scholarly journals Integrating Answer Set Programming with Semantic Dictionaries for Robot Task Planning

2017 ◽  
Author(s):  
Dongcai Lu ◽  
Yi Zhou ◽  
Feng Wu ◽  
Zhao Zhang ◽  
Xiaoping Chen
Keyword(s):  
Answer Set Programming ◽  
Planning System ◽  
Service Robot ◽  
Task Planning ◽  
Planning Framework ◽  
Representation Language ◽  
Robot Task ◽  
User Instructions ◽  
High Level ◽  
Answer Set

In this paper, we propose a novel integrated task planning system for service robot in domestic domains. Given open-ended high-level user instructions in natural language, robots need to generate a plan, i.e., a sequence of low-level executable actions, to complete the required tasks. To address this, we exploit the knowledge on semantic roles of common verbs defined in semantic dictionaries such as FrameNet and integrate it with Answer Set Programming --- a task planning framework with both representation language and solvers. In the experiments, we evaluated our approach using common benchmarks on service tasks and showed that it can successfully handle much more tasks than the state-of-the-art solution. Notably, we deployed the proposed planning system on our service robot for the annual RoboCup@Home competitions and achieved very encouraging results.

Automatic robot program synthesis for assembly

Robotica ◽  
1992 ◽  
Vol 10 (2) ◽  
pp. 113-123 ◽  
Author(s):  
H. A. ElMaraghy ◽  
J. M. Rondeau
Keyword(s):  
Error Detection ◽  
Automatic Generation ◽  
Planning System ◽  
Problem Solver ◽  
Task Definition ◽  
Robot Trajectory ◽  
Planning Framework ◽  
Robot Task ◽  
Definition Of ◽  
High Level

SUMMARYThis paper describes a revised version of ROBOPLAN, a goal-oriented robot task planning system for automatic generation, decomposition and execution of high-level robot plans for assembly. It emphasizes its new features, i.e., modularity, formal definition of the task, robust plan synthesis, and execution of each assembly step. A task definition language allows a formal description of the robot universe and the assembly task to be input to ROBOPLAN. The expert task planner is a non-linear backward chaining problem solver, using a goal driven depth-first strategy. The implemented search strategy has been tested in the assembly domain, but it could be used in other domains where planning is needed. The motion planner provides a non-optimal, safe robot trajectory; collision free path planning has not been included yet. A robot executable code is generated for each assembly step and monitored in real time. The error detection and recovery capability of the system is rather limited at present, since no sensors are used. The initial implementation of the system has been tested and evaluated on the assembly of a DC motor. The potential of extending this planning framework to other applications is also discussed.

Action language ℬ𝒞+

2015 ◽  
Vol 30 (4) ◽  
pp. 899-922 ◽  
Author(s):  
Joseph Babb ◽  
Joohyung Lee
Keyword(s):  
Main Idea ◽  
Answer Set Programming ◽  
Stable Model ◽  
Formal Models ◽  
Transition Systems ◽  
Action Languages ◽  
Action Language ◽  
Language Constructs ◽  
High Level ◽  
Answer Set

Abstract Action languages are formal models of parts of natural language that are designed to describe effects of actions. Many of these languages can be viewed as high-level notations of answer set programs structured to represent transition systems. However, the form of answer set programs considered in the earlier work is quite limited in comparison with the modern Answer Set Programming (ASP) language, which allows several useful constructs for knowledge representation, such as choice rules, aggregates and abstract constraint atoms. We propose a new action language called BC +, which closes the gap between action languages and the modern ASP language. The main idea is to define the semantics of BC + in terms of general stable model semantics for propositional formulas, under which many modern ASP language constructs can be identified with shorthands for propositional formulas. Language BC  + turns out to be sufficiently expressive to encompass the best features of other action languages, such as languages B , C , C + and BC . Computational methods available in ASP solvers are readily applicable to compute BC +, which led to an implementation of the language by extending system cplus2asp .

scholarly journals RTPO: A Domain Knowledge Base for Robot Task Planning

Electronics ◽  
2019 ◽  
Vol 8 (10) ◽  
pp. 1105 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Chen
Keyword(s):  
Knowledge Base ◽  
Domain Knowledge ◽  
Task Planning ◽  
Specific Domain ◽  
Goal State ◽  
Ontology Language ◽  
Planning Algorithm ◽  
Domain Knowledge Base ◽  
Robot Task ◽  
High Level

