This paper presents a novel approach to underwater terrain mapping for Autonomous Underwater Vehicles (AUVs) operating in close proximity to complex 3D environments. The proposed methodology creates a probabilistic elevation map of the terrain using a monocular image learning-based scene range estimator as a sensor. This scene range estimator can filter transient objects such as fish and lighting variations. The mapping approach considers uncertainty in both the estimated scene range and robot pose as the AUV moves through the environment. The resulting elevation map can be used for reactive path planning and obstacle avoidance to allow robotic systems to approach the underwater terrain as closely as possible. The performance of our approach is evaluated in a simulated underwater environment by comparing the reconstructed terrain to ground truth reference maps, as well as demonstrated using AUV field data collected within in a coral reef environment. The simulations and field results show that the proposed approach is feasible for obstacle detection and range estimation using a monocular camera in reef environments.
Center for Intelligent Machines, McGill University（麦吉尔大学智能机器中心）
We present a robotic exploration technique in which the goal is to learn to a visual model and be able to distinguish between different terrains and other visual components in an unknown environment. We use ROST, a realtime online spatiotemporal topic modeling framework to model these terrains using the observations made by the robot, and then use an information theoretic path planning technique to define the exploration path. We conduct experiments with aerial view and underwater datasets with millions of observations and varying path lengths, and find that paths that are biased towards locat…
Expansion of today's underwater scenarios and missions necessitates the requestion for robust decision making of the Autonomous Underwater Vehicle (AUV); hence, design an efficient decision making framework is essential for maximizing the mission productivity in a restricted time. This paper focuses on developing a deliberative conflict-free-task assignment architecture encompassing a Global Route Planner (GRP) and a Local Path Planner (LPP) to provide consistent motion planning encountering both environmental dynamic changes and a priori knowledge of the terrain, so that the AUV is reactively…
This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the concept of a coverage tree (CT) with a TSP-inspired tree traversal strategy. The CT-CPP method is validated on a high-fidelity underwater simulator and the results are compared to an existing terrain following CPP method. The results show that CT-CPP yields significant reduction in t…
College of Science and Engineering, Flinders University（弗林德斯大学科学与工程学院）；Centre for Maritime Engineering, Control and Imaging, Flinders University（弗林德斯大学海事工程、控制与成像中心）；Seattle Children's Research Institute, University of Washington（华盛顿大学西雅图儿童研究所）
This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle's task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle's battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available t…
The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mi…
STERN: Simultaneous Trajectory Estimation and Relative Navigation for Autonomous Underwater Proximity Operations
Centre for Naval Architecture, KTH Royal Institute of Technology（KTH皇家理工学院海军建筑中心）；Division of Robotics, Perception, and Learning, KTH Royal Institute of Technology（KTH皇家理工学院机器人、感知与学习系）
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 object…
Wake-Informed 3D Path Planning for Autonomous Underwater Vehicles Using A* and Neural Network Approximations
Centre for Defence Engineering, Research and Training (CDERT), Flinders University（国防工程研究与培训中心（CDERT）、弗林德斯大学）；Maritime Engineering, Faculty of Engineering and Physical Sciences, University of Southampton（海洋工程系，工程与物理科学学院，南安普顿大学）
Autonomous UAV Pipeline Near-proximity Inspection via Disturbance-Aware Predictive Visual Servoing
School of Automation, Key Laboratory of Measurement and Control of CSE, Ministry of Education, Southeast University（自动化学院、CSE测量与控制重点实验室、教育部、东南大学）；Department of Aeronautical and Automotive Engineering, Loughborough University（航空与汽车工程系、罗汉布大学）；Research Centre for Low Altitude Economy and the Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University（低空经济研究中心和航空与航空工程系、香港理工大学）
DUViN: Diffusion-Based Underwater Visual Navigation via Knowledge-Transferred Depth Features
Department of Electrical and Electronic Engineering, The University of Melbourne, Australia（墨尔本大学电子与电气工程系）；School of Mathematics and Statistics, The University of Melbourne, Australia（墨尔本大学数学与统计学学院）
In this paper, we present a fast, on-line mapping and planning solution for operation in unknown, off-road, environments. We combine obstacle detection along with a terrain gradient map to make simple and adaptable cost map. This map can be created and updated at 10 Hz. An A* planner finds optimal paths over the map. Finally, we take multiple samples over the control input space and do a kinematic forward simulation to generated feasible trajectories. Then the most optimal trajectory, as determined by the cost map and proximity to A* path, is chosen and sent to the controller. Our method allow…
Relative Navigation and Dynamic Target Tracking for Autonomous Underwater Proximity Operations
AI, Robotics and Cybersecurity Center, Örebro University（人工智能、机器人与网络安全中心，奥雷布罗大学）；Centre for Naval Architecture, KTH Royal Institute of Technology（海军工程中心，皇家理工学院）；department of Aeronautics and Astronautics, Massachusetts Institute of Technology（航空与航天系，麻省理工学院）；Division of Robotics, Perception and Learning, KTH Royal Institute of Technology（机器人、感知与学习系，皇家理工学院）
Humanoid robots are increasingly demanded to operate in interactive and human-surrounded environments while achieving sophisticated locomotion and manipulation tasks. To accomplish these tasks, roboticists unremittingly seek for advanced methods that generate whole-body coordination behaviors and meanwhile fulfill various planning and control objectives. Undoubtedly, these goals pose fundamental challenges to the robotics and control community. To take an incremental step towards reducing the performance gap between theoretical foundations and real implementations, we present a planning and co…
ARCSnake V2: Mechanical Adaptations For An Amphibious Multi-Domain Screw-Propelled Snake-Like Robot
Mechanical and Aerospace Engineering Department, University of California San Diego（加州大学圣地亚哥分校机械与航空航天工程系）；Electrical and Computer Engineering Department, University of California, San Diego（加州大学圣地亚哥分校电气与计算机工程系）
Woods Hole Oceanographic Institution（伍兹霍尔海洋研究所）；Point Loma Nazarene University（波因特洛马拿撒勒大学）；NOAA（美国国家海洋和大气管理局）
This paper explores the use of a Bayesian non-parametric topic modeling technique for the purpose of anomaly detection in video data. We present results from two experiments. The first experiment shows that the proposed technique is automatically able characterize the underlying terrain, and detect anomalous flora in image data collected by an underwater robot. The second experiment shows that the same technique can be used on images from a static camera in a dynamic unstructured environment. In the second dataset, consisting of video data from a static seafloor camera capturing images of a bu…
