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This paper describes the data collected by the University of Nebraska-Lincoln (UNL) as part of the field deployments during the Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) flight campaign in July 2018. The UNL deployed two multirotor unmanned aerial systems (UASs) at multiple sites in the San Luis Valley (Colorado, USA) for data collection to support three science missions: convection initiation, boundary layer transition, and cold air drainage flow. We conducted 172 flights resulting in over 21 h of cumulative flight time. Our novel design for the sensor housing onboard the UAS was employed in these flights to meet the aspiration and shielding requirements of the temperature and humidity sensors and to separate them from the mixed turbulent airflow from the propellers. Data presented in this paper include timestamped temperature and humidity data collected from the sensors, along with the three-dimensional position and velocity of the UAS. Data are quality-controlled and time-synchronized using a zero-order-hold interpolation without additional post-processing. The full dataset is also made available for download at https://doi.org/10.5281/zenodo.4306086 (Islam et al., 2020).

Unmanned aerial systems (UAS) can advance understanding of the atmosphere and improve weather prediction, but public perceptions of drone technologies need to be assessed to ensure successful societal integration. Our qualitative study examines public perceptions of UAS technology, and the associated risks and benefits, for such civilian purposes. We examine how people form perceptions, and discuss the implications of these perceptions for UAS design and regulation. Our study finds the public to be favorable toward UAS used for “noble” purposes. Participant views are informed by popular media, personal experiences, comparisons between technologies, and consideration of the trustworthiness of the users, regulators, and technology itself.

This paper describes the data collected by the University of Nebraska-Lincoln (UNL) as part of the field deployment during the Lower Atmospheric Process Studies at Elevation – a Remotely-piloted Aircraft Team Experiment (LAPSE-RATE) flight campaign in July 2018. UNL deployed two multirotor unmanned aerial systems (UASs) at various sites in the San Luis Valley (Colorado, USA) for data collection in support of three science missions: convection-initiation, boundary layer transition, and cold air drainage flow. We conducted 172 flights resulting in over 1300 minutes of cumulative flight time. Our novel design for the sensor housing onboard the UAS was employed in these flights to meet the aspiration and shielding requirements of the temperature/humidity sensors, and attempt to separate them from the mixed turbulent airflow from the propellers. Data presented in this paper include time-stamped temperature and humidity data collected from the sensors, along with the three-dimensional position and velocity of the UAS. Data are quality controlled and time-synchronized using a zero-order-hold interpolation without additional post processing. The full dataset is also made available for download at (https://doi.org/10. 5281/zenodo.4306086 (Islam et al. , 2020)).

Projections indicate that, as an industry, unmanned aerial vehicles (UAVs, commonly known as drones) could bring more than 100 000 jobs and $80 billion in economic growth to the U.S. by 2025 [1]. However, these promising projections do not account for how various publics may perceive such technologies. Understanding public perceptions is important because the attitudes of different groups can have large effects on the trajectory of a technology, strongly facilitating or hindering technology acceptance and uptake [2].

High-flying unmanned aerial vehicles (UAVs) are transforming industrial and research agriculture by delivering high spatiotemporal resolution data on a field environment. While current UAVs fly high above fields collecting aerial imagery, future low-flying aircraft will directly interact with the environment and will utilize a wider variety of sensors. Safely and reliably operating close to unstructured environments requires improving UAVs' sensing, localization, and control algorithms. To this end, we investigate localizing a micro-UAV in corn phenotyping trials using a laser scanner and IMU to control the altitude and position of the vehicle relative to the plant rows. In this process, the laser scanner is not only a means of localization, but also a scientific instrument for measuring plant properties. Experimental evaluations demonstrate that the is capable of safely and reliably operating in real-world phenotyping trials. We experimentally validate the system in both low and high wind conditions in fully mature corn fields. Using test data from 18 test flights, we show that the UAV is capable of localizing its position to within one field row of the true position.

