Andrew Tinka

Autonomous Searching and Tracking of a River using an UAV

Surveillance operations include inspecting and monitoring river boundaries, bridges and coastlines. An autonomous unmanned aerial vehicle (UAV) can decrease the operational costs, expedite the monitoring process and be used in situations where a manned inspection is not possible. This paper addresses the problem of searching and mapping such littoral boundaries using an autonomous UAV based on visual feedback. Specifically, this paper describes an exploration system that equips a fixed wing UAV to autonomously search a given area for a specified structure (could be a river, a coastal line etc.), identify the structure if present and map the coordinates of the structure based on the images from the onboard sensor(could be vision or near infra-red). Experimental results with a fixed wing UAV searching and mapping the coordinates of a 2 mile stretch of a river with a cross track error of around 9 meters are presented.

Ensemble Kalman Filter based state estimation in 2D shallow water equations using Lagrangian sensing and state augmentation

We present a state estimation method for two-dimensional shallow water equations in rivers using Lagrangian drifter positions as measurements. The aim of this method is to compensate for the lack of knowledge of upstream and downstream boundary conditions in rivers that causes inaccuracy in the velocity field estimation by releasing drifters equipped with GPS receivers. The drifters report their positions and thus provide additional information of the state of the river. This information is incorporated into shallow water equations by using Ensemble Kalman Filtering (EnKF). The proposed method is based on the discretization of the governing nonlinear equations using the finite element method in unstructured meshes. We incorporate the drifter positions into the unknown state, which directly exploits the Langrangian nature of the measurements. The performance of the method is assessed with twin experiments.

Quadratic programming based data assimilation with passive drifting sensors for shallow water flows

We present a method for assimilating Lagrangian sensor measurement data into a shallow water equation model. The underlying estimation problem (in which the dynamics of the system are represented by a system of partial differential equations) relies on the formulation of a minimisation of an error functional, which represents the mismatch between the estimate and the measurements. The corresponding so-called variational data assimilation problem is formulated as a quadratic programming problem with linear constraints. For the hydrodynamics application of interest, data is obtained from drifting sensors that gather position and velocity. The data assimilation method refines the estimate of the initial conditions of the hydrodynamic system. The method is implemented using a new sensor network hardware platform for gathering flow information from a river, which is presented in this article for the first time. Validation of the results is performed by comparing them to an estimate derived from an independent set of static sensors, some of which were deployed as part of our field experiments.

Floating Sensor Networks for River Studies

Free-floating sensor packages that take local measurements and track flows in water systems, known as drifters, are a standard tool in oceanography, but are new to estuarial and riverine studies. A system based on drifters for making estimates on a hydrodynamic system requires the drifters themselves, a communication network, and a method for integrating the gathered data into an estimate of the state of the hydrodynamics. This paper presents a complete drifter system and documents a pilot experiment in a controlled channel. The utility of the system for making measurements in unknown environments is highlighted by a combined parameter estimation and data assimilation algorithm using an extended Kalman filter. The performance of the system is illustrated with field data collected at the Hydraulic Engineering Research Unit, Stillwater, OK.

Combined state-parameter estimation for shallow water equations

In this article, a method for assimilating data into the shallow water equations when some of the model parameters are unknown is presented. The one dimensional Saint-Venant equations are used as a model of water flow in open channels. Using these equations, a nonlinear state space model is obtained. Lagrangian measurements of the flow velocity field are used as observations or measurements. These measurements may be obtained from a group of drifters equipped with GPS receivers and communication capabilities which move with the flow and report their position at every time step. Using the derived state-space model, the extended Kalman filter is used to estimate the state and the unknown model parameters given the latest measurements. The performance of the method is evaluated using data collected from an experiment performed at the USDA-ARS Hydraulic Engineering Research Unit (HERU) in Stillwater, Oklahoma in November 2009.

