PDDL Planning with Natural Language-Based Scene Understanding for UAV-UGV Cooperation

Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent...

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Bibliographic Details
Published in:Applied sciences 2019-09, Vol.9 (18), p.3789
Main Authors: Moon, Jiyoun, Lee, Beom-Hee
Format: Article
Language:English
Subjects:
Algorithms
artificial intelligence
autonomous robots
Cognition & reasoning
Cooperation
Decision making
Deep learning
Environmental information
Image retrieval
Information sharing
Knowledge acquisition
Language
language descriptions
mission planning
Natural language
Neural networks
Planning
Robotics
Robots
Scene analysis
semantic graphs
Semantics
Sensors
Unmanned aerial vehicles
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Summary:Natural-language-based scene understanding can enable heterogeneous robots to cooperate efficiently in large and unconstructed environments. However, studies on symbolic planning rarely consider the semantic knowledge acquisition problem associated with the surrounding environments. Further, recent developments in deep learning methods show outstanding performance for semantic scene understanding using natural language. In this paper, a cooperation framework that connects deep learning techniques and a symbolic planner for heterogeneous robots is proposed. The framework is largely composed of the scene understanding engine, planning agent, and knowledge engine. We employ neural networks for natural-language-based scene understanding to share environmental information among robots. We then generate a sequence of actions for each robot using a planning domain definition language planner. JENA-TDB is used for knowledge acquisition storage. The proposed method is validated using simulation results obtained from one unmanned aerial and three ground vehicles.
ISSN:2076-3417
2076-3417
DOI:10.3390/app9183789