Browsing by Subject "Underwater Robotics"

Now showing 1 - 6 of 6
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    An Evaluation of Occupancy Sensing Methods for Autonomous Underwater Vehicle Docking
    (2024-12-16) Schmertman, Brock B.
    Docking stations play a critical role in the long-term deployment of autonomous underwater vehicles (AUVs) by automating a range of costly tasks [4], [5]. One requirement of a functional station is an occupancy sensor system capable of detecting when the AUV is correctly oriented to initiate docking. A majority of occupancy sensing approaches presented in academic literature either employ specialized equipment, require a particular form factor to implement, or lack sufficient technical detail to replicate. To address these shortcomings, this report presents the implementation and evaluation of various occupancy sensing methods using inexpensive, commercially available components conformable to many AUV and docking station form factors. First, it was determined that contact-based sensing, implemented with a limit switch, successfully confirmed a critical segment of the AUV was in place to initiate docking. If precautions are implemented to mitigate the risk of false positives, contact-based sensing has a strong potential to reliably measure AUV occupancy. Second, a commercially available radio frequency identification (RFID) reader was found to successfully propagate RFID signals through barriers of air, tap water, and salt water. This demonstrates its potential for use in both fresh and saltwater environments. However, the serial connection to the reader was found to fail in a chlorinated pool, which presents operational issues in certain conductive mediums. Third, a commercially available inductive module was found to successfully couple through barriers of air and tap water, demonstrating its potential to measure AUV occupancy in a freshwater environment by monitoring the current circulating the transmitter. However, coupling was not observed through chlorinated pool water, indicating the module is prone to attenuation in more conductive, electrolyte mediums. Lastly, it was found that applying a simple high-pass filtering algorithm to the accelerometer output of an inertial measurement unit (IMU) was highly effective in characterizing collisions between the AUV and docking station. Moreover, collision detection may be a viable approach to occupancy detection if additional functionality is implemented to distinguish AUV and environmental collisions.
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    De-noising Motion Predictions of Scuba Divers for Aquatic Robots
    (2021)
    Current diver predictors output a sequence of bounding boxes, with two corners randomly sampled from two bivariate gaussians. This introduces noise and uncertainty into the prediction outputs and makes the conversion of this sequence of 2D boxes into a 3D motion vector challenging. This poster describes an approach to de-noise this output and convert the predictions into a format that can be used by aquatic robots to plan their motion and follow scuba divers robustly.
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    Machine Vision for Improved Human-Robot Cooperation in Adverse Underwater Conditions
    (2021-05) Islam, Md Jahidul
    Visually-guided underwater robots are deployed alongside human divers for cooperative exploration, inspection, and monitoring tasks in numerous shallow-water and coastal-water applications. The most essential capability of such companion robots is to visually interpret their surroundings and assist the divers during various stages of an underwater mission. Despite recent technological advancements, the existing systems and solutions for real-time visual perception are greatly affected by marine artifacts such as poor visibility, lighting variation, and the scarcity of salient features. The difficulties are exacerbated by a host of non-linear image distortions caused by the vulnerabilities of underwater light propagation (e.g., wavelength-dependent attenuation, absorption, and scattering). In this dissertation, we present a set of novel and improved visual perception solutions to address these challenges for effective underwater human-robot cooperation. The research outcomes entail novel design and efficient implementation of the underlying vision and learning-based algorithms with extensive field experimental validations and real-time feasibility analyses for single-board deployments. The dissertation is organized into three parts. The first part focuses on developing practical solutions for autonomous underwater vehicles (AUVs) to accompany human divers during an underwater mission. These include robust vision-based modules that enable AUVs to understand human swimming motion, hand gesture, and body pose for following and interacting with them while maintaining smooth spatiotemporal coordination. A series of closed-water and open-water field experiments demonstrate the utility and effectiveness of our proposed perception algorithms for underwater human-robot cooperation. We also identify and quantify their performance variability over a diverse set of operating constraints in adverse visual conditions. The second part of this dissertation is devoted to designing efficient techniques to overcome the effects of poor visibility and optical distortions in underwater imagery by restoring their perceptual and statistical qualities. We further demonstrate the practical feasibility of these techniques as pre-processors in the autonomy pipeline of visually-guided AUVs. Finally, the third part of this dissertation develops methodologies for high-level decision-making such as modeling spatial attention for fast visual search, learning to identify when image enhancement and super-resolution modules are necessary for a detailed perception, etc. We demonstrate that these methodologies facilitate up to 45% faster processing of the on-board visual perception modules and enable AUVs to make intelligent navigational and operational decisions, particularly in autonomous exploratory tasks. In summary, this dissertation delineates our attempts to address the environmental and operational challenges of real-time machine vision for underwater human-robot cooperation. Aiming at a variety of important applications, we develop robust and efficient modules for AUVs to 'follow and interact' with companion divers by accurately perceiving their surroundings while relying on noisy visual sensing alone. Moreover, our proposed perception solutions enable visually-guided robots to 'see better' in noisy conditions, and 'do better' with limited computational resources and real-time constraints. In addition to advancing the state-of-the-art, the proposed methodologies and systems take us one step closer toward bridging the gap between theory and practice for improved human-robot cooperation in the wild.
