CLC number: TP242
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2019-03-14
Cited: 0
Clicked: 7377
Citations: Bibtex RefMan EndNote GB/T7714
Yu-qian Jiang, Shi-qi Zhang, Piyush Khandelwal, Peter Stone. Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems[J]. Frontiers of Information Technology & Electronic Engineering, 2019, 20(3): 363-373.
@article{title="Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems",
author="Yu-qian Jiang, Shi-qi Zhang, Piyush Khandelwal, Peter Stone",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="20",
number="3",
pages="363-373",
year="2019",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1800514"
}
%0 Journal Article
%T Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
%A Yu-qian Jiang
%A Shi-qi Zhang
%A Piyush Khandelwal
%A Peter Stone
%J Frontiers of Information Technology & Electronic Engineering
%V 20
%N 3
%P 363-373
%@ 2095-9184
%D 2019
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1800514
TY - JOUR
T1 - Task planning in robotics: an empirical comparison of PDDL- and ASP-based systems
A1 - Yu-qian Jiang
A1 - Shi-qi Zhang
A1 - Piyush Khandelwal
A1 - Peter Stone
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 20
IS - 3
SP - 363
EP - 373
%@ 2095-9184
Y1 - 2019
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1800514
Abstract: Robots need task planning algorithms to sequence actions toward accomplishing goals that are impossible through individual actions. Off-the-shelf task planners can be used by intelligent robotics practitioners to solve a variety of planning problems. However, many different planners exist, each with different strengths and weaknesses, and there are no general rules for which planner would be best to apply to a given problem. In this study, we empirically compare the performance of state-of-the-art planners that use either the planning domain description language (PDDL) or answer set programming (ASP) as the underlying action language. PDDL is designed for task planning, and PDDL-based planners are widely used for a variety of planning problems. ASP is designed for knowledge-intensive reasoning, but can also be used to solve task planning problems. Given domain encodings that are as similar as possible, we find that PDDL-based planners perform better on problems with longer solutions, and ASP-based planners are better on tasks with a large number of objects or tasks in which complex reasoning is required to reason about action preconditions and effects. The resulting analysis can inform selection among general-purpose planning systems for particular robot task planning domains.
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