Navigation & control of an automated SWATH surface vessel for bathymetric mapping

With the abundant amount of water on the earth, the study of underwater terrain plays an important role in the use and sustainability of marine resources. A wide variety of technical systems are used to collect such bathymetric data, and autonomous vehicles are being explored as a manner in which to make this process more cost-effective. Students in Santa Clara University\u27s Robotic Systems Laboratory are contributing to this effort through the development of an autonomous SWATH boat that can create such maps. As part of this thesis work, the navigation and control system of this SWATH boat has been significantly enhanced. This includes refinement of a path-following controller, development of a trajectory controller that sequences desired paths in a Mow-the-Lawn pattern, and the creation of planning tools to generate trajectories based on high-level descriptions of regions to be mapped. This work included exploration of feed-forward disturbance controllers to improve rejection of wind and current disturbances, and it also included a canard control system to trim vehicle pitch in order to increase mapping speeds. These improvements were experimentally demonstrated in multiple field deployments, and the SWATH vehicle is now capable of providing science-quality bathymetric maps

Tilt compensated mechanical measurement mechanism for very shallow water USV bathymetry

Currently researchers are advancing unmanned surface vehicle (USV) for bathymetry and hydrography applications. The idea of USV was introduced to avoid the risk and danger of personnel on the water terrain. When it comes to shallow water bathymetry mapping most method has the limitation to measure terrain which less than 1 meter deep. Commonly USV uses sonar depth sensor to measure the water depth yet it has a minimum range which it can measure. In this paper, the USV has been equipped with an additional mechanical bar measurer to be used on the very shallow part of the water bodies. By lowering and measuring the angle of the bar respect to the boat’s draft, the depth of the very shallow water can be obtained. The cases of the rocking of the boat are also being considered by measuring the roll and pitch and implement forward kinematic method for depth correction

MARV: Marine Autonomous Research Vessel

Conducting hydrologic research in remote areas is currently performed manually, making it a labor intensive and inaccurate solution. Due to the size, weight, and cost of automated solutions on the market today, a need has arisen for a low cost, highly portable, autonomous solution. Working closely with Santa Clara University’s Robotics Systems Lab (RSL), our team has developed a low cost, highly portable autonomous marine research vessel named MARV (Marine Autonomous Research Vessel). It is an autonomous surface platform where scientists outfit the vessel with their own data acquisition equipment. The mechanical chassis is collapsible for modes of remote transportation (i.e. helicopter, small trucks, backpacking). With a final weight of 25 kilograms, material cost of $4,482, and a cross track error of ±1 meter, we have successfully designed and manufactured low cost, highly portable autonomous solution. However, MARV does not operate on an adaptive navigation system. Further developments such as object avoidance and depth control would result in a fully autonomous marine platform

Robot-assisted measurement for hydrologic understanding in data sparse regions

This article describes the field application of small, low-cost robots for remote surface data collection and an automated workflow to support water balance computations and hydrologic understanding where water availability data is sparse. Current elevation measurement approaches, such as manual surveying and LiDAR, are costly and infrequent, leading to potential inefficiencies for quantifying the dynamic hydrologic storage capacity of the land surface over large areas. Experiments to evaluate a team of two different robots, including an unmanned aerial vehicle (UAV) and an unmanned surface vehicle (USV), to collect hydrologic surface data utilizing sonar and visual sensors were conducted at three different field sites within the Arkavathy Basin river network located near Bangalore in Karnataka, South India. Visual sensors were used on the UAV to capture high resolution imagery for topographic characterization, and sonar sensors were deployed on the USV to capture bathymetric readings; the data streams were fused in an automated workflow to determine the storage capacity of agricultural reservoirs (also known as “tanks”) at the three field sites. This study suggests: (i) this robot-assisted methodology is low-cost and suitable for novice users, and (ii) storage capacity data collected at previously unmapped locations revealed strong power-type relationships between surface area, stage, and storage volume, which can be incorporated into modeling of landscape-scale hydrology. This methodology is of importance to water researchers and practitioners because it produces local, high-resolution representations of bathymetry and topography and enables water balance computations at small-watershed scales, which offer insight into the present-day dynamics of a strongly human impacted watershed

Experiments in dynamic control of autonomous marine vehicles using acoustic modems

Marine robots are an increasingly attractive means for observing and monitoring in the ocean, but underwater acoustic communication (“acomms”) remains a major challenge, especially for real-time control. Packet loss occurs widely, bit rates are low, and there are significant delays. We consider here strategies for feedback control with acomms links in either the sensor-controller channel, or the controller-actuator channel. On the controller-actuator side we implement sparse packetized predictive control (S-PPC), which simultaneously addresses packet-loss and the data rate limit. For the sensor-controller channel we study a modified information filter (MIF) in a Linear Quadratic Gaussian (LQG) control scheme. Field experiments were carried out with both approaches, regulating crosstrack error in a robotic kayak using acomms. Outcomes with both the S-PPC and MIF LQG confirm that good performance is achievable.United States. Office of Naval Research (Grant N00014-09-1-0700)National Science Foundation (U.S.) (Contract CNS-1212597)Finmeccanic

