This paper presents the design, modeling, control and navigation for a novel ground-based mobile sensing platform that can collect multi-modal data in agricultural research farms for high throughput modular plant phenotyping. The platform will have the following capabilities (i) Navigate in a row-crop farm to collect data with minimal human intervention during operation (ii) Autonomous decision making i.e, it can take its own decisions for maximizing the value of information of the acquired data and (iii) Scalable in terms of the size of the farmland. The design requirements for such a platform or robot is formulated, and a detailed discussion on realizing such a design is presented. The dynamics of the robot is presented in the state space form and it is abstracted in the form of a control flow diagram for the automatic steering system. An adaptive sampling approach has been taken to generate an estimated belief-space which is leveraged in the proposed opportunistic sensing scheme to generate way-points for navigation.
- Dynamic Systems and Control Division
Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping
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Saha, H, Gao, T, Emadi, H, Jiang, Z, Singh, A, Ganapathysubramanian, B, Sarkar, S, Singh, A, & Bhattacharya, S. "Autonomous Mobile Sensing Platform for Spatio-Temporal Plant Phenotyping." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 2: Mechatronics; Estimation and Identification; Uncertain Systems and Robustness; Path Planning and Motion Control; Tracking Control Systems; Multi-Agent and Networked Systems; Manufacturing; Intelligent Transportation and Vehicles; Sensors and Actuators; Diagnostics and Detection; Unmanned, Ground and Surface Robotics; Motion and Vibration Control Applications. Tysons, Virginia, USA. October 11–13, 2017. V002T21A005. ASME. https://doi.org/10.1115/DSCC2017-5207
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