderbilt University,
Spring 1988
G. Karsai, et. al.: "Knowledge-Based Approach to Real-Time Supervisory Control",
Proc. of the 1988 American Control Conference, Atlanta, GA, pp. 620-626, 1988.
C. Mirolo, E. Pagello: "A Solid Modeling System for Robot Action Planning", IEEE
Computer Graphics & Applications, January 1989, pp. 55-69
8. Unimation Inc.: "User's Guide to VAL Ir', Danbury, CT 1986
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