John-Paul Ore

Autonomous Aerial Water Sampling

Obtaining spatially separated, high-frequency water samples from rivers and lakes is critical to enhance our understanding and effective management of freshwater resources. In this work, we present an aerial water sampler and assess the system through field experiments. The aerial water sampler has the potential to vastly increase the speed and range at which scientists obtain water samples while reducing cost and effort. The water sampling system includes 1 a mechanism to capture three 20i¾?ml samples per mission, 2 sensors and algorithms for altitude approximation over water, and 3 software components that integrate and analyze sensor data, control the vehicle, drive the sampling mechanism, and manage risk. We validate the system in the lab, characterize key sensors, develop a framework for quantifying risk, and present results of outdoor experiments that characterize the performance of the system under windy conditions. In addition, we compare water samples from local lakes obtained by our system to samples obtained by traditional sampling techniques. We find that even winds of 5.8i¾?m/s have little impact on the water sampling system and that the samples collected are consistent with traditional techniques for most properties. These experiments show that despite the challenges associated with flying precisely over water, it is possible to quickly obtain scientifically useful water samples with an unmanned aerial vehicle.

On air-to-water radio communication between UAVs and water sensor networks

Ocean monitoring using underwater sensor networks faces communication challenges in retrieving data, communicating large amounts of data between nodes, and covering increasing spatial regions while remaining connected. With underwater sensor networks that are capable of surfacing, unmanned aerial vehicles (UAVs) provide a solution to this by providing radio-based data muling services, but, as this area is still unexplored, the utility of this solution is unclear. In this paper, we examine the theoretical expectations, perform several field experiments, and analyze the communication success rates of 802.15.4 radios near the water surface both communicating between surface nodes as well as between a node and the UAV. These indicate that on the water surface internode radio communication is poor, but node to UAV communication can provide both reasonable ranges and success rates. We additionally measure and analyze the energy aspects of the systems, determining the impacts of parameters such as network size and distance between nodes on the UAV energy. Finally, we consolidate the information into an algorithm outlining how to configure and design hybrid UAV and underwater sensor network systems.

Environmental Reviews and Case Studies: Bringing Unmanned Aerial Systems Closer to the Environment

Increasingly, Unmanned Aerial Systems (UASs) are changing the way that scientists and practitioners collect environmental data. Current UASs, however, are largely relegated to collecting data while flying remotely, far away in the air. This article examines two case studies where micro-UASs fly in immediate proximity to the environment, enabling them to collect physical samples and capture sensor data that cannot be obtained at a distance. The first case study presents an aerial water sampler that flies to remote locations and dips a pump into the water to collect samples for lab analysis. The second case study examines a UAS that flies within a meter of crops to accurately measure their height. Each requires different sensors and methods specifically tailored to operating and interacting near the environment. This article evaluates the performance of these systems and also presents preliminary validation that they collect datasets that are compatible with those gathered by existing approaches. Futhermore, it distills some common underlying design and operating principles shared by UASs aimed at working close to the environment. Finally, this article concludes that in spite of numerous pending challenges, UASs that directly interact with the environment will transform the way environmental data is collected.

Surface classification for sensor deployment from UAV landings

Using Unmanned Aerial Vehicles (UAVs) to deploy sensor networks promises an autonomous and useful method of installation in remote or hard to access locations. Some sensors, such as soil moisture sensors, must be physically installed in soft soil, yet UAVs cannot easily determine soil softness with remote sensors. In this paper, we use data from an onboard accelerometer measured during UAV landings to determine the softness of the ground. We collect and analyze over 200 data sets gathered from 8 different materials: foam, carpet, wood, tile, grass, dirt, concrete, and woodchips. Based on this analysis, we examine a number of features from the accelerometer and four classification algorithms: LDA, QDA, SVM, and binary decision trees. The decision tree performs well and is simple to implement onboard the UAV. We implement this in our UAV control system and perform experiments to verify that the UAV can accurately classify the softness of the surface with 90% accuracy. This lays the groundwork for our future work on developing a UAV capable of installing sensors in soft soil.

