TY - GEN
T1 - Embedded High Precision Control and Corn Stand Counting Algorithms for an Ultra-Compact 3D Printed Field Robot
AU - Kayacan, Erkan
AU - Zhang, Zhongzhong
AU - Chowdhary, Girish
N1 - Publisher Copyright:
© 2018, MIT Press Journals. All rights reserved.
PY - 2018
Y1 - 2018
N2 - This paper presents embedded high precision control and corn stands counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor and stand counting are measured manually. This is highly labor intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm to enable TerraSentia to count corn stands by driving through the fields autonomously. We present results of an extensive field-test study that shows that (i) the robot can track paths precisely with less than 5 cm error so that the robot is less likely to damage plants, and (ii) the machine vision algorithm is robust against interferences from leaves and weeds, and the system has been verified in corn fields at the growth stage of V4, V6, VT, R2, and R6 from five different locations. The robot predictions agree well with the ground truth with countrobot = 0.96 × counthuman + 0.85 and correlation coefficient R = 0.96.
AB - This paper presents embedded high precision control and corn stands counting algorithms for a low-cost, ultra-compact 3D printed and autonomous field robot for agricultural operations. Currently, plant traits, such as emergence rate, biomass, vigor and stand counting are measured manually. This is highly labor intensive and prone to errors. The robot, termed TerraSentia, is designed to automate the measurement of plant traits for efficient phenotyping as an alternative to manual measurements. In this paper, we formulate a Nonlinear Moving Horizon Estimator (NMHE) that identifies key terrain parameters using onboard robot sensors and a learning-based Nonlinear Model Predictive Control (NMPC) that ensures high precision path tracking in the presence of unknown wheel-terrain interaction. Moreover, we develop a machine vision algorithm to enable TerraSentia to count corn stands by driving through the fields autonomously. We present results of an extensive field-test study that shows that (i) the robot can track paths precisely with less than 5 cm error so that the robot is less likely to damage plants, and (ii) the machine vision algorithm is robust against interferences from leaves and weeds, and the system has been verified in corn fields at the growth stage of V4, V6, VT, R2, and R6 from five different locations. The robot predictions agree well with the ground truth with countrobot = 0.96 × counthuman + 0.85 and correlation coefficient R = 0.96.
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U2 - 10.15607/RSS.2018.XIV.036
DO - 10.15607/RSS.2018.XIV.036
M3 - Conference contribution
AN - SCOPUS:85104188113
SN - 9780992374747
T3 - Robotics: Science and Systems
BT - Robotics
A2 - Kress-Gazit, Hadas
A2 - Srinivasa, Siddhartha S.
A2 - Howard, Tom
A2 - Atanasov, Nikolay
PB - MIT Press Journals
T2 - 14th Robotics: Science and Systems, RSS 2018
Y2 - 26 June 2018 through 30 June 2018
ER -