James V. Henrickson

Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research

Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Aircraft system identification using artificial neural networks with flight test data

This paper presents linear system identification results using noisy data from a six-degree-of-freedom aircraft simulation and data obtained from flight test of an Unmanned Aerial System using the recently developed novel Artificial Neural Network System Identification algorithm. The method uses an artificial neural network with a single input layer and single output layer to learn the elements of the state transition and control distribution matrices directly. This results in a discrete time state-space model of the aircraft dynamics which is then converted to continuous time using standard techniques. For simulated data, the true linear model is used for verifying the identified model. Linear models are generated from the flight data and are compared with results obtained using the well-established Observer/Kalman Identification algorithm. Results show that the neural-network method identifies valid models for both longitudinal and lateral/directional axes, and performs comparably to Observer/Kalman Identification. Advantages of the method include ease-of-use and low complexity.

Infrastructure assessment with small unmanned aircraft systems

As small unmanned aircraft systems become increasingly affordable, reliable, and formally recognized under federal regulation, they become increasingly attractive as novel platforms for civil applications. This paper details the development and demonstration of both fixed-wing and rotorcraft-based unmanned systems for infrastructure assessment tasks. Tasks such as inspecting pavement condition and vegetation encroachment on roadways, debris and tree coverage on railways, and corrosion on bridges and water towers are considered. Rationale for sensor, vehicle, and ground equipment selections are provided, in addition to developed flight operation procedures for two-man crews. Preliminary imagery results are presented and analyzed, and these results demonstrate that both fixed-wing and rotor-wing small unmanned aircraft systems hold promise in enabling rapid and cost-efficient infrastructure health assessment.

Multispectral and DSLR sensors for assessing crop stress in corn and cotton using fixed-wing unmanned air systems

As small unmanned aircraft systems become increasingly affordable, reliable, and formally recognized under federal regulation, they become increasingly attractive as novel platforms for civil applications. This paper details the development and demonstration of fixed-wing unmanned aircraft systems for precision agriculture tasks. Tasks such as soil moisture content and high throughput phenotyping are considered. Rationale for sensor, vehicle, and ground equipment selections are provided, in addition to developed flight operation procedures for minimal numbers of crew. Preliminary imagery results are presented and analyzed, and these results demonstrate that fixed-wing unmanned aircraft systems modified to carry non-traditional sensors at extended endurance durations can provide high quality data that is usable for serious scientific analysis.

Shape Control of Tensegrity Structures

Tensegrity is a relatively new approach to structural design that has seen great advances in recent years. The unique properties of tensegrity structures allow for the design of deployable and lightweight structures|a combination highly applicable in the context of space systems. This work focuses on a rigorous development of shape control for tensegrity

Optimal placement of solar reflectors at the lunar south pole

A recent NASA Innovative Advanced Concepts study suggests that several reflectors placed around the perimeter of Shackleton Crater may be capable of redirecting uninterrupted solar energy to a region within the crater year-round—effectively creating an energy oasis in the otherwise dark and cold extreme environment. This work further explores this concept by identifying sets of reflector placement locations around the perimeter of Shackleton Crater that maximize the amount of time in which at least one reflector in the set has access to sunlight. Using LRO LOLA data, a 3D model of Shackleton Crater and the surrounding terrain is created. Using a ray-tracing algorithm, synthetic imagery of this region is then generated using ephemeris data for the year 2020, from which an illumination model is generated. A placement optimization search algorithm is then developed, and results are shown for cases of 1, 2, and 3 reflectors over a search space focused on the western ridge of the crater. Results indicate that three reflectors strategically placed around the perimeter of the crater would be capable of providing energy to an area within the crater for at least 92.5% of the year if placed at surface level, or at least 96.8% if placed 100 meters above the surface.

Corn and sorghum phenotyping using a fixed-wing UAV-based remote sensing system

Recent development of unmanned aerial systems has created opportunities in automation of field-based high-throughput phenotyping by lowering flight operational cost and complexity and allowing flexible re-visit time and higher image resolution than satellite or manned airborne remote sensing. In this study, flights were conducted over corn and sorghum breeding trials in College Station, Texas, with a fixed-wing unmanned aerial vehicle (UAV) carrying two multispectral cameras and a high-resolution digital camera. The objectives were to establish the workflow and investigate the ability of UAV-based remote sensing for automating data collection of plant traits to develop genetic and physiological models. Most important among these traits were plant height and number of plants which are currently manually collected with high labor costs. Vegetation indices were calculated for each breeding cultivar from mosaicked and radiometrically calibrated multi-band imagery in order to be correlated with ground-measured plant heights, populations and yield across high genetic-diversity breeding cultivars. Growth curves were profiled with the aerial measured time-series height and vegetation index data. The next step of this study will be to investigate the correlations between aerial measurements and ground truth measured manually in field and from lab tests.

Shape Control of Tensegrity Airfoils

This paper develops and applies tensegrity concepts to the design of shape-controllable 2D airfoils. After introducing tensegrity systems and dynamics, a tension-driven shape control strategy is outlined, and a method of generating variable complexity tensegrity airfoils is developed. The described shape control strategy is then applied to the task of transforming a given tensegrity airfoil from some initial shape to a desired final shape. Results show the generation of tensegrity systems that approximate various NACA 4series airfoil profiles, and simulation results demonstrate successful shape control of both symmetric and asymmetric tensegrity airfoils.

Characterization of Shape Memory Alloys Using Artificial Neural Networks

Shape memory alloys are capable of delivering advantageous solutions to a wide range of engineering-based problems. Implementation of these solutions, however, is often complicated by the hysteretic, non-linear, thermomechanical behavior of the material. Existing constitutive models are largely capable of accurately describing this unique behavior, but they require prior characterization of material parameters. Current characterization procedures necessitate extensive data collection and data processing, creating a high barrier of entry for shape memory alloy application. This thesis develops a novel approach in which a form of computational intelligence is applied to the task of shape memory alloy material parameter characterization. Specifically, this work develops a methodology in which an artificial neural network is trained to identify transformation temperatures and stress influence coefficients of shape memory alloy specimens using strain-temperature coordinates as inputs. Training data is generated through the use of an existing shape memory alloy constitutive model. Factorial and Taguchi-based methods of generating training data are implemented and compared. Results show that trained artificial neural networks are capable of identifying shape memory alloy material parameters with satisfactory accuracy. Comparison of the implemented training data generation methods indicates that the Taguchi-based approach yields an artificial neural network that outperforms that of the factorial-based approach despite requiring significantly fewer training data specimens.

Reflector placement for providing near-continuous solar power to robots in Shackleton Crater

Motivated by a NASA Innovative Advanced Concepts study assessing the possibility of using solar reflectors to create a perpetual solar power-based energy oasis at the south pole of the Moon, this work identifies location combinations near Shack-leton Crater that provide near-continuous access to sunlight. Specifically, location combinations are found in which at least one location in the set has access to sunlight throughout 99% of the year 2020. Locations are identified and evaluated using a process involving two illumination simulation and analysis methods and a set-cover algorithm. Results are presented that demonstrate the trade-off between number of locations and height above surface level to achieve near-continuous access to sunlight near Shackleton Crater. Identified near-continuous solar power solutions span from three locations 25 meters above surface level to single locations 775 meters above surface level.