Intelligent utilization of resources and improved mission performance in an autonomous agent require consideration of cyber and physical resources. The allocation of these resources becomes more complex when the system expands from one agent to multiple agents, and the control shifts from centralized to decentralized. Consensus is a distributed algorithm that lets multiple agents agree on a shared value, but typically does not leverage mobility. We propose a coupled consensus control strategy that co-regulates computation, communication frequency, and connectivity of the agents to achieve faster convergence times at lower communication rates and computational costs. In this strategy, agents move towards a common location to increase connectivity. Simultaneously, the communication frequency is increased when the shared state error between an agent and its connected neighbors is high. When the shared state converges (i.e., consensus is reached), the agents withdraw to the initial positions and the communication frequency is decreased. Convergence properties of our algorithm are demonstrated under the proposed co-regulated control algorithm. We evaluated the proposed approach through a new set of cyber-physical, multi-agent metrics and demonstrated our approach in a simulation of unmanned aircraft systems measuring temperatures at multiple sites. The results demonstrate that, compared with fixed-rate and event-triggered consensus algorithms, our co-regulation scheme can achieve improved performance with fewer resources, while maintaining high reactivity to changes in the environment and system.

Small unmanned aircraft systems (sUAS) are rapidly transforming atmospheric research. With the advancement of the development and application of these systems, improving knowledge of best practices for accurate measurement is critical for achieving scientific goals. We present results from an intercomparison of atmospheric measurement data from the Lower Atmospheric Process Studies at Elevation&mdash, a Remotely piloted Aircraft Team Experiment (LAPSE-RATE) field campaign. We evaluate a total of 38 individual sUAS with 23 unique sensor and platform configurations using a meteorological tower for reference measurements. We assess precision, bias, and time response of sUAS measurements of temperature, humidity, pressure, wind speed, and wind direction. Most sUAS measurements show broad agreement with the reference, particularly temperature and wind speed, with mean value differences of 1.6 &plusmn, 2 . 6 ∘ C and 0.22 &plusmn, 0 . 59 m/s for all sUAS, respectively. sUAS platform and sensor configurations were found to contribute significantly to measurement accuracy. Sensor configurations, which included proper aspiration and radiation shielding of sensors, were found to provide the most accurate thermodynamic measurements (temperature and relative humidity), whereas sonic anemometers on multirotor platforms provided the most accurate wind measurements (horizontal speed and direction). We contribute both a characterization and assessment of sUAS for measuring atmospheric parameters, and identify important challenges and opportunities for improving scientific measurements with sUAS.

Traditional configurations for mounting Temperature&ndash, Humidity (TH) sensors on multirotor Unmanned Aerial Systems (UASs) often suffer from insufficient radiation shielding, exposure to mixed and turbulent air from propellers, and inconsistent aspiration while situated in the wake of the UAS. Descent profiles using traditional methods are unreliable (when compared to an ascent profile) due to the turbulent mixing of air by the UAS while descending into that flow field. Consequently, atmospheric boundary layer profiles that rely on such configurations are bias-prone and unreliable in certain flight patterns (such as descent). This article describes and evaluates a novel sensor housing designed to shield airborne sensors from artificial heat sources and artificial wet-bulbing while pulling air from outside the rotor wash influence. The housing is mounted above the propellers to exploit the rotor-induced pressure deficits that passively induce a high-speed laminar airflow to aspirate the sensor consistently. Our design is modular, accommodates a variety of other sensors, and would be compatible with a wide range of commercially available multirotors. Extensive flight tests conducted at altitudes up to 500 m Above Ground Level (AGL) show that the housing facilitates reliable measurements of the boundary layer phenomena and is invariant in orientation to the ambient wind, even at high vertical/horizontal speeds (up to 5 m/s) for the UAS. A low standard deviation of errors shows a good agreement between the ascent and descent profiles and proves our unique design is reliable for various UAS missions.

