5 EXPERIMENTS
We present some experimental results to solve plan-
ning described with Spatial-PDDL.
5.1 Experimental Results
The computation time to solve a spatial planning
problem described in Space-PDDL depends on com-
puting time of answering to queries.
Table 2: Computation Time of Plan Solution Generation
Solving the problem in Example 3 (seconds). The path
computation time is less then one second.
Queries Solving
problemQueries number Answer to query
[2-8] < 1 < 1
[9-13] [1-2.2] [1 - 3.08]
[14-20] [3.01-4.8] [4.01 - 7]
[21-30] [5.05-10.09] [8.07 -
15.01]
Table 2 shows that the computation time depends
on the number of requests and the response time.
The latter depends on the rules number defined in
SpaceOntology. This computing time is very reason-
able even if we have a large number of requests. In-
deed, in Table 2, for a query number between 21 and
30, solving the problem is done in 15.01 seconds.
6 CONCLUSIONS
Our main contribution was the extension of PDDL to
spatial knowledge: Spatial-PDDL. To do this, we uses
SpaceOntology that considers several spatial knowl-
edge aspects; the space hierarchy, spatial relations
(numeric, topological and fuzzy). This permits to
consider not only numeric spatial information in pre-
conditions but also topological and fuzzy information.
Moreover, spatial reasoning (the inference mecha-
nisms) provided by this ontology permits a complex
reasoning. SpaceOntology in the planning, is not used
for verification or control, but as information needed
for planning.
Future work will concern the improvement of spa-
tial interaction between human and robots. In other
words, we aim to formalize communication between
them, especially in the case where the set of fuzzy
rules are not sufficient to find a solution.
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