Underwater navigation is an area of increasing research interest due to its fundamental complexity and industrial applications. However, due to convenience and current theoretical understanding, the vast majority of underwater platforms utilize thrusters, while other forms of propulsion, such as undulatory locomotion, have been given limited exposure. This paper provides the first real-time motion planning framework that produces energy and time efficient paths with empirical local optimality for articulated swimming robots in 3D, called SIMP. SIMP utilizes learned associations between parameterized dynamically feasible undulatory gaits with their expected energy cost, velocity, and swept-out volume of the robot during execution, to formulate a simplified optimization problem that decides the path to be followed with the corresponding consecutive gaits, and navigates the robot safely in complex 3D environments. The proposed pipeline is tested in numerical experiments with realistic dynamics for a 10 link underwater snake robot (USR) with anguilliform gaits, in simulated cluttered environments of significant challenge, displaying real-time replanning performance of more than 1 Hz.

Robust underwater motion planning of autonomous underwater vehicles (AUVs) in dynamic cluttered environments is a problem that has yet to be addressed in depth. Due to advances in technology and computational capacity, AUVs are expected to operate safely and autonomously in increasingly challenging environments, necessitating methods that are able to safely navigate robots in real-time. Though, most solutions remain overly cautious and conservative. This paper proposes RUMP, a novel locally-optimal motion planning framework for robust real-time autonomous underwater navigation in 3D cluttered environments consisting of observed static and dynamic obstacles. The problem is modeled using path optimization and can be solved in real-time with a common nonlinear solver. The constructed objective function allows deciding the local goal during optimization to both maximize safety within a planning horizon and minimize the expected distance to the target position. Furthermore, path safety is considered for the entire transition between consecutive states, utilizing a novel approach for continuous spatiotemporal collision checks. The proposed formulation provides safe performance even in environments with obstacles that may move orders of magnitude faster than the AUV itself. Simulation experiments, in different challenging scenarios of obstacles moving up to 100 times faster than the robot, showcase robustness and efficient real-time performance of more than 15 Hz.

Autonomous operation in underwater environ-ments is, arguably, one of the most complex domains. It requires safe operations under the presence of unpredictable surge, currents, uncertainty, and dynamic obstacles that challenges to the highest degree real-time motion planning; the primary focus of this paper. Although previous work addressed the problem of safe real-time 3D navigation in cluttered underwater environments, it did not account explicitly for disturbances, currents, dynamic obstacles, or uncertainty growth. This paper presents ResiPlan, a novel motion planning framework that utilizes past information of errors monitoring the path follower's performance, along with estimation of dynamic obstacles and uncertainty, to produce adaptive paths by adjusting the safety margins accordingly. Extensive numerical experiments and simulations validate the safety guarantees of the technique, in a variety of different environments with various types of disturbance, showcasing the strong potential to be utilized for operations in challenging underwater environments.

In this paper we present a complete framework for Underwater SLAM utilizing a single inexpensive sensor. Over the recent years, imaging technology of action cameras is producing stunning results even under the challenging conditions of the underwater domain. The GoPro 9 camera provides high definition video in synchronization with an Inertial Measurement Unit (IMU) data stream encoded in a single mp4 file. The visual inertial SLAM framework is augmented to adjust the map after each loop closure. Data collected at an artificial wreck of the coast of South Carolina and in caverns and caves in Florida demonstrate the robustness of the proposed approach in a variety of conditions.

Visual monitoring operations underwater require both observing the objects of interest in close-proximity, and tracking the few feature-rich areas necessary for state estimation. This paper introduces the first navigation framework, called AquaVis, that produces on-line visibility-aware motion plans that enable Autonomous Underwater Vehicles (AUVs) to track multiple visual objectives with an arbitrary camera configuration in real-time. Using the proposed pipeline, AUVs can efficiently move in 3D, reach their goals while avoiding obstacles safely, and maximizing the visibility of multiple objectives along the path within a specified proximity. The method is sufficiently fast to be executed in real-time and is suitable for single or multiple camera configurations. Experimental results show the significant improvement on tracking multiple automatically-extracted points of interest, with low computational overhead and fast re-planning times.Accompanying short video: https://youtu.be/JKO bbrIZyU

Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through narrow spaces. Two different modalities are proposed: planning with a known map results in efficient trajectories through cluttered spaces; operating in an unknown environment utilizes the point cloud from the visual features detected to navigate efficiently while avoiding the detected obstacles. The proposed approach is rigorously tested, both on simulation and in-pool experiments, proven to be fast enough to enable safe real-time 3D autonomous navigation for an AUV.

In this paper, we propose a real-time deep learning approach for determining the 6D relative pose of Autonomous Underwater Vehicles (AUV) from a single image. A team of autonomous robots localizing themselves in a communication-constrained underwater environment is essential for many applications such as underwater exploration, mapping, multi-robot convoying, and other multi-robot tasks. Due to the profound difficulty of collecting ground truth images with accurate 6D poses underwater, this work utilizes rendered images from the Unreal Game Engine simulation for training. An image-to-image translation network is employed to bridge the gap between the rendered and the real images producing synthetic images for training. The proposed method predicts the 6D pose of an AUV from a single image as 2D image keypoints representing 8 corners of the 3D model of the AUV, and then the 6D pose in the camera coordinates is determined using RANSAC-based PnP. Experimental results in real-world underwater environments (swimming pool and ocean) with different cameras demonstrate the robustness and accuracy of the proposed technique in terms of translation error and orientation error over the state-of-the-art methods. The code is publicly available.

A plethora of state estimation techniques have appeared in the last decade using visual data, and more recently with added inertial data. Datasets typically used for evaluation include indoor and urban environments, where supporting videos have shown impressive performance. However, such techniques have not been fully evaluated in challenging conditions, such as the marine domain. In this paper, we compare ten recent open-source packages to provide insights on their performance and guidelines on addressing current challenges. Specifically, we selected direct and indirect methods that fuse camera and Inertial Measurement Unit (IMU) data together. Experiments are conducted by testing all packages on datasets collected over the years with underwater robots in our laboratory. All the datasets are made available online.

This paper addresses the problem of robot global localization in a known environment, in the presence of many dynamic obstacles. Deploying a robot in crowded spaces such as museums, shopping malls, department stores, or university campuses is especially challenging because the moving people occlude the static parts of the environment, such as walls and doorways, making the robot essentially blind. A new weighting function is proposed for a particle filter state estimation algorithm that accounts for the presence of dynamic obstacles and avoids population depletion. An active localization strategy is employed which guides the robot to locations that resolve ambiguities and eliminate hypotheses in a systematic manner. Experimental results from multiple simulations and from real robot deployments validate the localization improvements achieved by the proposed method.