Sensing and connection systems for assisted and autonomous driving and unmanned vehicles

The special issue, “Sensors, Wireless Connectivity and Systems for Autonomous Vehicles and Smart Mobility” on MDPI Sensors presents 12 accepted papers, with authors from North America, Asia, Europe and Australia, related to the emerging trends in sensing and navigation systems (i.e., sensors plus related signal processing and understanding techniques in multi-agent and cooperating scenarios) for autonomous vehicles, including also unmanned aerial and underwater ones

Characterization of optical communication in a leader-follower unmanned underwater vehicle formation

As part of the research to development an optical communication design of a leader-follower formation between unmanned underwater vehicles (UUVs), this paper presents light field characterization and design configuration of the hardware required to allow the use of distance detection between UUVs. The study specifically is targeting communication between remotely operated vehicles (ROVs). As an initial step in this study, the light field produced from a light source mounted on the leader UUV was empirically characterized and modeled. Based on the light field measurements, a photo-detector array for the follower UUV was designed. Evaluation of the communication algorithms to monitor the UUV’s motion was conducted through underwater experiments in the Ocean Engineering Laboratory at the University of New Hampshire. The optimal spectral range was determined based on the calculation of the diffuse attenuation coefficients by using two different light sources and a spectrometer. The range between the leader and the follower vehicles for a specific water type was determined. In addition, the array design and the communication algorithms were modified according to the results from the light field

Pose Detection and Control of Unmanned Underwater Vehicles (UUVs) Utilizing an Optical Detector Array

As part of the research for development of a leader-follower formation between unmanned underwater vehicles (UUVs), this study presents an optical feedback system for UUV navigation via an optical detector array. Capabilities of pose detection and control in a static-dynamic system (e.g. UUV navigation into a docking station) and a dynamic-dynamic system (e.g. UUV to UUV leader-follower system) are investigated. In both systems, a single light source is utilized as a guiding beacon for a tracker/follower UUV. The UUV uses an optical array consisting of photodiodes to receive the light field emitted from the light source. For UUV navigation applications, accurate pose estimation is essential. In order to evaluate the feasibility of underwater distance detection, the effective communication range between two platforms, i.e. light source and optical detector, and the optimum spectral range that allowed maximum light transmission are calculated. Based on the light attenuation in underwater, the geometry and dimensions of an optical detector array are determined, and the boundary conditions for the developed pose detection algorithms along with the error sources in the experiments are identified. As a test bed to determine optical array dimensions and size, a simulator, i.e. numerical software, is developed, where planar and curved array geometries of varying number of elements are analytically compared and evaluated. Results show that the curved optical detector array is able to distinguish 5 degree- of-freedom (DOF) motion (translation in x, y, z-axes and pitch and yaw rotations) with respect to a single light source. Analytical pose detection and control algorithms are developed for both static-dynamic and dynamic-dynamic systems. Results show that a 5 x 5 curved detector array with the implementation of SMC is reasonably sufficient for practical UUV positioning applications. The capabilities of an optical detector array to determine the pose of a UUV in 3-DOF (x, y and z-axes) are experimentally tested. An experimental platform consisting of a 5 x 5 photodiode array mounted on a hemispherical surface is used to sample the light field emitted from a single light source. Pose detection algorithms are developed to detect pose for steady-state and dynamic cases. Monte Carlo analysis is conducted to assess the pose estimation uncertainty under varying environmental and hardware conditions such as water turbidity, temperature variations in water and electrically-based noise. Monte Carlo analysis results show that the pose uncertainties (within 95% confidence interval) associated with x, y and z-axes are 0.78 m, 0.67 m and 0.56 m, respectively. Experimental results demonstrate that x, y and z-axes pose estimates are accurate to within 0.5 m, 0.2 m and 0.2 m, respectively

Scientific challenges and present capabilities in underwater robotic vehicle design and navigation for oceanographic exploration under-ice.

This paper reviews the scientific motivation and challenges, development, and use of underwater robotic vehicles designed for use in ice-covered waters, with special attention paid to the navigation systems employed for under-ice deployments. Scientific needs for routine access under fixed and moving ice by underwater robotic vehicles are reviewed in the contexts of geology and geophysics, biology, sea ice and climate, ice shelves, and seafloor mapping. The challenges of under-ice vehicle design and navigation are summarized. The paper reviews all known under-ice robotic vehicles and their associated navigation systems, categorizing them by vehicle type (tethered, untethered, hybrid, and glider) and by the type of ice they were designed for (fixed glacial or sea ice and moving sea ice). © 2020 by the authors

Integrated Positioning System of Autonomous Underwater Robot and Its Application in High Latitudes of Arctic Zone

