George A. Kantor
Research Professor / Associate Director of Education Carnegie Mellon University
- Pittsburgh PA
George Kantor has been developing robotic technologies to address problems in agriculture and scientific exploration for 15 years.
Biography
The control of dynamical systems becomes increasingly important as the era of robotics research dominated by quasi-static machines rapidly comes to a close. Similarly, the importance of state estimation grows as robotic applications require robots to function in larger, more complex environments. George's research addresses both of these issues by focusing on the dual problems of controlling robotic mechanisms with non-trivial dynamics and perceiving the state of world through indirect measurements. His approach is both analytical and experimental: George uses mathematics to understand the physical behavior of a given system and then use that understanding to create algorithms for control or estimation. George strives to develop new theoretical concepts and translate them into real-world implementations that solve problems such as balancing an unstable robot or estimating the location of an autonomous vehicle.
Areas of Expertise
Media Appearances
Pittsburgh-Based AI Startup Bloomfield Robotics is Acquired by Kubota Corporation, a Global Tractor Manufacturer
AP News online
2024-09-11
“Our goal with Bloomfield from our first day was to enable farmers to produce more with fewer resources,” says Bloomfield Co-Founder George Kantor. “To feed the ever-increasing global population, farmers need to increase the productivity of their crops using fewer scarce resources, and Bloomfield provides them the plant-level knowledge and data that they need to do it.”
Why CMU’s School of Computer Science (finally) launched a bachelor of science in robotics
Technical.ly online
2023-09-12
“Part of the reason that we waited until now to introduce this degree is because we were waiting for the jobs to become available,” co-director George Kantor said. Here's what students can expect from the new program.
CMU Puts AI To Work in New NSF-funded Institutes
Carnegie Mellon University online
2021-07-30
George Kantor, a research professor in RI, will lead CMU's work in the USDA-NIFA AI Institute for Resilient Agriculture (AIIRA), which is focused on AI and robotics in agriculture.
Seeing a future for crop estimation technology
Good Fruit Grower online
2020-08-04
That’s where the grower comes in, said George Kantor, the Carnegie Mellon researcher who led the development of the technology and co-founded Bloomfield. His research team is looking for ways to sense occluded fruit, but for now, the system doesn’t need to see all the berries to work in consistently managed vineyards. Growers must count a few sample vines by hand and plug that data into the computer so it can develop a ratio of “visible” to “invisible” fruit for yield estimations.
Pittsburgh's Girls of Steel Robotics Team Advances to Championship
90.5 WESA online
2015-04-06
George Kantor is a senior systems scientist at CMU’s Robotic Institute. He and fellow CMU employee Patti Rote co-founded the team five years ago with the intention of encouraging young women to pursue careers in science, technology, engineering and math (STEM) fields.
Balancing robot may care for disabled, elderly
NBC News online
2006-08-17
Ballbot, a narrow, 5-foot-tall robot, balances delicately on what looks like a bowling ball. Swaying slightly on a laboratory floor, the aluminum-framed droid seems ready to fall at any moment.
Media
Social
Industry Expertise
Education
University of Maryland
Ph.D.
Electrical and Computer Engineering
1999
University of Maryland
M.S.
Electrical Engineering, Electrical Engineering & Controls
1995
Michigan State University
B.S.
Electrical Engineering
1990
Affiliations
- Girls of Steel Robotics : Director
Links
Articles
Towards Autonomous Crop Monitoring: Inserting Sensors in Cluttered Environments
IEEE Robotics and Automation Letters2024
Monitoring crop nutrients can aid farmers in optimizing fertilizer use. Many existing robots rely on vision-based phenotyping, however, which can only indirectly estimate nutrient deficiencies once crops have undergone visible color changes. We present a contact-based phenotyping robot platform that can directly insert nitrate sensors into cornstalks to proactively monitor macronutrient levels in crops. This task is challenging because inserting such sensors requires sub-centimeter precision in an environment which contains high levels of clutter, lighting variation, and occlusion.
Cyber-agricultural systems for crop breeding and sustainable production
Trends in Plant Science2024
The cyber-agricultural system (CAS) represents an overarching framework of agriculture that leverages recent advances in ubiquitous sensing, artificial intelligence, smart actuators, and scalable cyberinfrastructure (CI) in both breeding and production agriculture. We discuss the recent progress and perspective of the three fundamental components of CAS – sensing, modeling, and actuation – and the emerging concept of agricultural digital twins (DTs).
Mapping of Potential Fuel Regions Using Uncrewed Aerial Vehicles for Wildfire Prevention
Forests2023
This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground.
A Robust Illumination-Invariant Camera System for Agricultural Applications
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2021
Object detection and semantic segmentation are two of the most widely adopted deep learning algorithms in agricultural applications. One of the major sources of variability in image quality acquired outdoors for such tasks is changing lighting conditions that can alter the appearance of the objects or the contents of the entire image. While transfer learning and data augmentation reduce the need for large amount of data to train deep neural networks to some extent, the large variety of cultivars and the lack of shared datasets in agriculture makes wide-scale field deployments difficult.
Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2019
Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases.
