Asterix: Robotic weed control in row crops
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Vegetables and other row-crops represent a large share of the agricultural production. There is a large variation in crop species, and a limited availability in specialized herbicides. The robot presented here utilizes the systematic cultivation techniques of row crops to navigate and operate in the field. By the use of machine vision it separates seeded vegetable crops from weed, and treat each weed within the row with individual herbicide droplets, without affecting the crop. This Dropon- Demand (DoD) method allow the use of non-selective herbicides and significant reductions in herbicide use. This thesis presents six research papers concerning the development of the DoD system and the mobile robot. The robot is tailored to its purpose with cost, maintainability, effcient operation and robustness in mind. The three-wheeled design is unconventional, and the design maintains maneuverability and stability with the benefit of reduced weight, complexity and cost. Topics within localization and navigation for agricultural robotics have been explored. Quaternion estimation by an Extended Kalman Filter and a Non-linear complimentary filter has been implemented on an ARM Cortex M3 microcontroller. A Bayesian framework for fusing delayed Visual Odometry measurements has been explored in simulations. A Non-linear Model Predictive Controller (NMPC) has been developed and explored in simulation to enable a controller guaranteed to not sway its wheels into the crop row and subsequently damage the crop. The framework is also suitable for implementing other constrains for operation in other environments, such as greenhouses or confined spaces. Path following by an adaptive controller and a Model Reference Adaptive Controller (MRAC) has been implemented and compared in indoor trials. The DoD system for herbicide application has been developed within and in connection with this project. The influence of liquid properties viscosity and surface tension on the formation and stability of droplets has been tested in lab trials. A control circuit for synchronized control of solenoid valves was developed and tested. Indoor pot trials with four weed species demonstrated that the Drop-on-Demand system (DoD) could control the weeds with as little as 7.6 µg glyphosate or 0.15 µg iodosulfuron per plant. The results also highlight the importance of liquid characteristics for leaf retention, as the common herbicide glyphosate had no effect unless mixed with suitable additives. The trials document the DoD effect on weed species not previously described in literature, and with an alternative herbicide to glyphosate, iodosulfuron. A field trial with the robot was performed in a carrot field, and all the weeds were effectively controlled with the DoD system. The robot and DoD system represent an important contribution to the range of systems presented witin Precision Agriculture for in-row weed control - a movement which as a whole represent a paradigm shift to the environmental impact and health risks of weed control, while providing valuable new tools to the producers.
Has partsPaper 1: Utstumo, Trygve; Gravdahl, Jan Tommy. Implementation and Comparison of Attitude Estimation Methods for Agricultural Robotics. 4th IFAC Conference on Modelling and Control in Agriculture, Horticulture and Post Harvest Industry - IFAC Proceedings Volumes, Vol. 46, Issue 18, August 2013, Pages 52-57 https://doi.org/10.3182/20130828-2-SF-3019.00051
Paper 2: Urdal, Frode; Utstumo, Trygve; Vatne, Jan Kåre; Ellingsen, Simen Andreas Ådnøy; Gravdahl, Jan Tommy. Design and control of precision drop-on-demand herbicide application in agricultural robotics. I: ICARCV 2014: Proceedings of the 13th International Conference on Control, Automation, Robotics and Vision. IEEE conference proceedings - https://doi.org/10.1109/ICARCV.2014.7064570 © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Paper 3: Utstumo, Trygve; Gravdahl, Jan Tommy; Berge, Therese W.. Non-linear Model Predictive Control for constrained robot navigation in row crops. Proceedings of the 2015 IEEE International Conference on Industrial Technology (ICIT 2015) https://doi.org/10.1109/ICIT.2015.7125124 © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Paper 4: Utstumo, Trygve; Dørum, Jarle; Gravdahl, Jan Tommy. Experimental comparison of adaptive controllers for trajectory tracking in agricultural robotics. I: 19th International Conference on System Theory, Control and Computing (ICSTCC). IEEE conference proceedings 2015 https://doi.org/10.1109/ICSTCC.2015.7321294 © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Paper 5: Arbo, Mathias Hauan; Utstumo, Trygve; Brekke, Edmund Førland; Gravdahl, Jan Tommy. Unscented Multi-Point Smoother for Fusion of Delayed Displacement Measurements: Application to Agricultural Robots. Modeling, Identification and Control 2017 ;Volum 38.(1) s. 1-9 https://doi.org/10.3390/robotics6040024 - Published with the Creative Commons Attribution 3.0 Unported (CC BY 3.0) license. See: http://creativecommons.org/licenses/by/3.0/.
Paper 6: T. Utstumo, F. Urdal, A. Brevik, J. Dørum, J. Netland, Ø. Overskeid, T. W. Berge, and J. T. Gravdahl. Robotic in-row weed control for vegetables. - © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ The final published version in Computers and Electronics in Agriculture Volume 154, November 2018, Pages 36-45 https://doi.org/10.1016/j.compag.2018.08.043