Application and analysis of a robust trajectory tracking controller for under-characterized autonomous vehicles

Wise, Melonee; Hsu, John

doi:10.1109/cca.2008.4629651

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…The cross-track error (distance) of recorded GPS points along each two-row path were also calculated following the formula in the papers [71][72][73] to verify the trajectory accuracy.…”

Section: Validation Experiments Resultsmentioning

confidence: 99%

“…Additionally, the position error and average position error are used to evaluate the accuracy of autonomous navigation and are determined by formulas described in [70]. The cross-track error (distance) of recorded GPS points along each two-row path were also calculated following the formula in the papers [71][72][73] to verify the trajectory accuracy. All the LiDAR and GPS recordings were processed and analyzed through our developed pipeline to calculate the plant volume per row/plot.…”

Section: Lidar Data Captured By the Dairybiobotmentioning

confidence: 99%

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Design of an Unmanned Ground Vehicle and LiDAR Pipeline for the High-Throughput Phenotyping of Biomass in Perennial Ryegrass

et al. 2020

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Perennial ryegrass biomass yield is an important driver of profitability for Australian dairy farmers, making it a primary goal for plant breeders. However, measuring and selecting cultivars for higher biomass yield is a major bottleneck in breeding, requiring conventional methods that may be imprecise, laborious, and/or destructive. For forage breeding programs to adopt phenomic technologies for biomass estimation, there exists the need to develop, integrate, and validate sensor-based data collection that is aligned with the growth characteristics of plants, plot design and size, and repeated measurements across the growing season to reduce the time and cost associated with the labor involved in data collection. A fully automated phenotyping platform (DairyBioBot) utilizing an unmanned ground vehicle (UGV) equipped with a ground-based Light Detection and Ranging (LiDAR) sensor and Real-Time Kinematic (RTK) positioning system was developed for the accurate and efficient measurement of plant volume as a proxy for biomass in large-scale perennial ryegrass field trials. The field data were collected from a perennial ryegrass row trial of 18 experimental varieties in 160 plots (three rows per plot). DairyBioBot utilized mission planning software to autonomously capture high-resolution LiDAR data and Global Positioning System (GPS) recordings. A custom developed data processing pipeline was used to generate a plant volume estimate from LiDAR data connected to GPS coordinates. A high correlation between LiDAR plant volume and biomass on a Fresh Mass (FM) basis was observed with the coefficient of determination of R2 = 0.71 at the row level and R2 = 0.73 at the plot level. This indicated that LiDAR plant volume is strongly correlated with biomass and therefore the DairyBioBot demonstrates the utility of an autonomous platform to estimate in-field biomass for perennial ryegrass. It is likely that no single platform will be optimal to measure plant biomass from landscape to plant scales; the development and application of autonomous ground-based platforms is of greatest benefit to forage breeding programs.

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“…The cross-track error (distance) of recorded GPS points along each two-row path were also calculated following the formula in the papers [71][72][73] to verify the trajectory accuracy.…”

Section: Validation Experiments Resultsmentioning

confidence: 99%