Design of a network of robotic Lagrangian sensors for shallow water environments with case studies for multiple applications

This article describes the design methodology for a network of robotic Lagrangian floating sensors designed to perform real-time monitoring of water flow, environmental parameters, and bathymetry of shallow water environments (bays, estuarine, and riverine environments). Unlike previous Lagrangian sensors which passively monitor water velocity, the sensors described in this article can actively control their trajectory on the surface of the water and are capable of inter-sensor communication. The addition of these functionalities enables Lagrangian sensing in obstacle-encumbered environments, such as rivers. The Ishikawa cause and effect design framework is used to ensure that the final system synthesizes the diverse operational and functional needs of multiple end-user groups to arrive at a broadly applicable system design. A summary of potential applications for the system is given including completed projects performed on behalf of the Department of Homeland Security, Office of Naval Research, and the California Bay-Delta Authority.

Quadratic Programming based data assimilation with passive drifting sensors for shallow water flows

We present a method for assimilating Lagrangian sensor measurement data into a Shallow Water Equation model. Using our method, the variational data assimilation problem is formulated as a Quadratic Programming problem with linear constraints. Drifting sensors that gather position and velocity information in the modeled system can then be used to refine the estimate of the initial conditions of the system. A new sensor network hardware platform for gathering flow information is presented. We summarize the results of a field experiment designed to demonstrate the capabilities of our assimilation method with data gathered from the sensors. Validation of the results is performed by comparing them to an estimate derived from an independent set of static sensors.

Autonomous River Navigation Using the Hamilton–Jacobi Framework for Underactuated Vehicles

Motorized floating sensors have distinct advantages over their non-actuated counterparts. A motorized unit can prevent the sensor from washing ashore or heading into dangerous areas, expanding the mission regions in which they can be feasibly operated. In this article, we present a control framework and describe the physically realized system used to prove its effectiveness. The controller uses two minimum-time-to-reach (MTTR) functions—one giving the time to reach the center of the river and one giving the time to reach the shoreline. The MTTR functions are constructed from solutions to Hamilton-Jacobi-Bellman-Isaacs (HJBI) Equations. Contours along these functions are used to define the state transition thresholds for an on-off controller. The first MTTR function is also used to construct the optimal bearing to travel back to the center of the river. We investigate the effectiveness of the controller using a software-in-the-loop (SIL) simulator. Using prototypes built at UC Berkeley, results from a field operational test in the Sacramento-San Joaquin River Delta are then presented to validate the simulation results.

A decentralized scheduling algorithm for time synchronized channel hopping

Time Synchronized Channel Hopping (TSCH) is an existing Medium Access Control scheme which enables robust communication through channel hopping and high data rates through synchronization. It is based on a time-slotted architecture, and its correct functioning depends on a schedule which is typically computed by a central node. This paper presents, to our knowledge, the first scheduling algorithm for TSCH networks which both is distributed and which copes with mobile nodes. Two variations on scheduling algorithms are presented. Aloha-based scheduling allocates one channel for broadcasting advertisements for new neighbors. Reservationbased scheduling augments Aloha-based scheduling with a dedicated timeslot for targeted advertisements based on gossip information. A mobile ad hoc motorized sensor network with frequent connectivity changes is studied, and the performance of the two proposed algorithms is assessed. This performance analysis uses both simulation results and the results of a field deployment of floating wireless sensors in an estuarial canal environment. Reservation-based scheduling performs significantly better than Aloha-based scheduling, suggesting that the improved network reactivity is worth the increased algorithmic complexity and resource consumption.

Mobile phone based drifting lagrangian flow sensors

Mobile phone based drifters offer distinct advantages over those using custom electronic circuit boards. They leverage the inexpensive and modern hardware provided by the mobile phone market to supply water resource scientists with a new solution to sensing water resources. Mobile phone based drifters strategically address in situ sensing applications in order to focus on the large scale use of mobile phones dealing with communications, software, hardware, and system reliability. We have demonstrated that a simple design of a drifter built around an Android phone robustly survives many hours of experimental usage. In addition to the positioning capabilities of the phone via GPS, we also use the accelerometer of the phone to filter out samples when the drifter is in storage. The success of these drifters as passive mobile phone sensors has also led us to develop motorized mobile phone drifters.