Auflistung nach Autor:in "Winkler, Jan"
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- ZeitschriftenartikelOpen-EASE: A Cloud-Based Knowledge Service for Autonomous Learning(KI - Künstliche Intelligenz: Vol. 29, No. 4, 2015) Tenorth, Moritz; Winkler, Jan; Beßler, Daniel; Beetz, MichaelWe present Open-EASE, a cloud-based knowledge base of robot experience data that can serve as episodic memory, providing a robot with comprehensive information for autonomously learning manipulation tasks. Open-EASE combines both robot and human activity data in a common, semantically annotated knowledge base, including robot poses, object information, environment models, the robot’s intentions and beliefs, as well as information about the actions that have been performed. A powerful query language and inference tools support reasoning about the data and retrieving information based on semantic queries. In this paper, we focus on applications of Open-EASE in the context of autonomous learning.
- ZeitschriftenartikelWeed Management of the Future(KI - Künstliche Intelligenz: Vol. 33, No. 4, 2019) Amend, Sandra; Brandt, David; Di Marco, Daniel; Dipper, Tobias; Gässler, Gabriel; Höferlin, Markus; Gohlke, Maurice; Kesenheimer, Katharina; Lindner, Peter; Leidenfrost, Roland; Michaels, Andreas; Mugele, Tobias; Müller, Arthur; Riffel, Tanja; Sampangi, Yeshwanth; Winkler, JanThe methods used to protect agricultural products currently undergo drastic changes. Artificial Intelligence is a prime candidate to overcome two challenges faced by farmers around the world: The increasing cost and decreasing availability of human labor for weed control, and the growing global restriction of herbicides. Deep Learning is one of the most prominent approaches for applying AI to all kinds of use cases in industrial applications, entertainment, and security. Its latest field of application is plant classification that enables automated weed control and precise spot spraying of herbicides. While cheap, powerful platforms for deploying classification mechanisms are widely available, this comes at the cost of expensive and effort rich classifier training. This effectively makes Deep Learning-based approaches unavailable for the majority of the agricultural sector. Deepfield Robotics presents a systematic approach for deploying AI onto fields at large, including the learnings that led to their self-contained AI driven plant classification modules that relieve individuals from having to deploy their own AI solution. The same technology acts as enabler for more agricultural domains, such as targeted fertilization, nano irrigation, and automated phenotyping. This article documents Deepfield Robotics’ findings and vision on how AI can be the workhorse for agricultural weeding labor.