Development and testing of a dual accelerometer vector sensor for AUV acoustic surveys

This paper presents the design, manufacturing and testing of a Dual Accelerometer Vector Sensor (DAVS). The device was built within the activities of theWiMUST project, supported under the Horizon 2020 Framework Programme, which aims to improve the efficiency of the methodologies used to perform geophysical acoustic surveys at sea by the use of Autonomous Underwater Vehicles (AUVs). The DAVS has the potential to contribute to this aim in various ways, for example, owing to its spatial filtering capability, it may reduce the amount of post processing by discriminating the bottom from the surface reflections. Additionally, its compact size allows easier integration with AUVs and hence facilitates the vehicle manoeuvrability compared to the classical towed arrays. The present paper is focused on results related to acoustic wave azimuth estimation as an example of its spatial filtering capabilities. The DAVS device consists of two tri-axial accelerometers and one hydrophone moulded in one unit. Sensitivity and directionality of these three sensors were measured in a tank, whilst the direction estimation capabilities of the accelerometers paired with the hydrophone, forming a vector sensor, were evaluated on a Medusa Class AUV, which was sailing around a deployed sound source. Results of these measurements are presented in this paper.European Union [645141]info:eu-repo/semantics/publishedVersio

Mixed marker-based/marker-less visual odometry system for mobile robots

When moving in generic indoor environments, robotic platforms generally rely solely on information provided by onboard sensors to determine their position and orientation. However, the lack of absolute references often leads to the introduction of severe drifts in estimates computed, making autonomous operations really hard to accomplish. This paper proposes a solution to alleviate the impact of the above issues by combining two vision‐based pose estimation techniques working on relative and absolute coordinate systems, respectively. In particular, the unknown ground features in the images that are captured by the vertical camera of a mobile platform are processed by a vision‐based odometry algorithm, which is capable of estimating the relative frame‐to‐frame movements. Then, errors accumulated in the above step are corrected using artificial markers displaced at known positions in the environment. The markers are framed from time to time, which allows the robot to maintain the drifts bounded by additionally providing it with the navigation commands needed for autonomous flight. Accuracy and robustness of the designed technique are demonstrated using an off‐the‐shelf quadrotor via extensive experimental test

A rotorcraft flight database for validation of vision-based ranging algorithms

A helicopter flight test experiment was conducted at the NASA Ames Research Center to obtain a database consisting of video imagery and accurate measurements of camera motion, camera calibration parameters, and true range information. The database was developed to allow verification of monocular passive range estimation algorithms for use in the autonomous navigation of rotorcraft during low altitude flight. The helicopter flight experiment is briefly described. Four data sets representative of the different helicopter maneuvers and the visual scenery encountered during the flight test are presented. These data sets will be made available to researchers in the computer vision community

Design and Evaluation of a Propulsion System for Small, Compact, Low-Speed Maneuvering Underwater Vehicles

Underwater vehicles used to perform precision inspection and non-destructive evaluation in tightly constrained or delicate underwater environments must be small, have low-speed maneuverability and a smooth streamlined outer shape with no appendages. In this thesis, the design and analysis of a new propulsion system for such underwater vehicles is presented. It consists primarily of a syringe and a plunger driven by a linear actuator and uses different inflow and outflow nozzles to provide continuous propulsive force. A prototype of the proposed propulsion mechanism is built and tested. The practical utility and potential efficacy of the system is demonstrated and assessed via direct thrust measurement experiments and by use of an initial proof-of-concept test vehicle. Experiments are performed to enable the evaluation and modelling of the thrust output of the mechanism as well as the speed capability of a vehicle employing the propulsion system

Challenges and Solutions for Autonomous Robotic Mobile Manipulation for Outdoor Sample Collection

In refinery, petrochemical, and chemical plants, process technicians collect uncontaminated samples to be analyzed in the quality control laboratory all time and all weather. This traditionally manual operation not only exposes the process technicians to hazardous chemicals, but also imposes an economical burden on the management. The recent development in mobile manipulation provides an opportunity to fully automate the operation of sample collection. This paper reviewed the various challenges in sample collection in terms of navigation of the mobile platform and manipulation of the robotic arm from four aspects, namely mobile robot positioning/attitude using global navigation satellite system (GNSS), vision-based navigation and visual servoing, robotic manipulation, mobile robot path planning and control. This paper further proposed solutions to these challenges and pointed the main direction of development in mobile manipulation

Toward Dynamical Sensor Management for Reactive Wall-following

We propose a new paradigm for reactive wallfollowing by a planar robot taking the form of an actively steered sensor model that augments the robot’s motion dynamics. We postulate a foveated sensor capable of delivering third-order infinitesimal (range, tangent, and curvature) data at a point along a wall (modeled as an unknown smooth plane curve) specified by the angle of the ray from the robot’s body that first intersects it. We develop feedback policies for the coupled (point or unicycle) sensorimotor system that drive the sensor’s foveal angle as a function of the instantaneous infinitesimal data, in accord with the trade-off between a desired standoff and progress-rate as the wall’s curvature varies unpredictably in the manner of an unmodeled noise signal. We prove that in any neighborhood within which the thirdorder infinitesimal data accurately predicts the local “shape” of the wall, neither robot will ever hit it. We empirically demonstrate with comparative physical studies that the new active sensor management strategy yields superior average tracking performance and avoids catastrophic collisions or wall losses relative to the passive sensor variant. This work was supported by AFOSR MURI FA9550–10–1−0567. For further information, visit Kod*lab

Hybrid state estimators for the control of remotely operated underwater vehicles

Submitted in partial fulfillment of the requirements for the degree of Ocean Engineer at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution August 1988This paper explores the use of 'hybrid' state estimators to increase the accuracy and flexibility of acoustic position measurement systems used in the control of underwater vehicles. Two different approaches to extend the range of acoustic position measurement systems are explored. The first approach is the use of an inexpensive Strapdown Inertial Measurement System (SIMS) to augment the acoustic with inertial position information. This approach is based on the experience gained using an attitude and inertial measurement package fielded on the JASON JUNIOR Remotely Operated Vehicle (ROV). The second approach is the use of a mobile, platform-mounted, acoustic net in conjunction with a platform tracking system. This second investigation used the JASON ROV as the basis for the simulation work. As motivation, some of the theoretical and practical difficulties encountered when range is extended using an unaugmented system are explored. Simulation results are used to demonstrate the effects of these difficulties on position estimation accuracy and on the performance in closed loop control of the vehicle. Using measured sensor noise characteristics, a hybrid Kalman filter is developed for each approach to take the greatest advantage of the available information. Formulation of the Kalman filter is different for each case. In the second case, the geographic position of the ROV is the sum of the acoustic net's geographic position, measured at a different interval by an RF positioning system, and the position of the ROV relative to the net, as measured acoustically. Closed loop vehicle performance evaluations are made for representative noise levels and update rates with and without the augmentation discussed in the first approach. Finally, conclusions are drawn about the benefits and applications of the hybrid Kalman filter to the control of Remotely Operated Vehicles