Knowledge can enhance the intelligence of robots’ high-level decision-making. However, there is no specific domain knowledge base for robot task planning in this field. Aiming to represent the knowledge in robot task planning, the Robot Task Planning Ontology (RTPO) is first designed and implemented in this work, so that robots can understand and know how to carry out task planning to reach the goal state. In this paper, the RTPO is divided into three parts: task ontology, environment ontology, and robot ontology, followed by a detailed description of these three types of knowledge, respectively. The OWL (Web Ontology Language) is adopted to represent the knowledge in robot task planning. Then, the paper proposes a method to evaluate the scalability and responsiveness of RTPO. Finally, the corresponding task planning algorithm is designed based on RTPO, and then the paper conducts experiments on the basis of the real robot TurtleBot3 to verify the usability of RTPO. The experimental results demonstrate that RTPO has good performance in scalability and responsiveness, and the robot can achieve given high-level tasks based on RTPO.

Bidirectional Data Exchange between C++ and Prolog for Robot Task Planning in the Hybrid Control Architecture

2014 ◽  
Vol 1039 ◽  
pp. 361-367 ◽  
Author(s):  
Gui Xin Wu ◽  
Shuo Xu ◽  
Da Wei Tu
Keyword(s):  
Data Exchange ◽  
Data Transfer ◽  
Hybrid Control ◽  
Qualitative Reasoning ◽  
Service Robot ◽  
Control Architecture ◽  
Task Planning ◽  
Reactive Control ◽  
Quantitative Calculation ◽  
Robot Task

To realize hierarchical robot task planning in the hybrid deliberative/reactive control architecture, C++ and Prolog are integrated to implement qualitative reasoning and quantitative calculation. With respect to a typical case in service robot operation studies, two experiments are conducted to examine “Prolog loading C++ programs” and “C++ loading Prolog programs”. They get the results of unidirectional data transfer and bidirectional data transfer between the two kinds of software, respectively. The research method is extendable to different engineering applications and different Prolog development environments.

High-level synthesis of on-chip multiprocessor architectures based on answer set programming

2018 ◽  
Vol 117 ◽  
pp. 161-179 ◽  
Author(s):  
Christophe Bobda ◽  
Franck Yonga ◽  
Martin Gebser ◽  
Harold Ishebabi ◽  
Torsten Schaub
Keyword(s):  
Answer Set Programming ◽  
Chip Multiprocessor ◽  
High Level Synthesis ◽  
Multiprocessor Architectures ◽  
On Chip ◽  
High Level ◽  
Answer Set

scholarly journals The Seventh Answer Set Programming Competition: Design and Results

2019 ◽  
Vol 20 (2) ◽  
pp. 176-204 ◽  
Author(s):  
MARTIN GEBSER ◽  
MARCO MARATEA ◽  
FRANCESCO RICCA
Keyword(s):  
Knowledge Representation ◽  
Logic Programming ◽  
State Of The Art ◽  
Answer Set Programming ◽  
The State ◽  
International Conference ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
The University ◽  
Answer Set

AbstractAnswer Set Programming (ASP) is a prominent knowledge representation language with roots in logic programming and non-monotonic reasoning. Biennial ASP competitions are organized in order to furnish challenging benchmark collections and assess the advancement of the state of the art in ASP solving. In this paper, we report on the design and results of the Seventh ASP Competition, jointly organized by the University of Calabria (Italy), the University of Genova (Italy), and the University of Potsdam (Germany), in affiliation with the 14th International Conference on Logic Programming and Non-Monotonic Reasoning (LPNMR 2017).

Extending Action Languages for Intelligent Service Robot Task Planning

2014 ◽  
Vol 24 (7) ◽  
pp. 1614-1625
Author(s):  
Guo-Qiang JIN ◽  
Xiao-Ping CHEN
Keyword(s):  
Service Robot ◽  
Task Planning ◽  
Action Languages ◽  
Robot Task

scholarly journals Planning with preferences using logic programming

2006 ◽  
Vol 6 (5) ◽  
pp. 559-607 ◽  
Author(s):  
TRAN CAO SON ◽  
ENRICO PONTELLI
Keyword(s):  
Logic Programming ◽  
Answer Set Programming ◽  
Planning Problem ◽  
Declarative Language ◽  
High Level ◽  
Planning Problems ◽  
Answer Set

We present a declarative language, ${\cal PP}$, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of ${\cal PP}$ allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with ${\cal PP}$ preferences.

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