During the past century, fire management has focused on techniques both to protect human communities from catastrophic wildfire and to maintain fire-dependent ecological systems. However, despite a large and increasing allocation of resources and personnel to achieve these goals, fire management objectives at regional to global scales are not being met. Current fire management techniques are clearly inadequate for the challenges faced by fire managers, and technological innovations are needed. Advances in unmanned aerial systems (UAS) technology provide opportunities for innovation in fire management and science. In many countries, fire management organizations are beginning to explore the potential of UAS for monitoring fires. We have taken the next step and developed a prototype that can precisely ignite fires as part of wildfire suppression tactics or prescribed fires (fire intentionally ignited within predetermined conditions to reduce hazardous fuels, improve habitat, or mitigate for large wildfires). We discuss the potential for these technologies to benefit fire management activities, while acknowledging the sizeable sociopolitical barriers that prevent their immediate broad application.

System monitoring can help to detect anomalies, but crafting monitors for robot systems is difficult due to the inherent complexity, changing, and uncertain operating environment. We address this challenge by automatically inferring system invariants and synthesizing those invariants into monitors to detect faults with an approach inspired by state of the art software engineering methods. Our approach is novel in that: (1) It automatically derives invariants from messages; (2) The invariants types are tailored to match the spatial, temporal, and architectural attributes of robotic systems; and (3) It automatically classifies and synthesizes invariants into an online invariants monitor node. We have assessed the approach in the context of two unmanned aerial vehicle systems running robot operating system. We found that monitoring the inferred invariants can reduce system failure rates when facing unexpected contexts from 76 to 11 %, and can detect differences between the lab environment and the field deployments.

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.

Fish counting is still a rudimentary process in most fisheries in Brazil. Current solutions are generally unaffordable for small and medium-size producers; hence, in order to provide a low-cost solution, this paper proposes a new technique for fish counting and presents a new image dataset to evaluate fish counting systems. The dataset is composed of a series of videos partially annotated at frame-level, which include approximately a thousand fish in high-resolution images. We describe a computer-vision based system that counts fish by combining information from blob detection, mixture of Gaussians and a Kalman filter. This work shows that the proposed method is a feasible approach for automatic fish counting, reducing costs and boosting production, as it increases labor availability. Our approach is efficient for fingerlings counting, with an average precision of 97.47%, recall of 97.61% and F-measure of 97.52% in the provided dataset.

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.

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.

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.

Systems interacting with the physical world operate on quantities measured with physical units. When unit operations in a program are inconsistent with the physical units' rules, those systems may suffer. Existing approaches to support unit consistency in programs can impose an unacceptable burden on developers. In this paper, we present a lightweight static analysis approach focused on physical unit inconsistency detection that requires no end-user program annotation, modification, or migration. 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. Then, leveraging rules from dimensional analysis, the approach propagates and infers units in programs that use these shared libraries, and detects inconsistent unit usage. We implement and evaluate the approach in a tool, analyzing 213 open-source systems containing +900,000 LOC, finding inconsistencies in 11% of them, with an 87% true positive rate for a class of inconsistencies detected with high confidence. An initial survey of robot system developers finds that the unit inconsistencies detected by our tool are 'problematic', and we investigate how and when these inconsistencies occur.

Capturing color in water is challenging due to the heavy non-uniform attenuation of light in water across the visible spectrum, which results in dramatic hue shifts toward blue. Yet observing color in water is important for monitoring and surveillance as well as marine biology studies related to species identification, individual and group behavior, and ecosystem health and activity monitoring. Underwater robots are equipped with motor control for large scale transects but they lack sensors that enable capturing color-accurate underwater images. We present a method for color-accurate imaging in water called perceptual adaptive illumination. This method dynamically mixes the illumination of an object in a distance-dependent way using a controllable multi-color light source. The color mix compensates correctly for color loss and results in an image whose color composition is equivalent to rendering the object in air. Experiments were conducted with a color palette in the pool and at three different coral reefs sites, and with an underwater robot collecting image data with the new sensor., United States. Office of Naval Research (Project N000140911051)

