ICAPS05 WS6 - icaps 2005


ICAPS05 WS6 - icaps 2005

But, in reality, both problems are not independent for

mainly two reasons:

1. the global objective of the mission is to track important

ground areas as regularly as possible and to deliver data as

soon as possible after observation: there is one objective,

not two;

2. observation and data down-loading interfere through energy

and memory: observation consumes energy and

memory, and data down-loading consumes energy and

produces memory.

Between both decision modules, consistency in terms of

mission objective is obtained via the maintenance for each

request of the same level of priority from ground tracking

sharing to observation and data down-loading.

About energy and memory, it can be observed that the

situation is not symmetric: observation consumes energy

and memory, but data down-loading consumes energy and

produces memory. If data down-loading is insufficient, onboard

memory gets quickly full, either observation is no

more possible, or previously recorded data is removed. In

the opposite direction, if observation is insufficient, data

down-loading remains possible. Data down-loading is thus

a bottleneck for the whole system. This is why we decided

to give it priority for the access to energy.

In such conditions, data down-loading can be decided

independently from observation, taking into account only

currently recorded data. As to observation, it must forecast

the amount of energy that will be consumed and also

the amount of memory that will be released by data downloading

during the next visibility windows, and guarantee

that data down-loading will have always enough energy to

do its job. This can be done at any time via the greedy algorithm

presented above.


Concerning observation decisions, first experimental results

have been presented in (Damiani, Verfaillie, & Charmeau

2004). But, we carried out since then more ambitious experiments,

involving the whole constellation (12 satellites),

a control center, and two mission centers (one dedicated to

forest fires and the other one to volcanic eruptions), over a

temporal horizon of 16 hours.

We assume (1) about one hundred ground areas to track

that are known at the beginning of the simulation horizon

and (2) about ten that appear during the simulation horizon

(in both cases, fifty-fifty shared between forest fires and volcanic

eruptions). We assume that the tracking of the first

ones is shared by the control center between the satellites of

the constellation and that the resulting observation requests

of priority 0, 1, or 2 are sent to each satellite at the beginning

of the simulation horizon. We assume also that the second

ones are detected by any satellite when flying over them, resulting

in observation requests of priority 3.

We compared three ways of managing observation requests,

in fact three ways of deciding to trigger or not an

observation just before its starting time:

• to apply a very simple decision rule: trigger it when it is

physically possible (DR1);

• to apply a bit less blind decision rule: trigger it when it

is physically possible and not in conflict with a future observation

of higher priority (DR2);

• to use the result of the anytime observation planning module:

trigger it when it is the first observation of the current

plan (AP ).

Moreover, in order to measure the distance to optimality,

we compare these three realistic management options,

with two unrealistic ones. The first one, we refer to as SP

for super-powerful, assumes that the observation planning

module has each time enough time to reason from the current

time to the end of the simulation horizon. The second

one, we refer to as SP O for super-powerful and omniscient,

assumes in addition that it knows from the beginning of the

simulation horizon what will happen over the whole simulation

horizon i.e., the ground phenomena that will appear and

the resulting observation requests.

For each of these management options (either realistic or

not), for each satellite, and for each priority level, we measured

the ratio between the number of performed observations

and the number of observation requests.

Table 10 shows typical results obtained on one satellite

for which energy and memory constraints strongly limit the

number of requests that can be satisfied. Note immediately

that the total number of satisfied requests (last column) does

not change dramatically from DR1 to AP (from 82 to 88).

What significantly changes is the distribution of these satisfied

requests between priority levels. Decision rules DR1

and DR2, which do not take into account energy and memory,

satisfy too many low priority observation requests that

consume energy and memory and then prevent the satellite

from satisfying later high priority observation requests 9 . As

expected, DR2, which is a bit less blind than DR1, performs

better than this latter does. As expected too, the anytime

planning module AP , which takes into account energy

and memory, performs better than DR2 does. Surprisingly,

despite of a reasoning horizon that is limited by real-time

constraints, it performs almost as well as unrealistic superpowerful

and omniscient planning modules (SP and SP O):

it only fails to satisfy one request of priority 3 and one of

priority 2.

Figure 11 shows the evolution of the energy and of the

memory available on-board resulting from the AP management

option. Sharp increases in energy, followed by a

plateau, occur when the satellite goes from a night to a day

period and sharp increases in memory occur when the satellite

is in visibility of a reception station and can down-load



ICAPS 2005

The presentation we made may give the impression of local

decision problems, separately studied and solved via specialized

algorithms, without any global view of the whole

9 These poor results could be improved, at least with regard to

memory constraints, by allowing the on-board memory management

system to overwrite low priority data with high priority one,

as suggested in (Khatib et al. 2003).

26 Workshop on Planning under Uncertainty for Autonomous Systems

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