Browsing by Subject "Robot Vision"

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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.