In this paper we describe the design and control algorithms of AMOUR , a low-cost autonomous underwater vehicle (AUV) capable of missions of marine survey and monitoring. AMOUR is a highly maneuverable robot capable of hovering and carrying dynamic payloads during a single mission. The robot can carry a variety of payloads. It uses internal buoyancy and balance control mechanisms to achieve power efficient motions regardless of the payload size. AMOUR is designed to operate in synergy with a wireless underwater sensor network (WUSN) of static nodes. The robot’s payload was designed in order to deploy, relocate and recover the static sensor nodes. It communicates with the network acoustically for signaling and localization and optically for data muling. We present control algorithms, navigation algorithms, and experimental data from pool and ocean trials with AMOUR that demonstrate its basic navigation capabilitrials with AMOUR that demonstrate its basic navigation capabili ties, power efficiency, and ability to carry dynamic payloads.

Sensors deployed in remote locations provide unprecedented amounts of data, but powering these sensors over long periods remains a challenge. In this paper, we develop and present a UAV-based wireless power transfer system. We discuss design considerations and present our system that allows a UAV to fly to remote locations to charge hard to access sensors. We analyze the impact of different materials on the wireless power transfer system. Since GPS does not provide sufficient accuracy, we develop and experimentally characterize a relative localization algorithm based on sensing the magnetic field of the power transfer system and optical flow that allows the UAV to localize the sensor with an average error of 15 cm to enable the transfer of on average 4.2 W. These results overcome some of the practical challenges associated with wirelessly charging sensors with a UAV and show that UAVs with wireless power transfer systems can greatly extend the life of remotely deployed sensors.

The significance of thermal heterogeneities in small surface water bodies as drivers of mixing and for habitat provision is increasingly recognized, yet obtaining three-dimensionally-resolved observations of the thermal structure of lakes and rivers remains challenging. Remote observations of water temperature from aerial platforms are attractive: such platforms do not require shoreline access, they can be quickly and easily deployed and redeployed to facilitate repeated sampling and can rapidly move between target locations, allowing multiple measurements to be made during a single flight. However, they are also subject to well-known limitations, including payload, operability and a tradeoff between the extent and density over which measurements can be made within restricted flight times. This paper introduces a novel aerial thermal sensing platform that lowers a temperature sensor into the water to record temperature measurements throughout a shallow water column and presents results from initial field experiments comparing emph{in situ} temperature observations to those made from the UAS platform. These experiments show that with minor improvements, UASs have the potential to enable high-resolution 3D thermal mapping of a (sim)1-ha lake in 2–3 flights (textit{circa} 2 h), sufficient to resolve diurnal variations. This paper identifies operational constraints and key areas for further development, including the need for the integration of a faster temperature sensor with the aerial vehicle and better control of the sensor depth, especially when near the water surface.

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.

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.

Recharging sensor networks using Unmanned Aerial Vehicles (UAVs) provides a possible method for increasing network lifetime. In this paper, we evaluate that approach, determining how much of a benefit it provides and under what conditions. We base our simulations and field experiments on data collected from charging with our UAV-based wireless power transfer system, which has similar transfer ranges and efficiencies as other such systems. We determine that a UAV can increase the network lifetime up to 290% compared to no recharging, that the UAV should recharge 30% of the sensor node battery capacity at one time for the maximum benefit, and that the UAV should recharge the lowest powered node until the network reaches a size of approximately 306 nodes at which point it should recharge the sink. We also examine how the sensor network can aid this through sink selection. The policy varies as network size increases, with a static approach working well until 200 nodes, and then either a perimeter or heuristic approach works best. These results inform future use of UAVs in recharging and working with sensor networks.