Object Manipulation using a Multirobot Cluster with Force Sensing

This research explored object manipulation using multiple robots by developing a control system utilizing force sensing. Multirobot solutions provide advantages of redundancy, greater coverage, fault-tolerance, distributed sensing and actuation, and reconfigurability. In object manipulation, a variety of solutions have been explored with different robot types and numbers, control strategies, sensors, etc. This research involved the integration of force sensing with a centralized position control method of two robots (cluster control) and building it into an object level controller. This controller commands the robots to push the object based on the measured interaction forces between them while maintaining proper formation with respect to each other and the object. To test this controller, force sensor plates were attached to the front of the Pioneer 3-AT robots. The object is a long, thin, rectangular prism made of cardboard, filled with paper for weight. An Ultra Wideband system was used to track the positions and headings of the robots and object. Force sensing was integrated into the position cluster controller by decoupling robot commands, derived from position and force control loops. The result was a successful pair of experiments demonstrating controlled transportation of the object, validating the control architecture. The robots pushed the object to follow linear and circular trajectories. This research is an initial step toward a hybrid force/position control architecture with cluster control for object transportation by a multirobot system

An intelligent navigation system for an unmanned surface vehicle

Merged with duplicate record 10026.1/2768 on 27.03.2017 by CS (TIS)A multi-disciplinary research project has been carried out at the University of Plymouth to design and develop an Unmanned Surface Vehicle (USV) named ýpringer. The work presented herein relates to formulation of a robust, reliable, accurate and adaptable navigation system to enable opringei to undertake various environmental monitoring tasks. Synergistically, sensor mathematical modelling, fuzzy logic, Multi-Sensor Data Fusion (MSDF), Multi-Model Adaptive Estimation (MMAE), fault adaptive data acquisition and an user interface system are combined to enhance the robustness and fault tolerance of the onboard navigation system. This thesis not only provides a holistic framework but also a concourse of computational techniques in the design of a fault tolerant navigation system. One of the principle novelties of this research is the use of various fuzzy logic based MSDF algorithms to provide an adaptive heading angle under various fault situations for Springer. This algorithm adapts the process noise covariance matrix ( Q) and measurement noise covariance matrix (R) in order to address one of the disadvantages of Kalman filtering. This algorithm has been implemented in Spi-inger in real time and results demonstrate excellent robustness qualities. In addition to the fuzzy logic based MSDF, a unique MMAE algorithm has been proposed in order to provide an alternative approach to enhance the fault tolerance of the heading angles for Springer. To the author's knowledge, the work presented in this thesis suggests a novel way forward in the development of autonomous navigation system design and, therefore, it is considered that the work constitutes a contribution to knowledge in this area of study. Also, there are a number of ways in which the work presented in this thesis can be extended to many other challenging domains.DEVONPORT MANAGEMENT LTD, J&S MARINE LTD AND SOUTH WEST WATER PL

An enhanced time synchronization protocol in automated surface vehicles

1056-1069Mobility of an autonomous surface vehicle (ASV) has caused the wireless sensor and actuator networks (WSANs) onboard to be mobile as well. However, the time synchronization of a WSAN on ASV is more challenging due to the environmental harshness and node mobility. In this paper, an enhanced control-theoretic distributed time synchronization protocol called Time Synchronization using Distributed Observer algorithm with Sliding mode control element (TSDOS) is presented to solve the time synchronization issue in ASV. This TSDOS protocol feeds a sliding mode control element on the relative comparative error to estimate the skew rate and relative skew rate. Through the theoretical analysis and simulations, TSDOS has showed the advantages of totally distributed, robust to node failure and time-varying clock frequencies, which are the situations usually faced by an ASV. In addition, TSDOS also has better or comparable performance over existing protocols in terms of rate of convergence, consensus error spike, and steady-state error

A Framework for Collaborative Multi-task, Multi-robot Missions

Robotics is a transformative technology that will empower our civilization for a new scale of human endeavors. Massive scale is only possible through the collaboration of individual or groups of robots. Collaboration allows specialization, meaning a multirobot system may accommodate heterogeneous platforms including human partners. This work develops a unified control architecture for collaborative missions comprised of multiple, multi-robot tasks. Using kinematic equations and Jacobian matrices, the system states are transformed into alternative control spaces which are more useful for the designer or more convenient for the operator. The architecture allows multiple tasks to be combined, composing tightly coordinated missions. Using this approach, the designer is able to compensate for non-ideal behavior in the appropriate space using whatever control scheme they choose. This work presents a general design methodology, including analysis techniques for relevant control metrics like stability, responsiveness, and disturbance rejection, which were missing in prior work. Multiple tasks may be combined into a collaborative mission. The unified motion control architecture merges the control space components for each task into a concise federated system to facilitate analysis and implementation. The task coordination function defines task commands as functions of mission commands and state values to create explicit closed-loop collaboration. This work presents analysis techniques to understand the effects of cross-coupling tasks. This work analyzes system stability for the particular control architecture and identifies an explicit condition to ensure stable switching when reallocating robots. We are unaware of any other automated control architectures that address large-scale collaborative systems composed of task-oriented multi-robot coalitions where relative spatial control is critical to mission performance. This architecture and methodology have been validated in experiments and in simulations, repeating earlier work and exploring new scenarios and. It can perform large-scale, complex missions via a rigorous design methodology