TECHNOLOGY DIGEST - Draper Laboratory

Autonomous Mission Management for Spacecraft Rendezvous Using an Agent Hierarchy
and any applicable constraints. Subplanner 1 returns the
cost of the optimal trajectory from A to B, call it J1.
Similarly, the supervisor planner may request subplanner
2 to solve the B to C problem and receives the B to C
cost, J2. The total cost, J1 + J2, is plotted topographically
on the right as a function of the interface states
(coordinates of B). The supervisor optimizes the A to C
trajectory by searching for the optimal coordinates of B.
Depending on the attributes of the problem, the supervisor
may find the optimal solution by using one of any
number of optimization techniques, gradient descent for
example.
Figure 2. Optimization or decision-making via hierarchical
planners.
On the other hand, the supervisor may not be required to
find the optimal solution at all. It may instead search
through a predefined set of interface states (red Xs in the
figure) choosing the option with the lowest cost. When
the supervisor evaluates a set of options, we shall call the
process decision-making. When an iterative search is
used, the process may be an optimization process. The
multilevel optimization technique described here is
referred to as interaction coordination. Other techniques,
such as price coordination are also available. [2]
Of course, this iterative process is not new to those familiar
with optimization. Many optimization algorithms
work by iterating on a set of control variables to minimize
a cost function. But diagramming the problem this
way highlights some of the advantages of thinking about
planning in a hierarchical sense. Each subplanner may be
an expert at its piece of the problem. Subplanner 2 need
not have any knowledge of subplanner 1’s problem and
vice versa. Further, the supervisor need not have all the
details of the subproblems to solve the overall problem.
Second, once the vehicle has passed point B, there is no
longer any need to consider the A to B problem, and subplanner
1 (and all its associated data and memory) may
be deactivated. Finally, note that the time horizon for
each planner gets longer as we move up the hierarchy
with the supervisor having the lowest level of detail while
planning over the longest time horizon.
It is easy to imagine an extension of the two-level hierarchy
described above in which each subplanner in turn
solves its problem by invoking subplanners of its own.
44
A
Subplanner
1
Cost = J1
Supervisor
Planner
B = interface
state
B
Subplanner
2
Cost = J2
Total Cost, J = J1 + J2
(out of page)
B2
J=50 J=40
B1
C
Discrete decision points
J=30
J=50
Optimum
This sort of hierarchical planning breakdown has one
other very important advantage. The same process that
creates the planning tree results in a natural breakdown
for all the functions required of a mission manager. In
fact, the process of invoking subplanners and sub-subplanners
can be used to create a hierarchy of agents, each
responsible for monitoring, diagnosing, planning, and
executing a section of the mission. We shall refer to these
agents as activity agents. Activity agents that are connected
to a superior agent in the hierarchy are referred to as
children of the superior, while the superior agent is the
parent of each child. Agents without children (bottom
level nodes) are referred to as leaf agents, or leaf nodes.
Figure 3 shows an example for a spacecraft rendezvous
and docking mission. Each of the activity agents has four
sub-boxes, corresponding to monitoring, diagnosis,
planning, and execution. Just as for planning, the time
horizons for each activity increases with its level in the
hierarchy, while the detail of its functions decreases.
This process of breaking down a complex problem or
mission into activities or phases is referred to as temporal
decomposition. The process of defining the functionality of
each activity agent is referred to here as functional decomposition.
In this context, the functional decomposition
consists of defining the monitoring, diagnosis, planning,
and execution functions required by each
activity.
Rendez
D P
M E
Burn
D P
M E
D P
M E
Survey
D P
M E
Acquire Insert
D P
M E
Mission
D P
M E
Maneuver
D P
M E
Approach
D P
M E
Maintain Init Final
D P D P D P
M E M E M E
Figure 3. Example hierarchy of activity agents.
The interfaces between parent and child activities in
Figure 3 consist of problem specifications from parent to
child and solution information from child to parent. For
vehicle applications, the problem specification usually
consists of a start state for the child activity, a goal state,
and a set of constraints. Sometimes, the goal state is only
partially specified or constrained, so the child must
return the final state in the solution information.
Solution information may also contain sensitivity data,
resource data, and cost data, etc. At the lowest level, the
interfaces are commands to the GN&C and subsystems.
These commands may be thought of as problem specifications
to the vehicle.
The implementation of these hierarchical mission management
architectures requires a software framework