We define a family of novel interest operators for extracting features from one-dimensional panoramic images for use in mobile robot navigation. Feature detection proceeds by applying local interest operators in the scale space of a 1D circular image formed by averaging the center scanlines of a cylindrical panorama. We demonstrate that many such features remain stable over changes in viewpoint and in the presence of noise and camera vibration, and define a feature descriptor that collects shape properties of the scale-space surface and color information from the original images. We then present a novel dynamic programming method to establish globally optimal correspondences between features in images taken from different viewpoints. Our method can handle arbitrary rotations and large numbers of missing features. It is also robust to significant changes in lighting conditions and viewing angle, and in the presence of some occlusion.

In this paper we address the problem of planning reliable landmarkbased robot navigation strategies in the presence of significant sensor uncertainty. The navigation environments are modeled with directed weighted graphs in which edges can be traversed with given probabilities. To construct robust and efficient navigation plans, we compute “expected shortest paths” in such graphs. We formulate the expected shortest paths problem as a Markov decision process and provide two algorithms for its solution. We demonstrate the practicality of our approach using an extensive experimental analysis using graphs with varying sizes and parameters.

When extending the life of Wireless Rechargeable Sensor Networks (WRSN), one challenge is charging networks as they grow larger. Overcoming this limitation will render a WRSN more practical and highly adaptable to growth in the real world. Most charging algorithms require a priori full knowledge of sensor nodes’ power levels in order to determine the nodes that require charging. In this work, we present a probabilistic algorithm that extends the life of scalable WRSN without a priori power knowledge and without full network exploration. We develop a probability bound on the power level of the sensor nodes and utilize this bound to make decisions while exploring a WRSN. We verify the algorithm by simulating a wireless power transfer unmanned aerial vehicle, and charging a WRSN to extend its life. Our results show that, without knowledge, our proposed algorithm extends the life of a WRSN on average 90% of what an optimal full knowledge algorithm can achieve. This means that the charging robot does not need to explore the whole network, which enables the scaling of WRSN. We analyze the impact of network parameters on our algorithm and show that it is insensitive to a large range of parameter values.

Remote sensing by Unmanned Aerial Vehicles (UAVs) is changing the way agriculture operates by increasing the spatial-temporal resolution of data collection. Micro-UAVs have the potential to further improve and enrich the data collected by operating close to the crops, enabling the collection of higher spatio-temporal resolution data. In this paper, we present a UAV-mounted measurement system that utilizes a laser scanner to compute crop heights, a critical indicator of crop health. The system filters, transforms, and analyzes the cluttered range data in real-time to determine the distance to the ground and to the top of the crops. We assess the system in an indoor testbed and in a corn field. Our findings indicate that despite the dense canopy and highly variable sensor readings, we can precisely fly over crops and measure its height to within 5cm of measurements gathered using current measurement technology.

Acoustic communication typically dominates the power usage in underwater sensor networks. To balance the conflicting needs of communication and power usage, we utilize a sensor network platform with underwater acoustic communication, surface level radio communication, and a depth adjustment system to switch between them. We focus on determining which nodes should surface to create a radio communication route for situations where, for the message size, it is more energy efficient to transmit via radio yet acoustic messaging and the depth adjustment system still dominate the overall energy usage. For a given path in 2D or 3D, we develop and examine a set of eight algorithms to select these nodes while taking energy usage and packet loss into account. We perform an analysis of the algorithms and show that a decentralized approach where nodes know their two-hop neighbors provides the most energy efficient method.

We present an algorithm for estimating thruster configurations of underwater vehicles with reconfigurable thrusters. The algorithm estimates each thruster’s effect on the vehicle’s attitude and position. The estimated parameters are used to maintain the robot’s attitude and position.

