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1. Coverage Path Planning for Mapping of Underwater Structures

   https://doi.org/10.1109/IEEECONF38699.2020.9389389  

   Karapetyan, Nare ; Johnson, James V. ; Rekleitis, Ioannis ( October 2020 , Global Oceans 2020: Singapore – U.S. Gulf Coast)

   null (Ed.)

   This paper addresses the problem of the coverage path planning in a 3D environment for surveying underwater structures. We propose to use the navigation strategy that a human diver will execute when circumnavigating around a region of interest, in particular when collecting data from a shipwreck. In contrast to the previous methods in the literature, we are aiming to perform coverage in completely unknown environment with some initial prior information. Our proposed method uses convolutional neural networks to learn the control commands based on the visual input. Preliminary results and a detailed overview of the proposed method are discussed. 

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2. 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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3. Towards Learning Ocean Models for Long-term Navigation in Dynamic Environments

   https://doi.org/10.1109/OCEANSChennai45887.2022.9775460  

   Padrao, Paulo ; Dominguez, Alberto ; Bobadilla, Leonardo ; Smith, Ryan N. ( February 2022 , OCEANS 2022 - Chennai)

   The use of underwater robot systems, including Autonomous Underwater Vehicles (AUVs), has been studied as an effective way of monitoring and exploring dynamic aquatic environments. Furthermore, advances in artificial intelligence techniques and computer processing led to a significant effort towards fully autonomous navigation and energy-efficient approaches. In this work, we formulate a reinforcement learning framework for long-term navigation of underwater vehicles in dynamic environments using the techniques of tile coding and eligibility traces. Simulation results used actual oceanic data from the Regional Ocean Modeling System (ROMS) data set collected in Southern California Bight (SCB) region, California, USA 

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4. Towards GPU-Accelerated PRM for Autonomous Navigation

   https://doi.org/10.1007/978-3-030-43020-7_74  

   Blankenburg, Janelle ; Kelley, Richard ; Feil-Seifer, David ; Wu, Rui ; Harris, Frederick C. ( May 2020 , International Conference on Information Technology–New Generations)

   Sampling based planning is an important step for long-range navigation for an autonomous vehicle. This work proposes a GPU-accelerated sampling based path planning algorithm which can be used as a global planner in autonomous navigation tasks. A modified version of the generation portion for the Probabilistic Road Map (PRM) algorithm is presented which reorders some steps of the algorithm in order to allow for parallelization and thus can benefit highly from utilization of a GPU. The GPU and CPU algorithms were compared using a simulated navigation environment with graph generation tasks of several different sizes. It was found that the GPU-accelerated version of the PRM algorithm had significant speedup over the CPU version (up to 78×). This results provides promising motivation towards implementation of a real-time autonomous navigation system in the future. 

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5. NMPC-LBF: Nonlinear MPC with Learned Barrier Function for Decentralized Safe Navigation of Multiple Robots in Unknown Environments

   https://doi.org/10.1109/IROS47612.2022.9981177  

   Lafmejani, Amir Salimi ; Berman, Spring ; Fainekos, Georgios ( October 2022 , 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   In this paper, we present a decentralized control approach based on a Nonlinear Model Predictive Control (NMPC) method that employs barrier certificates for safe navigation of multiple nonholonomic wheeled mobile robots in unknown environments with static and/or dynamic obstacles. This method incorporates a Learned Barrier Function (LBF) into the NMPC design in order to guarantee safe robot navigation, i.e., prevent robot collisions with other robots and the obstacles. We refer to our proposed control approach as NMPC-LBF. Since each robot does not have a priori knowledge about the obstacles and other robots, we use a Deep Neural Network (DeepNN) running in real-time on each robot to learn the Barrier Function (BF) only from the robot's LiDAR and odometry measurements. The DeepNN is trained to learn the BF that separates safe and unsafe regions. We implemented our proposed method on simulated and actual Turtlebot3 Burger robot(s) in different scenarios. The implementation results show the effectiveness of the NMPC-LBF method at ensuring safe navigation of the robots. 

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