Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. An Autonomous Underwater Vehicle (AUV) moving through a cluttered space, such as a shipwreck, or a decorated cave is an extremely challenging problem and has not been addressed in the past. This paper proposed a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization with near-optimal guarantees for AUVs. A sampling based correction procedure ensures that the planning is not limited by local minima, enabling navigation through narrow spaces. The method is shown, both on simulation and in-pool experiments, to be fast enough to enable real-time autonomous navigation for an Aqua2 AUV with strong safety guarantees.
Enabling Aqua2 AUVs to perform complex tasks has been attempted a few times. In the beginning basic patterns were used, then complex swimming gaits were developed in order to perform patterns such as swimming on the side, in a corkscrew motion, or performing a barrel roll @cite_2 . Visual tags placed on structures were used to enable the AUV to navigate @cite_16 , while a learned reactive controller had the vehicle maintain safe distance while moving over a coral reef @cite_24 @cite_39 .
@cite_24 @cite_16 @cite_39 @cite_2
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We present a GPU-based integrated robotic platform that enables collision avoidance, navigation, and image understanding on a single underwater vehicle. The platform enables observational tasks such as coral reef health assessment by enabling simultaneous operation of multiple image analysis taskswhile navigating in close proximity to obstacles. The integration of a GPU allows us to leverage deep neural networks for collision avoidance and automated object detection and classification while a general purpose CPU processes images to perform visual Simultaneous Localization and Mapping (SLAM). In this paper, we describe the system architecture and summarize experimental results for coral detection and collision-free navigation. Inspection and exploration of complex underwater structures requires the development of agile and easy to program platforms. In this paper, we describe a system that enables the deployment of an autonomous underwater vehicle in 3D environments proximal to the ocean bottom. Unlike many previous approaches, our solution: uses oscillating hydrofoil propulsion; allows for stable control of the robot’s motion and sensor directions; allows human operators to specify detailed trajectories in a natural fashion; and has been successfully demonstrated as a holistic system in the open ocean near both coral reefs and a sunken cargo ship. A key component of our system is the 3D control of a hexapod swimming robot, which can move the vehicle through agile sequences of orientations despite challenging marine conditions. We present two methods to easily generate robot trajectories appropriate for deployments in close proximity to challenging contours of the sea floor. Both offline recording of trajectories using augmented reality and online placement of fiducial tags in the marine environment are shown to have desirable properties, with complementary strengths and weaknesses. Finally, qualitative and quantitative results of the 3D control system are presented. We address the problem of learning vision-based, collision-avoiding, and target-selecting controllers in 3D, specifically in underwater environments densely populated with coral reefs. Using a highly maneuverable, dynamic, six-legged (or flippered) vehicle to swim underwater, we exploit real time visual feedback to make close-range navigation decisions that would be hard to achieve with other sensors. Our approach uses computer vision as the sole mechanism for both collision avoidance and visual target selection. In particular, we seek to swim close to the reef to make observations while avoiding both collisions and barren, coral-deprived regions. To carry out path selection while avoiding collisions, we use monocular image data processed in real time. The proposed system uses a convolutional neural network that takes an image from a forward-facing camera as input and predicts unscaled and relative path changes. The network is trained to encode our desired obstacle-avoidance and reef-exploration objectives via supervised learning from human-labeled data. The predictions from the network are transformed into absolute path changes via a combination of a temporally-smoothed proportional controller for heading targets and a low-level motor controller. This system enables safe and autonomous coral reef navigation in underwater environments. We validate our approach using an untethered and fully autonomous robot swimming through coral reef in the open ocean. Our robot successfully traverses 1000 m of the ocean floor collision-free while collecting close-up footage of coral reefs. We present an end-to-end framework for realizing fully automated gait learning for a complex underwater legged robot. Using this framework, we demonstrate that a hexapod flipper-propelled robot can learn task-specific control policies purely from experience data. Our method couples a state-of-the-art policy search technique with a family of periodic low-level controls that are well suited for underwater propulsion. We demonstrate the practical efficacy of tabula rasa learning, that is, learning without the use of any prior knowledge, of policies for a six-legged swimmer to carry out a variety of acrobatic maneuvers in three dimensional space. We also demonstrate informed learning that relies on simulated experience from a realistic simulator. In numerous cases, novel emergent gait behaviors have arisen from learning, such as the use of one stationary flipper to create drag while another oscillates to create thrust. Similar effective results have been demonstrated in under-actuated configurations, where as few as two flippers are used to maneuver the robot to a desired pose, or through an acrobatic motion such as a corkscrew. The success of our learning framework is assessed both in simulation and in the field using an underwater swimming robot.