Monitoring lakes, rivers, and oceans is critical to improving our understanding of complex large-scale ecosystems. In this work, we develop and analyze three path planning algorithms for underwater robots to optimize sensing in conjunction with networks of underwater sensors. The algorithms require different levels of knowledge about the environment: global, local, and decentralized control of the robot by the sensor network. We find our global Voronoi approach produces paths that are typically best for sensing, but are longer, which can be problematic if the robot has limited endurance. The local algorithm, inspired by Tangent Bug, produces paths that are usually shorter while still having good sensing. The decentralized controller also has good sensing and short paths and has the advantage that it can also adapt the depths of the underwater sensors to jointly optimize the sensor network and robot sensing and the robot path length. The drawback is the somewhat higher communication and processing requirements. For each of these algorithms we perform a detailed analysis and comparison in simulation. We identify limitations of each and provide framework for future improvements.

System monitoring can help to detect abnormalities and avoid failures. Crafting monitors for today's robotic systems, however, can be very difficult due to the systems' inherent complexity. In this work we address this challenge through an approach that automatically infers system invariants and synthesizes those invariants into monitors. The approach is novel in that it derives invariants by observing the messages passed between system nodes and the invariants types are tailored to match the spatial, temporal, and operational attributes of robotic systems. Further, the generated monitor can be seamlessly integrated into systems built on top of publish-subscribe architectures. An application of the technique on a system consisting of a unmanned aerial vehicle (UAV) landing on a moving platform shows that it can significantly reduce the number of crashes in unexpected landing scenarios.

Monitoring bridges with wireless sensor networks aids in detecting failures early, but faces power challenges in ensuring reasonable network lifetimes. Recharging select nodes with Unmanned Aerial Vehicles (UAVs) provides a solution that currently can recharge a single node. However, questions arise on the effectiveness of a limited recharging system, the appropriate node to recharge, and the best sink selection algorithm for improving network lifetime given a limited recharging system. This paper simulates such a network in order to answer those questions. It explores five different sink positioning algorithms to find which provides the longest network lifetime with the added capability of limited recharging. For a range of network sizes, our results show that network lifetime improves by over 350% when recharging a single node in the network, the best node to recharge is the one with the lowest power level, and that either the Greedy Heuristic or LP sink selection algorithms perform equally well.

Quad-rotor Micro Aerial Vehicles (MAVs) are used widely in research and increasingly in commercial applications as the cost of these platforms has dropped. The cost of entry, however, is still high in large part due to the time and effort involved in repairing vehicles after crashes while learning about the system design and dynamics. In this paper, we present an omni-directional hovercraft, which has dynamics similar to MAVs and can be used as an educational platform to teach students about the behavior and control of MAV-like platforms with minimal cost and effort. Teaching students about the capabilities and challenges associated with MAVs is critical for educating future engineers and scientists that will develop and use the next generation of MAVs. In addition, the hovercraft provides a safe platform for researchers to test control and coordination algorithms before trying them on higher-cost MAVs.

Wireless magnetic resonant power transfer is an emerging technology that has many advantages over other wireless power transfer methods due to its safety, lack of interference, and efficiency at medium ranges. In this paper, we develop a wireless magnetic resonant power transfer system that enables unmanned aerial vehicles (UAVs) to provide power to, and recharge batteries of wireless sensors and other electronics far removed from the electric grid. We address the difficulties of implementing and outfitting this system on a UAV with limited payload capabilities and develop a controller that maximizes the received power as the UAV moves into and out of range. We experimentally demonstrate our prototype wireless power transfer system by using a UAV to transfer nearly 5W of power to a ground sensor.

Acoustic communication typically dominates the power usage in underwater sensor networks. As networks underwater have very limited recharging capabilities, this challenges the network's ability to communicate collected data. To balance these conflicting needs, we utilize a sensor network platform with underwater acoustic communication, surface level radio communication, and a depth adjustment system to switch between them. Nodes determine if they should surface to communicate by approximating the network energy usage and data latency given the data transmission size. For a given path, we develop and examine a set of algorithms to select the nodes to rise to communicate the data via radio across the network while taking energy usage into account. We perform a preliminary analysis of the methods and show that for typical networks greedy approaches are nearly as good as centralized approaches, yet require minimal communication overhead and only local information.

