Browsing by Subject "Underwater Robotics"
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Item 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.Item Machine Vision for Improved Human-Robot Cooperation in Adverse Underwater Conditions(2021-05) Islam, Md JahidulVisually-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.Item Simulation of Semantically-Aware Obstacle Avoidance Algorithms for Underwater Robots(2021-08-30) Walaszek, Chris AResearchers 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.Item Toward Robotic Autonomy in Data-Scarce and Visually Challenging Environments(2023-06) Hong, JungseokRecent 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.