Towards improving mission execution for autonomous gliders with an ocean model and Kalman filter

In this paper, we examine the use of a Kalman filter to aid in the mission planning process for autonomous gliders. Given a set of waypoints defining the planned mission and a prediction of the ocean currents from a regional ocean model, we present an approach to determine the best, constant, time interval at which the glider should surface to maintain a prescribed tracking error, and minimizing time on the ocean surface. We assume basic parameters for the execution of a given mission, and provide the results of the Kalman filter mission planning approach. These results are compared with previous executions of the given mission scenario.

Bi-objective bundle adjustment: towards large-scale visual sea-floor mapping with a minimal sensor suite

Visual sea-floor mapping is a rapidly growing application for Autonomous Underwater Vehicles (AUVs). AUVs are well-suited to the task as they remove humans from a potentially dangerous environment, can reach depths human divers cannot, and are capable of long-term operation in adverse conditions. The output of sea-floor maps generated by AUVs has a number of applications in scientific monitoring: from classifying coral in high biological value sites to surveying sea sponges to evaluate marine environment health.

Visual sea-floor mapping from low overlap imagery using bi-objective bundle adjustment and constrained motion

In most visual mapping applications suited to Autonomous Underwater Vehicles (AUVs), stereo visual odometry (VO) is rarely utilised as a pose estimator as imagery is typically of very low framerate due to energy conservation and data storage requirements. This adversely affects the robustness of a vision-based pose estimator and its ability to generate a smooth trajectory. This paper presents a novel VO pipeline for low-overlap imagery from an AUV that utilises constrained motion and integrates magnetometer data in a bi-objective bundle adjustment stage to achieve low-drift pose estimates over large trajectories. We analyse the performance of a standard stereo VO algorithm and compare the results to the modified vo algorithm. Results are demonstrated in a virtual environment in addition to low-overlap imagery gathered from an AUV. The modified VO algorithm shows significantly improved pose accuracy and performance over trajectories of more than 300m. In addition, dense 3D meshes generated from the visual odometry pipeline are presented as a qualitative output of the solution.

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.

Kelp detection in highly dynamic environments using texture recognition

This paper describes a texture recognition based method for segmenting kelp from images collected in highly dynamic shallow water environments by an Autonomous Underwater Vehicle (AUV). A particular challenge is image quality that is affected by uncontrolled lighting, reduced visibility, significantly varying perspective due to platform egomotion, and kelp sway from wave action. The kelp segmentation approach uses the Mahalanobis distance as a way to classify Haralick texture features from sub-regions within an image. The results illustrate the applicability of the method to classify kelp allowing construction of probability maps of kelp masses across a sequence of images.

Integrating field survey data with satellite image data to improve shallow water seagrass maps : the role of AUV and snorkeller surveys

Repeatable and accurate seagrass mapping is required for understanding seagrass ecology and supporting management decisions. For shallow (< 5 m) seagrass habitats, these maps can be created by integrating high spatial resolution imagery with field survey data. Field survey data for seagrass is often collected via snorkelling or diving. However, these methods are limited by environmental and safety considerations. Autonomous Underwater Vehicles (AUVs) are used increasingly to collect field data for habitat mapping, albeit mostly in deeper waters (>20 m). Here we demonstrate and evaluate the use and potential advantages of AUV field data collection for calibration and validation of seagrass habitat mapping of shallow waters (< 5 m), from multispectral satellite imagery. The study was conducted in the seagrass habitats of the Eastern Banks (142 km2), Moreton Bay, Australia. In the field, georeferenced photos of the seagrass were collected along transects via snorkelling or an AUV. Photos from both collection methods were analysed manually for seagrass species composition and then used as calibration and validation data to map seagrass using an established semi-automated object based mapping routine. A comparison of the relative advantages and disadvantages of AUV and snorkeller collected field data sets and their influence on the mapping routine was conducted. AUV data collection was more consistent, repeatable and safer in comparison to snorkeller transects. Inclusion of deeper water AUV data resulted in mapping of a larger extent of seagrass (~7 km2, 5 % of study area) in the deeper waters of the site. Although overall map accuracies did not differ considerably, inclusion of the AUV data from deeper water transects corrected errors in seagrass mapped at depths to 5 m, but where the bottom is visible on satellite imagery. Our results demonstrate that further development of AUV technology is justified for the monitoring of seagrass habitats in ongoing management programs.

