A geometry-based analytic attitude estimation using a rate measurement and measurement of a single reference vector has been recently proposed. Because rigid body attitude estimation is a fundamentally nonlinear problem, the geometry-based method does not contain errors consequent to linearization approximations. A critical source of residual error in the geometric solution is on account of the noise and bias in the vector and rate measurements. A methodical perturbation analysis of the attitude estimate is performed in this paper that reveals the effects of measurement noise and bias, and provides means to compensate for, or filter out, such errors. Application of the filter and compensation provides better attitude estimation than a standard Extended Kalman filter using an optimal Kalman gain. The geometric method is first verified in experiments and then simulation results are provided that validate the better performance of the geometric attitude and bias estimator.

This paper presents a nonlinear, backstepping depth and pitch controller for a dual-bladder buoyancy engine actuated by gear pumps. Flow-rate feedback is obtained using a custom flow sensor comprised of a differential pressure sensor and a small, 3D-printed attachment. The controller is simulated using a model of the CephaloBot, our in-house developed autonomous underwater vehicle (AUV). Its depth control capability is also experimentally validated using a single-bladder buoyancy engine on-board a smaller-scale test cylinder. Lyapunov stability analysis shows global, asymptotic stability, which is exhibited in our simulation. Our experiments verify that this buoyancy engine is a feasible and effective depth controller for AUVs.

We present the concept of concurrent flow-based localization and mapping (FLAM) for autonomous field robots navigating within background flows. Different from the classical simultaneous localization and mapping (SLAM) problem, where the robot interacts with discrete features, FLAM utilizes the continuous flow fields as navigation references for mobile robots and provides flow field mapping capability with in-situ flow velocity observations. This approach is of importance to underwater vehicles in mid-depth oceans or aerial vehicles in GPS-denied atmospheric circulations. This article introduces the formulation of FLAM as a full SLAM solution motivated by the feature-based GraphSLAM framework. The performance of FLAM was demonstrated through simulation within artificial flow fields that represent typical geophysical circulation phenomena: a steady single-gyre flow field and a double-gyre flow field with unsteady turbulent perturbations. The results indicate that FLAM provides significant improvements in the robots' localization accuracy and a consistent approximation of the background flow field. It is also shown that FLAM leads to smooth robot trajectory estimates.

The lateral line sensory system found in fish and other aquatic organisms is believed to aid in various behavioral activities such as station keeping and wall detection. This paper presents results for an artificial lateral line designed for use in an underwater vehicle. Algorithms for leveraging this system to estimate hydrodynamic forces and for wall detection are presented and experimentally validated. Three sensor setups were built, two with fixed sensor positions and one with a novel, modular sensor block approach. The modular realization allows for flexibility in testing and can be integrated into the control system of the autonomous underwater vehicle built by our group. These lateral line sensory systems were experimentally validated and the results are presented.

Navigation for autonomous underwater vehicles (AUVs) in mid-depth water column is notoriously challenging due to the lack of features for reference. In this paper, we propose a flow-aided cooperative navigation (FACON) strategy to improve the navigation performance of a team of AUVs when neither frequent surfacing nor persistent bottom-locking is available. Preloaded ocean current forecasts are referenced by each individual vehicle as it performs dead-reckoning with an inertial navigation system and measures relative current velocities during navigation. A marginalized particle filter is applied by each AUV to track its location, velocity, sensor biases, and local flow perturbation unresolved by the ocean forecast. Meanwhile, AUVs perform distributed cooperative localization based on relative pose measurements and asynchronized local communication when they enter the communication range of one another. Opportunistic information fusion among AUVs is realized through covariance intersection. The performance of the flow-aided navigation scheme of a single AUV is analyzed within a simulated experiment based on field test data. The feasibility of FACON is discussed through simulation in a turbulent, multi-gyre flow field.

A global localization method for autonomous underwater vehicles (AUVs) based on background flow velocity map prediction is proposed. Background flow velocity maps with time stamps are assumed to be predicted by a regional ocean model with certain accuracy and these maps are preloaded onto vehicles. Vehicles measure (estimate) general absolute flow velocities at their locations through local sensing. Particle filters are implemented to estimate AUVs' locations by comparing local flow velocity measurements with flow velocity prediction. The proposed algorithm is path independent and is suitable for any non-uniform vector fields. Convergence of localization error is achieved in simulation tests in a time dependent benchmark flow field, namely the double-gyre flow field. A two-dimensional vector field interpolation scheme based on irregularly-spaced data points is implemented to estimate theoretical flow velocities at vehicles' locations. It is shown that interpolation error can be treated as low-level map noise, which has limited impact on convergence. Localization error converges in simulation tests with different levels of map noise, which we believe to be dominant in practice comparing to measurement noise and vehicle control noise.