To fully understand the ocean environment requires sensing the full water column. Utilizing a depth adjustment system on an underwater sensor network provides this while also improving global sensing and communications. This paper presents a depth adjustment system for waters up to 50 m deep that connects to the aquanode sensor network nodes. We performed experiments characterizing and demonstrating the functionality of the depth adjustment system. We discuss the application of this device in improving acoustic communication and also verify the functionality of a decentralized depth adjustment algorithm that optimizes the placement of the nodes for collecting sensing data., Singapore. DSO National Laboratories, United States. Multidisciplinary University Research Initiative (Antidote grant 138802), United States. Army Research Office. Multidisciplinary University Research Initiative. Swarms of Autonomous Robots and Mobile Sensors Project (Grant number N00014-09-1-1051), National Science Foundation (U.S.). Information Technology Research (Grantnumber IIS-0426838), National Science Foundation (U.S.). Information Technology Research (Grantnumber IIS-1116221), National Science Foundation (U.S.). Information Technology Research (Grantnumber IIS-1117178), National Science Foundation (U.S.). Information Technology Research (Grantnumber IIS-1133224)

Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. We develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum. We implement the controller on an underwater sensor network capable of adjusting their depths. Through simulations and experiments, we verify the functionality and performance of the system and algorithm.

Underwater vehicles are typically operated using a tether or a slow acoustic link. We present an underwater optical communication system that enables a high-throughput and low-latency link to an underwater robot. The optical link allows the robot to operate in cluttered environments without the need for a tether. We demonstrate the performance of the system in a number of experiments which characterize the optical link and demonstrate remote control of the robot using a human input device.

We present a modular sensor network platform capable of supporting a wide range of applications. We developed a platform to support a broad spectrum of scenarios, instantiating our system for applications on the ground, in the water, and in the air. Our system has operated in the field for over 240 days with month long continuous deployments, measuring positions, temperatures, pressures, and rainfall, while computing cattle behaviors, event locations, and future river level. We use this experimental experience to discuss the lessons learned in designing and using a modular and multi-functional system.

We present a control algorithm for autonomous underwater robots with modular thruster configuration. The algorithm can handle arbitrary thruster configurations. It maintains the robot's desired attitude while solving for translational motion. The attitude can be arbitrarily chosen from the special orthogonal group SO3 allowing the robot all possible orientations. The desired translational velocities can be chosen from R3 allowing the robot to follow arbitrary trajectories underwater. If the robot is not fully holonomic then the controller chooses the closest possible solution using least squares and outputs the error vector. We verify the controller with experiments using our autonomous underwater robot AMOUR. We achieve roll errors of 1.0 degree (2.1 degrees standard deviation) and pitch errors of 1.5 degrees (1.8 degrees standard deviation). We also demonstrate experimentally that the controller can handle both nonholonomic and fully holonomic thruster configurations of the robot. In the later case we show how depth can be maintained while performing 360 degree rolls. Further, we demonstrate an input device that allows a user to control the robot's attitude while moving along a desired trajectory.

In this paper we develop algorithms and hardware for the autonomous gathering of cattle. We present a comparison of three different autonomous gathering algorithms that employ sound and/or electric stimuli to guide the cattle. We evaluate these algorithms in simulation by extending previous behavioral simulations for cattle. We implemented one of these algorithms and present data from experiments in which cattle were equipped with sensor nodes that allowed cueing with sound and electric stimuli. We discuss the minimum requirements for algorithms and hardware for autonomous gathering.

This article describes AquaOptical, an underwater optical communication system. Three optical modems have been developed: a long-range system, a short-range system, and a hybrid system. We describe their hardware and software architectures and highlight trade-offs. We present pool and ocean experiments with each system. In clear water, AquaOptical achieved a data rate of 1.2 Mbit/s at distances up to 30 m. In water with visibility estimated at 3 m, AquaOptical achieved communication at data rates of 0.6 Mbit/s at distances up to 9 m.