Paper Info
Title
A cognitive architecture for automatic gardening.
Abstract
A cognitive system to autonomously control the growth of plants is proposed.The system integrates artificial intelligence and robotic techniques.Decisions are made using symbolic planning and machine learning.Plants are modelled using 3D model acquisition of deformable objects (leaves).Action rules are learned during run-time under the guidance of a human gardener. In large industrial greenhouses, plants are usually treated following well established protocols for watering, nutrients, and shading/light. While this is practical for the automation of the process, it does not tap the full potential for optimal plant treatment. To more efficiently grow plants, specific treatments according to the plant individual needs should be applied. Experienced human gardeners are very good at treating plants individually. Unfortunately, hiring a crew of gardeners to carry out this task in large greenhouses is not cost effective. In this work we present a cognitive system that integrates artificial intelligence (AI) techniques for decision-making with robotics techniques for sensing and acting to autonomously treat plants using a real-robot platform. Artificial intelligence techniques are used to decide the amount of water and nutrients each plant needs according to the history of the plant. Robotic techniques for sensing measure plant attributes (e.g. leaves) from visual information using 3D model representations. These attributes are used by the AI system to make decisions about the treatment to apply. Acting techniques execute robot movements to supply the plants with the specified amount of water and nutrients.
Year
DOI
Venue
2017
10.1016/j.compag.2017.04.015
Computers and Electronics in Agriculture
Keywords
Field
DocType
Cognitive architecture,Optimized plant treatments,Automatic gardening,Human-robot interaction,Learning planning operators
Computer vision,Cognitive systems,Greenhouse,Automation,Artificial intelligence,Engineering,Robot,Cognitive architecture,Machine learning,Human–robot interaction,Robotics
Journal
Volume
Issue
ISSN
138
C
0168-1699
Citations 
PageRank 
References 
2
0.40
20
Authors
6
Name
Order
Citations
PageRank
Alejandro Agostini112510.06
Guillem Alenyà221927.43
Andreas Fischbach3312.40
Hanno Scharr443037.92
Florentin Wörgötter51304119.30
Carme Torras61155115.66