Considerable evidence suggests that the lateral line system serves an important role in fish-like locomotion by providing hydrodynamic information about the surrounding fluid. In this paper, a fish-like locomotion model is developed in an ideal fluid with a background flow estimation algorithm inspired by the lateral line system. Specifically, the fish model is geometrically formulated with the Joukowski transformation and is dynamically defined as a deformable solid body. Describing the fluid-body interactions is a hydrodynamic model that incorporates the potential flow theory with a shedding mechanism of discrete vortices. With the hydrodynamic information on the boundary of the fish-like profile, the background flow field can be estimated with a modified vortex panel method. Base on the estimation, the control decision can potentially be adjusted accordingly, which may improve the swimming performance. Furthermore, in the profile model, body segments are defined such that the internal forces can be easily obtained for quantitative efficiency evaluation.

This article describes and validates a data-driven cooperative feedback control system for mobile sensors. The system can be used to guide resource constrained mobile sensors through a dynamically changing environment in order to obtain a path that results in data collection at important locations in the domain. A simulated chemical puff is used as a test application where small, resource constrained aerial vehicles provide mobile sensing capabilities. A fluid-based control scheme is used to guide the mobile sensors through the domain to collect data on the puff. The data is then used to update and improve a model of the puff concentration. Simulations are provided, demonstrating the decrease in error between the simulated and actual puffs over time. Additionally, the effect of using different numbers of mobile sensors as well as different schemes for guiding the mobile sensors is investigated. The feasibility of the technique using real sensors is demonstrated experimentally using a single UAV tracking a simulated puff.

This paper examines a new type of thruster which, similar to squid and jellyfish, generates propulsive forces by cyclically ingesting and expelling jets of water. The thrust output of this device depends greatly on the nature of the expelled jet, with greater output occurring for jets which form a single coherent vortex ring rather than a vortex ring with a trailing wake. The average thrust output as well as the jetting efficiency are maximized with respect to the thruster's nozzle diameter and jet velocity program. The appropriate constraints of the problem are defined including a dynamic constraint to ensure that the propulsive jet forms a single vortex ring. It is shown that the average thrust output under these constraints reaches a maximum when the nozzle radius is set to a critical value, and that using an orifice nozzle decreases propulsive efficiency at low Reynolds numbers.

Using a multi-vehicle control scheme based on smoothed particle hydrodynamics, a simulated swarm of unmanned underwater vehicles is guided along a pre-computed optimal trajectory between two points in an open domain under the influence of a strong background flow. The pre-computed trajectory is optimal in terms of fuel usage for a single vehicle. If the gradient of the velocity field is small compared to the total swarm radius, guiding a swarm of vehicles along this trajectory gives nearly optimal trajectories for all vehicles in the swarm without requiring additional costly optimization computations. We provide a bound on the maximum energy cost for vehicles in the swarm and also provide a more realistic estimate of the maximum energy cost. We also determine that the energy cost scales as N3/2for swarms with large numbers of vehicles, N. The algorithm and fuel cost bounds are verified in simulations of unmanned underwater vehicles moving across a double gyre system on the scale of a small ocean basin.

The effect of ocean flow on the motion of autonomous underwater vehicles (AUV) is often crucial in the development of underwater localization algorithms and should not be treated as small disturbances. The domination of strong ocean currents and the requirement of low power consumption prohibit AUVs to move against a background flow to obtain localization correction in a timely manner. Our recent studies, among others, enable an unmanned vehicle to follow a near optimal trajectory found by Lagrangian coherent structures based fluid control algorithm with minimal fuel usage, which improves the vehicles' runtime and the path following accuracy in the presence of strong background flow. Here, we propose a three-dimensional fully distributed localization hierarchy to improve the localization of low-cost mobile marine data collection underwater sensor networks using intra-vehicle communication and measurements. The proposed algorithm is realized by using the extended Kalman filter. Correlation terms in covariance matrices are considered independently to meet the distributed feature. Resulting simulated localization errors are bounded at satisfactory levels and the relationship between the number of AUVs and the performance of the algorithm is investigated.