Phriky-units: a lightweight, annotation-free physical unit inconsistency detection tool

Systems that interact with the physical world use software that represents and manipulates physical quantities. To operate correctly, these systems must obey the rules of how quantities with physical units can be combined, compared, and manipulated. Incorrectly manipulating physical quantities can cause faults that go undetected by the type system, likely manifesting later as incorrect behavior. Existing approaches for inconsistency detection require code annotation, physical unit libraries, or specialized programming languages. We introduce Phriky-Units, a static analysis tool that detects physical unit inconsistencies in robotic software without developer annotations. It does so by capitalizing on existing shared libraries that handle standardized physical units, common in the cyber-physical domain, to link class attributes of shared libraries to physical units. In this work, we describe how Phriky-Units works, provide details of the implementation, and explain how Phriky-Units can be used. Finally we present a summary of an empirical evaluation showing it has an 87% true positive rate for a class of inconsistencies we detect with high-confidence.

Controlled sensor network installation with unmanned aerial vehicles

Robots improve wireless sensor network (WSN) deployments by reducing deployment times, deploying nodes to improve coverage, and ferrying data. Utilizing Unmanned Aerial Vehicles (UAVs) to install sensor networks in environmentally sensitive areas is especially valuable, as the UAVs are able to quickly traverse rough and environmentally sensitive terrain. UAV based deployments are challenging, as the UAVs may need to install nodes in a specific orientation or location type, which is difficult to sense from a UAV. We present our work towards resolving these difficulties by first classifying the surface a UAV has landed on, and then conducting a post-deployment analysis of the installation.

Sensing Water Properties at Precise Depths from the Air

Water properties critical to our understanding and managing of freshwater systems change rapidly with depth. This work presents an Unmanned Aerial Vehicle (UAV) based method of keeping a passive, cable-suspended sensor payload at a precise depth, with 95% of submerged sensor readings within ±8.4 cm of the target depth, helping dramatically increase the spatiotemporal resolution of water science datasets. We use a submerged depth altimeter attached at the terminus of a semirigid cable as the sole input to a method of controlling the altitude of a UAV. We first model both the system and common environmental disturbances of wind, water, and GPS drift before implementing the system. In field experiments, we compare the precision of our new method to using the UAV’s air-pressure altimeter or using UAV-mounted ultrasonic sensors. We find that our new method reduces the standard deviation of depth readings by 75% in winds up to 8 m/s. We also show the step response of the system when transitioning between target depths. Finally we explore the depth-altimeter method with a longer, 8 m cable and show that our approach still greatly outperforms previous methods.

Assessing the type annotation burden

Type annotations provide a link between program variables and domain-specific types. When combined with a type system, these annotations can enable early fault detection. For type annotations to be cost-effective in practice, they need to be both accurate and affordable for developers. We lack, however, an understanding of how burdensome type annotation is for developers. Hence, this work explores three fundamental questions: 1) how accurately do developers make type annotations; 2) how long does a single annotation take; and, 3) if a system could automatically suggest a type annotation, how beneficial to accuracy are correct suggestions and how detrimental are incorrect suggestions? We present results of a study of 71 programmers using 20 random code artifacts that contain variables with physical unit types that must be annotated. Subjects choose a correct type annotation only 51% of the time and take an average of 136 seconds to make a single correct annotation. Our qualitative analysis reveals that variable names and reasoning over mathematical operations are the leading clues for type selection. We find that suggesting the correct type boosts accuracy to 73%, while making a poor suggestion decreases accuracy to 28%. We also explore what state-of-the-art automated type annotation systems can and cannot do to help developers with type annotations, and identify implications for tool developers.

Towards code-aware robotic simulation: vision paper

This vision paper explores the potential to dramatically enrich robotic simulations with insights gleaned from program analysis, and promises to be a key tool for future robot system developers to reduce effort and find tricky corner cases. Robotic simulations are a critical, cost-effective tool for developing, testing, and validating robotic software. However, most robotics simulations are intentionally unaware of how the code works. Our approach leverages two recent developments: 1) automatic program analysis that can semantically ground program variables and predicates in physical quantities like distance, velocity, or force; and 2) standardized simulation specifications that identify both what elements are simulated and also how they are simulated. Code-aware robotic simulation could enable robot system developers who increasingly rely on simulation to lower the cost and risk of system development by having access to richer simulation scenarios. We describe the approach using a detailed, step-by-step illustration for C++ using the Robot Operating System (ROS) and the Simulation Description Format (SDFormat), and identify key challenges to realizing this vision.