To prove the concept, researchers demonstrated the robot’s accurate navigation through remotely-transmitted GPS waypoints. A laser sensing platform called light detection and ranging, or LiDAR, helps the robot dodge obstacles. ORNL engineers outfitted the robot with an external frame equipped with augers, tools shaped like a large metal drill. Microcontrollers manage lowering the augers so their rotation churns soil upward into a bucket. The robot can take up to four samples at once before returning independently to its home position, said Masuo, who developed the software as well as some hardware and electrical adaptations to the robot.
The technology supports efforts to study interactions between plants and the environment. Using robots for sampling could enable scientists and farmers to gather more data while focusing on analysis, Masuo said.
The project lays the foundation for future field research integrating automation with real-time computing for SMART monitoring or ecosystem management. Development of newer, more durable, multifunctional, self-powered sensors will further enable continuously gathering above- and belowground data on system performance and responses, supporting higher-precision process models and artificial intelligence foundation models in the future.
Additional sensors could be added to the robot to expand monitoring capabilities and deepen understanding of ecosystem health, as well as conditions that enhance productivity and resilience. For example, the LiDAR and visual cameras that are part of the unit could be used to check plant density and stem diameter to better understand plant growth, Masuo said. LiDAR essentially creates a 3D map, which can be combined with video footage afterward to understand surrounding environmental conditions. Productivity of the plants has also been monitored using pole-mounted LiDAR.
The robotic platform as well as sensors in the SMART field system could be enhanced, for instance, to facilitate research into early indictors of disease and responses to environmental stress or disturbances such as wildfire, as well as how plants are absorbing and storing atmospheric CO2.
The project was funded through ORNL’s INTERSECT program, an LDRD initiative intended to encourage cross-disciplinary collaboration to tackle challenges such as autonomous experiments that leverage automation, advanced computing, scientific instruments and facilities.
This is the third collaboration between Kalluri and engineers from ORNL’s Manufacturing Science Division involving robotics applications. Previously, they worked together on two projects to develop automated monitoring and sampling systems for greenhouse research settings at ORNL, as the lab accelerates the transformation of plants for better bioenergy crops for biobased fuels, chemicals and materials, enabling a thriving bioeconomy.
“Through INTERSECT, I grow to understand needs I wouldn’t see because they are outside my area of study,” Nycz said. “When researchers from different fields are exposed to those needs and what is possible — that’s where the new ideas are being formed.”
“It’s been impressive how well this cross-cut team has worked together to reach the demonstration point in under a year,” Kalluri said. “We’ve tied together some very cool expertise that can make a difference in enhancing our understanding of the processes at play in both bioenergy plant productivity and carbon storage.”
The MDF, supported by the DOE Office of Energy Efficiency and Renewable Energy’s Advanced Materials and Manufacturing Technologies Office, is a nationwide consortium of collaborators working with ORNL to innovate, inspire and catalyze the transformation of U.S. manufacturing.
UT-Battelle manages ORNL for DOE’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. DOE’s Office of Science is working to address some of the most pressing challenges of our time. For more information, visit https://energy.gov/science. — Heather Duncan and Stephanie Seay
This Oak Ridge National Laboratory news article "Harvesting plant data with robotics, sensors and advanced computing" was originally found on https://www.ornl.gov/news