Studies have shown that the lateral line found in most fish and some other aquatic organisms is capable of providing hydrodynamic information of the surrounding fluid, which may facilitate many behavioral decisions. Previous work by the group introduced a lateral line inspired feedforward design for underwater vehicle control. The system utilizes pressure sensor arrays to estimate the hydrodynamic force acting on the vehicle such that the additional information will simplify the modeling process and improve the maneuvering accuracy for the control tasks in underwater exploration and environmental monitoring. In this paper, the feedforward control design is presented and tested in simulation for trajectory tracking and path following after expressing the force estimation algorithm in the three-dimensional domain. Pressure measurements at multiple locations on the vehicle surface form a least squares approximation of the pressure distribution. Hydrodynamic forces acting on the vehicle are then estimated and passed to the controller for improved performance. Preliminary experimental tests are conducted to vindicate the proposed algorithm.

A control algorithm based on Smoothed Particle Hydrodynamics (SPH) is used to guide a simulated swarm of unmanned vehicles along a pre-computed optimal trajectory. By using a fluid based control method, collision avoidance is automatically incorporated into the control. Given an initial position and goal location, it is computationally impractical to compute the optimal trajectories for a large swarm of vehicles. To overcome this, the optimal trajectory is computed for a single vehicle and the swarm is guided along this trajectory using fluid based control, in this case SPH. Importantly, this method avoids computing a potential field to guide the swarm and is easily adaptable to changing environments. Additionally, this method is intended to be used in the presence of a background flow through which the vehicles must travel. This control algorithm is found to be quite robust in the test case examined here and adding additional vehicles to the swarm has only a small impact on fuel consumption per vehicle.

A new type of underwater thruster was designed to provide high-accuracy, low-speed maneuvering to underwater robots. Located internal to the vehicle surface, these thrusters have a minimal effect on the forward-drag profile of the vehicle. These thrusters, whose inspiration comes from the natural propulsion of cephalopods and jellyfish, generate control forces by successive ingestion and expulsion of jets of water from a cavity mounted in the hull of the vehicle. The jetting process has no net mass flux but results in a positive momentum flux. A time-dependent thrust model was developed, which predicted the thruster dynamics as a function of time, actuation frequency, and thruster-driving parameters. A linear transfer-function model was developed to approximate both the thruster and vehicle dynamics, which led to maneuver categorization into three regimes:Cruising,Docking, andTransition. The predicted frequency response was verified through hybrid simulation to be accurate for predicting general trends and cutoff frequency.

A new type of underwater thruster is studied which is inspired by the natural locomotion of squid and other cephalopod. The thruster is similar to synthetic jet actuators used for flow control studies, but operates on different length and frequency scales. A hybrid simulation experiment has been designed to test the performance of this thruster in a static environment, under dynamic, unsteady driving signals. The hybrid simulation implements actual thrust production on a virtual vehicle model and attempts to control the trajectory of that vehicle. It was determined that this type of thruster being controlled with a basic PD control algorithm demonstrates excellent position tracking in a wide range of maneuvers. The thruster showed that it could produce controlling forces accurately on several scales of thrust. However, the maneuvering system was subject to an appreciable position overshoot at high vehicle oscillation frequencies, due to unmodeled thruster settling time.

Similar to propulsion techniques employed by squid and other cephalopod, a new type of thruster was designed which utilized pulsatile jet propulsion to generate controlling forces. The thrust production from this device was characterized in a static environment and seen to be well approximated by a simple fluid slug model. A linear transfer function model was derived to describe the transient dynamics of this thruster and a virtual vehicle simulation was developed to test the thruster with unsteady driving signals to verify this model. Due to the extremely non-linear nature of underwater vehicle environments we developed a scaling system to classify regimes of maneuvers and characterize their dynamics independently. Within the hybrid simulation environment, using a simple proportional derivative control algorithm, a thruster/controller was designed and observed to provide sufficient control over the vehicle performing large scale maneuvers. However, some additional compensation may be required for accurate small scale maneuvers which require minimal overshoot.

An airborne wireless sensor network (WSN) composed of bird-sized micro aerial vehicles (MAVs) enables low cost high granularity atmospheric sensing of toxic plume behavior and storm dynamics, and provides a unique three-dimensional vantage for monitoring wildlife and ecological systems. This paper describes a complete implementation of our SensorFlock airborne WSN, spanning the development of our MAV airplane, its avionics, semi-autonomous flight control software, launch system, flock control algorithm, and wireless communication networking between MAVs. We present experimental results from flight tests of flocks of MAVs, and a characterization of wireless RF behavior in air-to-air communication as well as air-to-ground communication.