Ocean Systems Lab

2025

1. 3DSSDF: Underwater 3D Sonar Reconstruction Using Signed Distance Functions

   Simon Archieri, Juliette Drupt, Ahmet Cinar, and 4 more authors

   In 2025 IEEE International Conference on Robotics & Automation (ICRA 2025), 2025

2024

1. MarineGym: Accelerated Training for Underwater Vehicles with High-Fidelity RL Simulation

   Shuguang Chu, Zebin Huang, Mingwei Lin, and 2 more authors

   arXiv preprint arXiv:2410.14117, 2024

2023

1. Enhancing AUV Autonomy With Model Predictive Path Integral Control

   Pierre Nicolay, Yvan Petillot, Mykhaylo Marfeychuk, and 2 more authors

   In OCEANS 2023, 2023

   Abs arXiv

   Autonomous underwater vehicles (AUVs) play a crucial role in surveying marine environments, carrying out underwater inspection tasks, and ocean exploration. However, in order to ensure that the AUV is able to carry out its mission successfully, a control system capable of adapting to changing environmental conditions is required. Furthermore, to ensure the robotic platform’s safe operation, the onboard controller should be able to operate under certain constraints. In this work, we investigate the feasibility of Model Predictive Path Integral Control (MPPI) for the control of an AUV. We utilise a non-linear model of the AUV to propagate the samples of the MPPI, which allow us to compute the control action in real time. We provide a detailed evaluation of the effect of the main hyperparameters on the performance of the MPPI controller. Furthermore, we compared the performance of the proposed method with a classical PID and Cascade PID approach, demonstrating the superiority of our proposed controller. Finally, we present results where environmental constraints are added and show how MPPI can handle them by simply incorporating those constraints in the cost function.

2. Adaptive Heading for Perception-Aware Trajectory Following

   J.S. Willners, S. Katagiri, S. Xu, and 3 more authors

   2023

   Abs

   This paper presents an adaptive heading approach for perception awareness during trajectory following. By adapting the heading of a robot to improve the feature tracking in the current mapped environment, the accuracy in localisation can be improved. This can have a significant advantage for autonomous operations in GPS-denied environments such as subsea or in caves. The aim of the proposed approach is to position the sensor used for perception and feature tracking in such a way that it; obtains a view that contains a good observation of the previously mapped environment, face forward along the direction of travel, reduces the change in heading and view the perceived environment along the surface’s estimated normals. These 4 objectives create a weighted utility function that is used to find the most beneficial heading. The benefit is a system that improves feature tracking for simultaneous localisation and mapping (SLAM) while considering the safety of the robot by being aware of its surrounding. To sense the environment, a simulated sensor is discretised to a set of vertical rays based on the vertical field of view. The vertical rays are swept 360 degrees around a position to evaluate for a new heading. This allows for the simulated sensor data from ray casting to be reused and therefore reduces the computational load to find the heading which maximises the utility function. The paper is focused on holonomic robots capable of controlling the robot’s heading or sensor orientation independently from the position. We present results and evaluation in a simulated environment where we show a great improvement in the SLAM’s pose estimation. In addition, we endow an autonomous underwater vehicle (AUV) with the proposed approach during field trials and present the result in two different environments. © 2023 IEEE.

2022

1. Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments

   E. Pairet, J.D. Hernández, M. Carreras, and 2 more authors

   IEEE Transactions on Automation Science and Engineering, 2022

   Abs

   Safe autonomous navigation is an essential and challenging problem for robots operating in highly unstructured or completely unknown environments. Under these conditions, not only robotic systems must deal with limited localization information but also their maneuverability is constrained by their dynamics and often suffers from uncertainty. In order to cope with these constraints, this article proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety guarantees. The proposed approach deals with the motion, probabilistic safety, and online computation constraints by: 1) incrementally mapping the surroundings to build an uncertainty-aware representation of the environment and 2) iteratively (re)planning trajectories to goal that is kinodynamically feasible and probabilistically safe through a multilayered sampling-based planner in the belief space. In-depth empirical analyses illustrate some important properties of this approach, namely: 1) the multilayered planning strategy enables rapid exploration of the high-dimensional belief space while preserving asymptotic optimality and completeness guarantees and 2) the proposed routine for probabilistic collision checking results in tighter probability bounds in comparison to other uncertainty-aware planners in the literature. Furthermore, real-world in-water experimental evaluation on a nonholonomic torpedo-shaped autonomous underwater vehicle and simulated trials in an urban environment on an unmanned aerial vehicle demonstrate the efficacy of the method and its suitability for systems with limited onboard computational power. Note to Practitioners-Emergent robotic applications require operating in previously unmapped scenarios. This article presents a unified mapping-planning strategy that enables robots to navigate autonomously and safely in harsh environments. © 2004-2012 IEEE.