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 novel approach to 6-DOF adaptive trajectory tracking control of an AUV in the presence of parameter uncertainties

In this paper, the trajectory tracking control of an autonomous underwater vehicle (AUVs) in six-degrees-of-freedom (6-DOFs) is addressed. It is assumed that the system parameters are unknown and the vehicle is underactuated. An adaptive controller is proposed, based on Lyapunov׳s direct method and the back-stepping technique, which interestingly guarantees robustness against parameter uncertainties. The desired trajectory can be any sufficiently smooth bounded curve parameterized by time even if consist of straight line. In contrast with the majority of research in this field, the likelihood of actuators׳ saturation is considered and another adaptive controller is designed to overcome this problem, in which control signals are bounded using saturation functions. The nonlinear adaptive control scheme yields asymptotic convergence of the vehicle to the reference trajectory, in the presence of parametric uncertainties. The stability of the presented control laws is proved in the sense of Lyapunov theory and Barbalat׳s lemma. Efficiency of presented controller using saturation functions is verified through comparing numerical simulations of both controllers.

A shallow water AUV for benthic and water column observations

Autonomous underwater vehicles (AUVs) are becoming commonplace in the study of inshore coastal marine habitats. Combined with shipboard systems, scientists are able to make in-situ measurements of water column and benthic properties. In CSIRO, autonomous gliders are used to collect water column data, while surface vessels are used to collect bathymetry information through the use of swath mapping, bottom grabs, and towed video systems. Although these methods have provided good data coverage for coastal and deep waters beyond 50m, there has been an increasing need for autonomous in-situ sampling in waters less than 50m deep. In addition, the collection of benthic and water column data has been conducted separately, requiring extensive post-processing to combine data streams. As such, a new AUV was developed for in-situ observations of both benthic habitat and water column properties in shallow waters. This paper provides an overview of the Starbug X AUV system, its operational characteristics including vision-based navigation and oceanographic sensor integration.

基于视觉伺服的水下机器人悬停定位

为实现水下机器人的悬停定位,提出一种基于视觉伺服的方法．通过安装在机器人上的摄像机实时摄取特定观察目标的图像,应用基于单目视觉位姿估计方法获取观察目标相对干摄像机的位姿信息,并以此作为反馈构成机器人运动的伺服控制．以本单位的水下机器人控制系统实验研究平台为载体,在实验水池内完成了实验．实验结果表明,在存在外力扰动的情况下机器人可以运动到指定位姿并保持悬停．

小型自治水下机器人运动控制系统研究

以主推加舵控制的小型自治水下机器人为研究对象,建立了水下机器人的数学模型并进行了分析。根据机器人结构的特点,对模型进行了必要的简化。设计了机器人的运动控制系统。通过湖试验正控制器的性能。

分散式控制和管理的UUV体系结构模型研究

针对传统的分层式体系结构在UUV 控制系统开发设计、维护和升级过程中所遇到的困难，首先分析了集中式控制和管理方法的不足和原因。在此基础上，借鉴自主计算的思想，提出了分散控制和管理的体系结构。这种方法把系统中每个独立的功能模块都抽象成为具有相同模型的控制基元，称之为“自主基元”，然后通过自主基元层次式、嵌套式的组织，构成完整的系统。分析表明，这种方法能够缩短控制系统的开发周期，也降低了维护和升级的难度。

估计从UUV航行参数的混合坐标系下的EKF算法

在主从式UUV 协作系统中，由于定位和导航的需要，要求尽快估计出从UUV 的航行参数，但通常所用的递推最小二乘（RLS）算法，其初始方位测量对滤波结果影响大且存在收敛速度慢、计算精度低的缺点，难以满足应用需求，而推广卡尔曼滤波（EKF）算法能较好地克服上述问题。在直角坐标系下（CEKF），方位信息与距离信息相互耦合导致初始振荡剧烈，改为混合坐标系（MEKF）后问题得到了极大的改善。最后，通过仿真及现场试验验证了此改进方法的有效性。

一种欠驱动AUV的三维实时避碰方法

针对以测距声纳为避碰传感器的一类欠驱动型AUV，提出了一种水平面和垂直面相结合的三维实时避碰方法。根据测距声纳和欠驱动AUV 的特殊性，首先从运动规划和路径规划2 个层次提出了AUV 混合型实时避碰结构，并分别设计了基于事件反馈监控的避碰自动机和基于免疫遗传的局部路径规划算法。多种典型障碍场景的半物理仿真实验表明，论文所提方法能够实现AUV 安全、稳定的三维避碰过程。

基于关节限位的自治水下机器人机械手系统运动规划研究

对自治水下机器人搭载的四功能水下电动机械手进行了简要描述。考虑到自治水下机器人机械手系统的运动学冗余,将关节限位算法用于系统逆运动学求解,避免载体大幅度姿态变化。利用Matlab仿真表明该算法在解决系统冗余的同时有效的限制了关节位移。