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    Simulation of Semantically-Aware Obstacle Avoidance Algorithms for Underwater Robots
    (2021-08-30) Walaszek, Chris A
    Researchers working with autonomous underwater vehicles (AUVs) must be able to test their robotic vision-based algorithms on-location in field trials. These trials can be time-consuming and carry the risk of unforeseen hardware and software bugs limiting the amount of data gathered. To this end, being able to test and evaluate algorithms risk-free in a computer simulation beforehand can be invaluable for researchers. Current simulation solutions can provide realistic physics and easily modifiable worlds, however using a 3D graphics engine to create realistic underwater scenarios can improve results considerably and ease the transition into a real-world environment. This research demonstrates the potential of the Unity 3D graphics engine to provide a realistic simulation environment by running and evaluating a vision-based underwater obstacle avoidance algorithm on a simulated Aqua robot. We find that Unity can provide simulated stereo images that can be used by the Semantic Obstacle Avoidance for Robots (SOAR) algorithm to navigate a simple obstacle field en route to a predetermined goal position.
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    Toward Robotic Autonomy in Data-Scarce and Visually Challenging Environments
    (2023-06) Hong, Jungseok
    Recent advances in field robotics have allowed robots to be used in challenging unstructured environments (e.g., space, ocean, and natural disaster scenes) instead of sending human explorers since such environments could pose a threat to human lives. However, most field robots are not fully autonomous, relying on communication with human operators. The inefficiencies and constraints of this dependency result in the limited usage of field robots. Hence, enhancing robotic autonomy for unstructured environments is necessary to enable robots to handle large-scale and time-sensitive problems without operators. Among such environments, the underwater domain needs immediate attention since the hydrosphere has high impacts on our lives. Specifically, water covers 71% of Earth’s surface and has the largest ecosystem on Earth. Unfortunately, various human activities have negatively affected the health of underwater ecosystems, which threatens our Earth’s ecosystem as a whole. One such example is the underwater debris problem. Underwater debris has already had damaging effects on the environment (e.g., damaging wildlife habitats), and its long-term effects are predicted to be catastrophic. Existing methods for collecting underwater debris are ineffective, and human exploration and clean-up of the debris are prohibitively risky and cannot adapt to the scale of the problem. This shows it is necessary to deploy underwater robots, often called autonomous underwater vehicles (AUVs), to clean up underwater debris, but the lack of robotic autonomy discourages AUVs from being used for the task. To bridge this gap, this thesis presents our efforts to improve robotic autonomy to make it possible for robots to handle challenging tasks, such as marine debris cleanup, in degraded environments. Part I introduces our efforts to improve robotic underwater object detection, focused on underwater debris, by addressing data scarcity and unique circumstances underwater (e.g., modified shapes of objects over time, degraded vision). In Part II, we present localization and obstacle avoidance algorithms underwater to enable robots to explore a given environment safely and localize underwater objects after detecting them. Even with the improved object detection and navigation methods covered in Part I and II, robots can still struggle with autonomous behaviors due to the extreme challenges that unstructured environments pose in perception and navigation. In cases such as these, human-robot collaborations are still necessary, but robotic capabilities to establish the collaborations are under-investigated and need to be improved. To enable such collaborations, we present algorithms for effective human-robot collaboration in Part III. Lastly, in Part IV, we introduce open-source underwater robotic systems to support robotic research, including the development and deployment process of the research presented in Part I - III. The research introduced in this thesis has been realized and tested on board physical AUVs in various water environments. While our research has mainly focused on the problems in the underwater environment, its robust results in this domain mean that it can be applied to real-world problems in other domains that share similar challenges. Thus, the research has great potential to significantly advance robotic autonomy, bring a broader impact on human well-being, and drive robotics forward for social good.
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    Toward Visual Communication Methods for Underwater Human-Robot Interaction
    (2024-04) Edge, Chelsey
    Trained divers take on the complex and often dangerous underwater environment to perform essential tasks. These tasks include inspection and repair of underwater infrastructure and monitoring the health of water systems through tasks such as observations of coral reefs and tracking of invasive species. Autonomous Underwater Vehicles (AUVs) able to assist with these tasks have become more widely deployed as their capabilities improve, however, when deployed as solo agents they lack the intuition and ability to adapt to unexpected situations as a human diver would. The objective of collaboration between a diver and an AUV brings together the ability of an AUV to perform tasks that are dangerous to the human diver, while maintaining the ability of the diver to monitor the situation and update task information as necessary. For this collaboration to be successful meaningful communication is essential, especially when the goal of the collaboration is to complete a task. This dissertation presents our work towards improving diver-AUV collaboration, focusing on utilizing visual perception onboard the AUV. In the following chapters, we discuss two novel communication algorithms that allow divers to communicate information about the location of an object required by an AUV to perform a task. These methods have been designed to take into account challenges such as limitations of on-board computation as well as challenges inherent to working in the underwater domain, such as non-traditional human body poses and limitations of traditional, terrestrial, computer vision. Evaluations of these methods are performed onboard AUVs. We then incorporate these algorithms into a communication system which allows a diver to assign the AUV a task based on the object detected. This system also provides feedback from the AUV to the diver about the task which will be performed, forming a closed loop communication system between diver and AUV. Validation of this system was performed fully onboard an AUV in the Caribbean Sea. In addition, as AUV visual perception can be hampered by the visual degradation of the underwater environment, we therefore present an investigation into a task-based method to improve AUV vision. We also discuss our contributions to the design and creation of the research platforms necessary for this research to move forward.