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Automated In-Field Leaf-Level Hyperspectral Imaging of Corn Plants Using a Cartesian Robotic PlatformZiling Chen (8810570) 21 June 2022 (has links)
Agriculture-related industry and academia have widely adopted Hyperspectral Imaging
(HSI) based in-field phenotyping activities. Current HSI solutions such as airborne remote sensing
platforms and handheld spectrometers have been proven effective and have become popular in
various phenotyping applications. However, the quality of remote sensing systems suffers from a
low signal-over-noise ratio due to the imaging distance and low resolution. Handheld leaf
spectrometers are slow, labor-intensive, and only measure a small spot on the leaf, which fails to
represent the canopy variation. In 2018, the Purdue ABE sensor lab developed a new handheld
hyperspectral leaf imager, LeafSpec. For the first time, field phenotyping researchers were able to
collect high-resolution leaf hyperspectral images without the negative impacts of ambient lighting
and leaf-slope angle changes. LeafSpec has been successfully tested in field assays and showed its
advantageous phenotyping quality. The goal of this study was to test the hypothesis that a robotic
system could replace the human operator required to perform in-field and leaf-level HSI using
LeafSpec. The system consisted of a modified version of the LeafSpec device, a machine vision
system for target leaf detection, a National Instrument MyRIO as a controller and a customized
cartesian robotic arm with five Degrees of Freedom (DOF). For each scan, the on-board machine
vision system recognized the top leaf collar and obtained the target coordinates. The coordinates
were then passed to the controller, which calculated the appropriate path and acceleration profile
and drove the arm to approach the target leaf and scan the leaf with the LeafSpec device. The
scanned image was then processed in real-time to calculate plant physiological features such as
chlorophyll content, nitrogen content and so on. In the 2019 field test, the designed system
collected data from 41 corn plants with two genotypes and three levels of nitrogen treatments with
an average cycle time of 86 seconds. The nitrogen content predicted by the designed system had
an R squared of 0.72 with the ground truth. The developers, therefore, concluded that the robotic
gantry system was capable of replacing human operators for LeafSpec hyperspectral corn leaf
imaging in the field with high quality.
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Enhancing Safety for Autonomous Systems via Reachability and Control Barrier FunctionsJason King Ching Lo (10716705) 06 May 2021 (has links)
<div>In this thesis, we explore different methods to enhance the safety and robustness for autonomous systems. We achieve this goal using concepts and tools from reachability analysis and control barrier functions. We first take on a multi-player reach-avoid game that involves two teams of players with competing objectives, namely the attackers and the defenders. We analyze the problem and solve the game from the attackers' perspectives via a moving horizon approach. The resulting solution provides a safety guarantee that allows attackers to reach their goals while avoiding all defenders. </div><div><br></div><div>Next, we approach the problem of target re-association after long-term occlusion using concepts from reachability as well as Bayesian inference. Here, we set out to find the probability identity matrix that associates the identities of targets before and after an occlusion. The solution of this problem can be used in conjunction with existing state-of-the-art trackers to enhance their robustness.</div><div><br></div><div>Finally, we turn our attention to a different method for providing safety guarantees, namely control barrier functions. Since the existence of a control barrier function implies the safety of a control system, we propose a framework to learn such function from a given user-specified safety requirement. The learned CBF can be applied on top of an existing nominal controller to provide safety guarantees for systems.</div>
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Smart Sensing System for a Lateral Micro Drilling RobotJose Alejandro Solorio Cervantes (11191893) 28 July 2021 (has links)
The oil and gas industry
faces a lack of compact drilling devices capable of performing horizontal
drilling maneuvers in depleted or abandoned wells in order to enhance oil
recovery. The purpose of this project was to design and develop a smart sensing
system that can be later implemented in compact drilling devices used to
perform horizontal drilling to enhance oil recovery in wells. A smart sensor is
the combination of a sensing element (sensor) and a microprocessor. Hence, a
smart sensing system is an arrangement that consists of different sensors,
where one or more have smart capabilities. The sensing system was built and
tested in a laboratory setting. For this, a test bench was used as a case study
to simulate the operation from a micro-drilling device. The smart sensing
system integrated the sensors essential for the direct operational measurements
required for the robot. The focus was on selecting reliable and sturdy
components that can handle the operation Down the Hole (DTH) on the final
lateral micro-drilling robot. The sensing system's recorded data was sent to a
microcontroller, where it was processed and then presented visually to the
operator through a User Interface (UI) developed in a cloud-based framework.
The information was filtered, processed, and sent to a controller that executed
commands and sent signals to the test bench’s actuators. The smart sensing
system included novel modules and sensors suitable for the operation in a harsh
environment such as the one faced in the drilling process. Furthermore, it was
designed as an independent, flexible module that can be implemented in test
benches with different settings and early robotic prototypes. The outcome of
this project was a sensing system able to provide robotic drilling devices with
flexibility while providing accurate and reliable measurements during their
operation.
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IIoT-based Instrumentation and Control System for a Lateral Micro-drilling Robot Using Machine Fault Diagnosis and Failure PrognosisJose A. Solorio Cervantes (11191893) 11 October 2023 (has links)
<p dir="ltr">This project aimed to develop an instrumentation and control system for a micro-drilling robot based on Industrial Internet of Things (IIoT) technologies. The automation system integrated IIoT technological tools to create a robust automation system capable of being used in drilling operations. The system incorporated industrial-grade sensors, which carried out direct measurements of the critical variables of the process. The indirect variables relevant to the control of the robot were calculated from the measured parameters. The system also considered the telemetry architecture necessary to reliably transmit data from the down-the-hole (DTH) robot to a receiver on the surface. Telemetry was based on wireless communication through long-range radio frequency (LoRa). The system developed had models based on Artificial Intelligence (AI) and Machine Learning (ML) for determining the mode of operation, detecting changes in the process, and changes in drilling variables in critical hydraulic components for the drilling process. Algorithms based on AI and ML models also allowed the user to make better decisions based on the variables' correlation to optimize the drilling process (e.g., dynamic change of flow, pressure, and RPMs based on automatic rock identification). A user interface (UI) was developed, and digital tools to perform data analysis were implemented. Safety assessment in all robot systems (e.g., electrical, hardware, software) was contemplated as a critical design component. The result of this research project provides innovative micro-drilling robots with the necessary technological tools to optimize the drilling process. The system made drilling more efficient, reliable, and safe, providing diagnostic and prognostic tools that allowed planning maintenance based on the actual health of the devices. The system that was developed was tested in a test bench under controlled conditions within a laboratory to characterize the system and collect data that allowed ML models' development, training, validation, and testing. The prototype of a micro-drilling robot installed on the test bench served as a case study to assess the implemented models' reliability and the proposed telemetry.</p>
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