Development of a mobile robotic phenotyping system for growth chamber-based studies of genotype x environment interactions

In order to fully understand the interaction between phenotype and genotype x environment to improve crop performance, a large amount of phenotypic data is needed. Studying plants of a given strain under multiple environments can greatly help to reveal their interactions. This thesis presents two key portions of the development of the Enviratron rover, a robotic system that aims to autonomously collect the labor-intensive data required to perform experiments in this area. The rover is part of a larger project which will track plant growth in multiple environments. The first aspects of the robot discussed in this thesis is the system hardware and main, or whole-chamber, imaging system. Semi-autonomous behavior is currently achieved, and the system performance in probing leaves is quantified and discussed. In contrast to existing systems, the rover can follow magnetic tape along all four directions (front, left, back, right), and uses a Microsoft Kinect V2 mounted on the end-effector of a robotic arm to position a threaded rod, simulating future sensors such as fluorimeter and Raman Spectrometer, at a desired position and orientation. Advantages of the tape following include being able to reliably move both between chambers and within a chamber regardless of dust and lighting conditions. The robot arm and Kinect system is unique in its speed at reconstructing an (filtered) environment when combined with its accuracy at positioning sensors. A comparison of using raw camera coordinates data and using KinectFusion data is presented. The results suggest that the KinectFusion pose estimation is fairly accurate, only decreasing accuracy by a few millimeters at distances of roughly 0.8 meter. The system can consistently position sensors to within 4 cm of the goal, and often within 3 cm. The system is shown to be accurate enough to position sensors to ñ 9 degrees of a desired orientation, although currently this accuracy requires human input to fully utilize the Kinect’s feedback. The second aspect of the robot presented in this thesis is a framework for generating collision-free robot arm motion within the chamber. This framework uses feedback from the Kinect sensor and is based on the Probabilistic Roadmaps (PRM) technique, which involves creating a graph of collision-free nodes and edges, and then searching for an acceptable path. The variant presented uses a dilated, down-sampled, KinectFusion as input for rapid collision checking, effectively representing the environment as a discretized grid and representing the robot arm as a collection of spheres. The approach combines many desirable characteristics of previous PRM methods and other collision-avoidance schemes, and is aimed at providing a reliable, rapidly-constructed, highly-connected roadmap which can be queried multiple times in a static environment, such as a growth chamber or a greenhouse. In a sample plant configuration with several of the most challenging practical goal poses, it is shown to create a roadmap in an average time of 32.5 seconds. One key feature is that nodes are added near the goal during each query, in order to increase accuracy at the expense of increased query time. A completed graph is searched for an optimal path connecting nodes near the starting pose and the desired end pose. The fastest graph search studied was an implementation of the A* algorithm. Queries using this framework took an average time of 0.46 seconds. The average distance between the attained pose and the desired location was 2.7 cm. Average distance C-space between the attained pose and the desired location was 3.65 degrees. The research suggests that the robotic framework presented has the potential to fulfill the main hardware and motion requirements of an autonomous indoor phenotyping robot, and can generate desired collision-free robot arm motion

Active Inverse Reward Design

Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training environment. We propose structuring this process as a series of queries asking the user to compare between different reward functions. Thus we can actively select queries for maximum informativeness about the true reward. In contrast to approaches asking the designer for optimal behavior, this allows us to gather additional information by eliciting preferences between suboptimal behaviors. After each query, we need to update the posterior over the true reward function from observing the proxy reward function chosen by the designer. The recently proposed Inverse Reward Design (IRD) enables this. Our approach substantially outperforms IRD in test environments. In particular, it can query the designer about interpretable, linear reward functions and still infer non-linear ones

Robotic 3D Plant Perception and Leaf Probing with Collision-Free Motion Planning for Automated Indoor Plant Phenotyping

Various instrumentation devices for plant physiology study such as chlorophyll fluorimeter and Raman spectrometer require leaf probing with accurate probe positioning and orientation with respect to leaf surface. In this work, we aimed to automate this process with a Kinect V2 sensor, a high-precision 2D laser profilometer, and a 6-axis robotic manipulator in a high-throughput manner. The relatively wide field of view and high resolution of Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. Given the number of plants, the location and size of each plant were estimated by K-means clustering. A real-time collision-free motion planning framework based on Probabilistic Roadmap was adopted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from top with the short-range profilometer to obtain a high-precision point cloud where potential leaf clusters were extracted by region growing segmentation. Each leaf segment was further partitioned into small patches by Voxel Cloud Connectivity Segmentation. Only the small patches with low root mean square values of plane fitting were used to compute probing poses. To evaluate probing accuracy, a square surface was scanned at various angles and its centroid was probed perpendicularly with a probing position error of 1.5 mm and a probing angle error of 0.84 degrees on average. Our growth chamber leaf probing experiment showed that the average motion planning time was 0.4 seconds and the average traveled distance of tool center point was 1 meter

Development of a Mobile Robotic Phenotyping System for Growth Chamber-based Studies of Genotype x Environment Interactions

To increase understanding of the interaction between phenotype and genotype x environment to improve crop performance, large amounts of phenotypic data are needed. Studying plants of a given strain under multiple environments can greatly help to reveal their interactions. To collect the labor-intensive data required to perform experiments in this area, a Mecanum-wheeled, magnetic-tape-following indoor rover has been developed to accurately and autonomously move between and inside growth chambers. Integration of the motor controllers, a robot arm, and a Microsoft Kinect (v2) 3D sensor was achieved in a customized C++ program. Detecting and segmenting plants in a multi-plant environment is a challenging task, which can be aided by integration of depth data into these algorithms. Image-processing functions were implemented to filter the depth image to minimize noise and remove undesired surfaces, reducing the memory requirement and allowing the plant to be reconstructed at a higher resolution in real-time. Three-dimensional meshes representing plants inside the chamber were reconstructed using the Kinect SDK’s KinectFusion. After transforming user-selected points in camera coordinates to robot-arm coordinates, the robot arm is used in conjunction with the rover to probe desired leaves, simulating the future use of sensors such as a fluorimeter and Raman spectrometer. This paper reports the system architecture and some preliminary results of the system

Association of ABC (HbA1c, Blood Pressure, LDL-Cholesterol) Goal Attainment with Depression and Health-Related Quality of Life among Adults with Type 2 Diabetes

Aims: To determine the relationship between ABC goal attainment, depression, and health-related quality of life (HRQoL) among a national sample of patients with type 2 diabetes (T2DM). Methods: A retrospective, cross-sectional analysis was performed examining 808 non-pregnant patients ≥ 20 years old with T2DM from the National Health and Nutrition Examination Survey (NHANES) 2007-2012. ABC goals were defined as HbA1c \u3c 7%, BP \u3c 130/80mmHg, and LDL-C \u3c 100mg/dL. Patient characteristics associated with ABC goal attainment were examined. Results: Overall, 23.7% of participants achieved simultaneous ABC goals. Severe depression was significantly associated with lower rates of ABC goal attainment compared to those with no depression (5.0% vs. 25.4%, p = 0.048). ABC goal attainment rates were lower among females, Hispanic and non-Hispanic Black minority groups, and patients with a duration of diabetes over five years, while increased visits with health care professionals was significantly associated with meeting all three ABC goals for patients with T2DM. Conclusions: The relationship between simultaneous ABC goal attainment, depression and HRQoL is complex. Patients with T2DM unable to meet ABC goals may benefit from increased contact with health care professionals