Non

SENSOR PERFORMANCE ANALYSIS FOR MINE DETECTION WITH UNMANNED VEHICLES IN VERY SHALLOW WATER AND SURF ZONES

The very shallow water and surf zones present extraordinary challenges for classifying submerged objects such as mines or shoals. Accessing these areas with traditional unmanned underwater vehicles is difficult, and remotely operated vehicles often require putting operators in harm’s way. This research explores the potential to perform object classification using only forward-looking sonar in the desired operating zones. Experiments were conducted in a controlled environment for two different target objects, a glass sphere and a rectangular cinder block. Next, forward-looking sonar images were analyzed to determine how the intensity and distribution of target returns changed as a function of distance and angle from the sonar. The ability to correlate experimentally measured intensity profiles with a target’s physical size and shape is examined. Finally, recommendations for future research are proposed to further develop this approach for potential naval applications like mine countermeasures.NECC, Virginia Beach, VA, 23459Lieutenant, United States NavyApproved for public release. Distribution is unlimited

Coastal benthic habitat mapping and monitoring by integrating aerial and water surface low-cost drones

Accurate data on community structure is a priority issue in studying coastal habitats facing human pressures. The recent development of remote sensing tools has offered a ground-breaking way to collect ecological information at a very fine scale, especially using low-cost aerial photogrammetry. Although coastal mapping is carried out using Unmanned Aerial Vehicles (UAVs or drones), they can provide limited information regarding underwater benthic habitats. To achieve a precise characterisation of underwater habitat types and species assemblages, new imagery acquisition instruments become necessary to support accurate mapping programmes. Therefore, this study aims to evaluate an integrated approach based on Structure from Motion (SfM) photogrammetric acquisition using low-cost Unmanned Aerial (UAV) and Surface (USV) Vehicles to finely map shallow benthic communities, which determine the high complexity of coastal environments. The photogrammetric outputs, including both UAV-based high (sub-meter) and USV-based ultra-high (sub-centimetre) raster products such as orthophoto mosaics and Digital Surface Models (DSMs), were classified using Object-Based Image Analysis (OBIA) approach. The application of a supervised learning method based on Support Vector Machines (SVM) classification resulted in good overall classification accuracies > 70%, proving to be a practical and feasible tool for analysing both aerial and underwater ultra-high spatial resolution imagery. The detected seabed cover classes included above and below-water key coastal features of ecological interest such as seagrass beds, “banquettes” deposits and hard bottoms. Using USV-based imagery can considerably improve the identification of specific organisms with a critical role in benthic communities, such as photophilous macroalgal beds. We conclude that the integrated use of low-cost unmanned aerial and surface vehicles and GIS processing is an effective strategy for allowing fully remote detailed data on shallow water benthic communities

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions

A COLLISION AVOIDANCE SYSTEM FOR AUTONOMOUS UNDERWATER VEHICLES

The work in this thesis is concerned with the development of a novel and practical collision avoidance system for autonomous underwater vehicles (AUVs). Synergistically, advanced stochastic motion planning methods, dynamics quantisation approaches, multivariable tracking controller designs, sonar data processing and workspace representation, are combined to enhance significantly the survivability of modern AUVs. The recent proliferation of autonomous AUV deployments for various missions such as seafloor surveying, scientific data gathering and mine hunting has demanded a substantial increase in vehicle autonomy. One matching requirement of such missions is to allow all the AUV to navigate safely in a dynamic and unstructured environment. Therefore, it is vital that a robust and effective collision avoidance system should be forthcoming in order to preserve the structural integrity of the vehicle whilst simultaneously increasing its autonomy. This thesis not only provides a holistic framework but also an arsenal of computational techniques in the design of a collision avoidance system for AUVs. The design of an obstacle avoidance system is first addressed. The core paradigm is the application of the Rapidly-exploring Random Tree (RRT) algorithm and the newly developed version for use as a motion planning tool. Later, this technique is merged with the Manoeuvre Automaton (MA) representation to address the inherent disadvantages of the RRT. A novel multi-node version which can also address time varying final state is suggested. Clearly, the reference trajectory generated by the aforementioned embedded planner must be tracked. Hence, the feasibility of employing the linear quadratic regulator (LQG) and the nonlinear kinematic based state-dependent Ricatti equation (SDRE) controller as trajectory trackers are explored. The obstacle detection module, which comprises of sonar processing and workspace representation submodules, is developed and tested on actual sonar data acquired in a sea-trial via a prototype forward looking sonar (AT500). The sonar processing techniques applied are fundamentally derived from the image processing perspective. Likewise, a novel occupancy grid using nonlinear function is proposed for the workspace representation of the AUV. Results are presented that demonstrate the ability of an AUV to navigate a complex environment. To the author's knowledge, it is the first time the above newly developed methodologies have been applied to an A UV collision avoidance system, and, therefore, it is considered that the work constitutes a contribution of knowledge in this area of work.J&S MARINE LT