Van T. Huynh

Controlling Buoyancy-Driven Profiling Floats for Applications in Ocean Observation

Establishing a persistent presence in the ocean with an autonomous underwater vehicle (AUV) capable of observing temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we examine the utility of vehicles that can only control their depth in the water column for such extended deployments. We present a strategy that utilizes ocean model predictions to facilitate a basic level of autonomy and enables general control for these profiling floats. The proposed method is based on experimentally validated techniques for utilizing ocean current models to control autonomous gliders. With the appropriate vertical actuation, and utilizing spatio-temporal variations in water speed and direction, we show that general controllability results can be met. First, we apply an A* planner to a local controllability map generated from predictions of ocean currents. This computes a path between start and goal waypoints that has the highest likelihood of successful execution. A computed depth plan is generated with a model-predictive controller (MPC), and selects the depths for the vehicle so that ambient currents guide it toward the goal. Mission constraints are included to simulate and motivate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA, USA, that show encouraging results in the ability of a drifting vehicle to reach a desired location.

Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments

In this paper, we present a monocular vision based autonomous navigation system for Micro Aerial Vehicles (MAVs) in GPS-denied environments. The major drawback of monocular systems is that the depth scale of the scene can not be determined without prior knowledge or other sensors. To address this problem, we minimize a cost function consisting of a drift-free altitude measurement and up-to-scale position estimate obtained using the visual sensor. We evaluate the scale estimator, state estimator and controller performance by comparing with ground truth data acquired using a motion capture system. All resources including source code, tutorial documentation and system models are available online4.

Derivation of an error model for tractor-trailer path tracking

Developing a specific model for a tractor-trailer system subject to slips in real working environment is essential for understanding its system behavior and designing controllers. This paper presents a comprehensive analysis of the kinematics of the tractor-trailer under the influence of disturbances caused by the ground onto the implements and wheels. An error model is then derived, based on the open-loop kinematic model and the desired reference path. By applying basic theory of engineering mechanics for rigid body with slips, in the form of extra velocities and angles, exerting on rear axles and steered wheel as well as the trailers implements, the open-loop kinematic model is primarily established. Provided the current state of the vehicle and random disturbances of the environment are known with bounds, the next states of the system can be estimated, the vehicle running path is predicted, along with setting a basis for further controller design. The development is verified by simulation for both kinematic and error models with disturbances obeying the normal distribution.

Predictive motion planning for AUVs subject to strong time-varying currents and forecasting uncertainties

This paper presents a novel path planning method for minimizing the energy consumption of an autonomous underwater vehicle subjected to time varying ocean disturbances and forecast model uncertainty. The algorithm determines 4-Dimensional path candidates using Nonlinear Robust Model Predictive Control (NRMPC) and solutions optimised using A*-like algorithms. Vehicle performance limits are incorporated into the algorithm with disturbances represented as spatial and temporally varying ocean currents with a bounded uncertainty in their predictions. The proposed algorithm is demonstrated through simulations using a 4-Dimensional, spatially distributed time-series predictive ocean current model. Results show the combined NRMPC and A* approach is capable of generating energy-efficient paths which are resistant to both dynamic disturbances and ocean model uncertainty.

A nonlinear PI and backstepping-based controller for tractor-steerable trailers influenced by slip

Autonomous guidance of agricultural vehicles is vital as mechanized farming production becomes more prevalent. It is crucial that tractor-trailers are guided with accuracy in both lateral and longitudinal directions, whilst being affected by large disturbance forces, or slips, owing to uncertain and undulating terrain. Previous research has been concentrated on trajectory control that provides longitudinal and lateral accuracy if the vehicle moves without sliding and/or the trailer is passive. In this paper, we extend current results by addressing the problem of robust trajectory tracking along straight and circular paths of a tractors with steerable trailers. We develop a controller that is a robust combination of a backstepping and nonlinear PI control. For vehicles subjected to slip, the proposed controller makes the lateral deviations and the orientation errors of the tractor and trailer converge to a neighborhood near the origin. Simulation results are presented to illustrate that the suggested controller ensures precise trajectory tracking in the presence of slip.

Controlling Minimally-Actuated Vehicles for Applications in Ocean Observation

Establishing a persistent presence in the ocean with an AUV to observe temporal variability of large-scale ocean processes requires a unique sensor platform. In this paper, we propose a strategy that utilizes ocean model predictions to increase the autonomy and control of Lagrangian or profiling floats for precisely this purpose. An A* planner is applied to a local controllability map generated from predictions of ocean currents to compute a path between prescribed waypoints that has the highest likelihood of successful execution. The control to follow the planned path is computed by use of a model predictive controller. This controller is designed to select the best depth for the vehicle to exploit ambient currents to reach the goal waypoint. Mission constraints are employed to simulate a practical data collection mission. Results are presented in simulation for a mission off the coast of Los Angeles, CA USA, and show surprising results in the ability of a Lagrangian float to reach a desired location.

Eigenvalue assignment for positive observers and its feasibility

Abstract In this paper, we discuss a new problem of establishing feasibility conditions for regional eigenvalue assignment of positive observers. Previous results on regional eigenvalue assignment for observers of linear time-invariant positive systems are also improved. Unlike observable dynamical systems whose closed-loop eigenvalues can be assigned arbitrarily, eigenvalues of positive observers are indicated unable to be assigned into any arbitrary region. We derive feasibility conditions in the form of constrained convex programming under which the regional eigenvalue assignment is possible. Moreover, we propose a new method for solving the regional eigenvalue problem of positive observers once the feasibility conditions are satisfied. Numerical examples are given to show the efficacy of the proposed method.

Design and development of a low-cost Autonomous Surface Vehicle

Marine robotics is a rapidly growing field, with applications of both Autonomous Underwater Vehicles (AUV) and Autonomous Surface Vehicles (ASV) becoming extensive and within reach for many people. Presented is a low-cost design for an ASV, focusing on the ability for the average person with only little mechanical and electrical skills to assemble. The ASV also incorporates a winch into its design, allowing the ASV to perform many tasks that make it stand out from other ASV systems available in the market, A novel system containing both an ASV and an AUV is introduced, where the designed ASV would be able to work with AUV systems to find and collect underwater objects.