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1. Monocular Camera and Single-Beam Sonar-Based Underwater Collision-Free Navigation with Domain Randomization

   Yang, P. ; Liu, H. ; Roznere, M. ; Quattrini Li, A. ( January 2022 , International Symposium on Robotics Research (ISRR))

   Billard, A. ; Asfour, T. ; Khatib, O. (Ed.)

   Underwater navigation presents several challenges, including unstructured unknown environments, lack of reliable localization systems (e.g., GPS), and poor visibility. Furthermore, good-quality obstacle detection sensors for underwater robots are scant and costly; and many sensors like RGB-D cameras and LiDAR only work in-air. To enable reliable mapless underwater navigation despite these challenges, we propose a low-cost end-to-end navigation system, based on a monocular camera and a fixed single-beam echo-sounder, that efficiently navigates an underwater robot to waypoints while avoiding nearby obstacles. Our proposed method is based on Proximal Policy Optimization (PPO), which takes as input current relative goal information, estimated depth images, echo-sounder readings, and previous executed actions, and outputs 3D robot actions in a normalized scale. End-to-end training was done in simulation, where we adopted domain randomization (varying underwater conditions and visibility) to learn a robust policy against noise and changes in visibility conditions. The experiments in simulation and real-world demonstrated that our proposed method is successful and resilient in navigating a low-cost underwater robot in unknown underwater environments. The implementation is made publicly available at https://github.com/dartmouthrobotics/deeprl-uw-robot-navigation. 

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2. Exploring Regression-Based Narrative Planning

   Ware, Stephen G. ; Fisher, Mira ( October 2020 , Proceedings of the 12th Intelligent Narrative Technologies workshop at the 16th AAAI international conference on Artificial Intelligence and Interactive Digital Entertainment)

   A valid and believable narrative plan must often meet at least two requirements: the author’s goal must be satisfied by the end, and every action taken must make sense based on the goals and beliefs of the characters who take them. Many narrative planners are based on progression, or forward search through the space of possible states. When reasoning about goals and beliefs, progression can be wasteful, because either the planner needs to satisfy the author’s goal first and then explain actions, backtracking when an explanation cannot be found, or explain actions as they are taken, which may waste effort explaining actions that are not relevant to the author’s goal. We propose that regression, or backward search from goals, can address this problem. Regression ensures that every action sequence is intentional and only reasons about the agent beliefs needed for a plan to make sense. 

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3. Learning Safe Numeric Action Models

   https://doi.org/10.1609/aaai.v37i10.26424  

   Mordoch, Argaman ; Juba, Brendan ; Stern, Roni ( June 2023 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Powerful domain-independent planners have been developed to solve various types of planning problems. These planners often require a model of the acting agent's actions, given in some planning domain description language. Yet obtaining such an action model is a notoriously hard task. This task is even more challenging in mission-critical domains, where a trial-and-error approach to learning how to act is not an option. In such domains, the action model used to generate plans must be safe, in the sense that plans generated with it must be applicable and achieve their goals. Learning safe action models for planning has been recently explored for domains in which states are sufficiently described with Boolean variables. In this work, we go beyond this limitation and propose the NSAM algorithm. NSAM runs in time that is polynomial in the number of observations and, under certain conditions, is guaranteed to return safe action models. We analyze its worst-case sample complexity, which may be intractable for some domains. Empirically, however, NSAM can quickly learn a safe action model that can solve most problems in the domain. 

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4. Human Diver-Inspired Visual Navigation: Towards Coverage Path Planning of Shipwrecks

   https://doi.org/10.4031/MTSJ.55.4.6  

   Karapetyan, Nare ; Johnson, James V. ; Rekleitis, Ioannis ( July 2021 , Marine Technology Society Journal)

   null (Ed.)

   Abstract This work proposes vision-only navigation strategies for an autonomous underwater robot. This approach is a step towards solving the coverage path planning problem in a 3-D environment for surveying underwater structures. Given the challenging conditions of the underwater domain, it is very complicated to obtain accurate state estimates reliably. Consequently, it is a great challenge to extend known path planning or coverage techniques developed for aerial or ground robot controls. In this work, we are investigating a navigation strategy utilizing only vision to assist in covering a complex underwater structure. We propose to use a navigation strategy akin to what a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. The focus of this article is a step towards enabling the autonomous operation of lightweight robots near underwater wrecks in order to collect data for creating photo-realistic maps and volumetric 3-D models while at the same time avoiding collisions. The proposed method uses convolutional neural networks to learn the control commands based on the visual input. We have demonstrated the feasibility of using a system based only on vision to learn specific strategies of navigation with 80% accuracy on the prediction of control command changes. Experimental results and a detailed overview of the proposed method are discussed. 

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5. Scientific Challenges and Present Capabilities in Underwater Robotic Vehicle Design and Navigation for Oceanographic Exploration Under-Ice

   https://doi.org/10.3390/rs12162588  

   Barker, Laughlin D. ; Jakuba, Michael V. ; Bowen, Andrew D. ; German, Christopher R. ; Maksym, Ted ; Mayer, Larry ; Boetius, Antje ; Dutrieux, Pierre ; Whitcomb, Louis L. ( August 2020 , Remote Sensing)

   This paper reviews the scientific motivation and challenges, development, and use of underwater robotic vehicles designed for use in ice-covered waters, with special attention paid to the navigation systems employed for under-ice deployments. Scientific needs for routine access under fixed and moving ice by underwater robotic vehicles are reviewed in the contexts of geology and geophysics, biology, sea ice and climate, ice shelves, and seafloor mapping. The challenges of under-ice vehicle design and navigation are summarized. The paper reviews all known under-ice robotic vehicles and their associated navigation systems, categorizing them by vehicle type (tethered, untethered, hybrid, and glider) and by the type of ice they were designed for (fixed glacial or sea ice and moving sea ice). 

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