Due to the challenges regarding the limits of their endurance and autonomous capabilities, underwater docking for autonomous underwater vehicles (AUVs) has become a topic of interest for many academic and commercial applications. Herein, we take on the problem of relative navigation for the generalized version of the docking operation, which we address as proximity operations. Proximity operations typically involve only two actors, a chaser and a target. We leverage the similarities to proximity operations (prox-ops) from spacecraft robotic missions to frame the diverse docking scenarios with a set of phases the chaser undergoes on the way to its target. We emphasize the versatility on the use of factor graphs as a generalized representation to model the underlying simultaneous trajectory estimation and relative navigation (STERN) problem that arises with any prox-ops scenario, regardless of the sensor suite or the agents' dynamic constraints. To emphasize the flexibility of factor graphs as the modeling foundation for arbitrary underwater prox-ops, we compile a list of state-of-the-art research in the field and represent the different scenario using the same factor graph representation. We detail the procedure required to model, design, and implement factor graph-based estimators by addressing a long-distance acoustic homing scenario of an AUV to a moving mothership using datasets from simulated and real-world deployments; an analysis of these results is provided to shed light on the flexibility and limitations of the dynamic assumptions of the moving target. A description of our front- and back-end is also presented together with a timing breakdown of all processes to show its potential deployment on a real-time system. 

Terminal docking is an important step towards long-term underwater residency of Autonomous Underwater Vehicles (AUVs). An important part is to correctly estimate the relative position between the AUV and the docking station. While there are many solutions to this problem, it is unclear how they perform with respect to each other in terms of accuracy and computational performance. We propose a side by side comparison of a Rao-Blackwellized particle filter (RBPF) with a Maximum-A-Posteriori (MAP) method in a vision-based terminal homing scenario. Both methods are evaluated in a simulation study based on performance under different uncertainties. Subsequently, they are validated using real-world data from field tests. The comparison shows that in the simulation study, the smoothing performs more accurate than the RBPF, whereas on the experimental data, they perform equally. However, the smoothing requires less computational power compared to the RBPF.

Autonomous underwater docking is of the utmost importance for expanding the capabilities of Autonomous Underwater Vehicles (AUVs). Due to a historical focus on underwater docking to only static targets, the research gap in underwater docking to dynamically active targets has been left relatively untouched. We address the state estimation problem that arises when trying to rendezvous a chaser AUV with a dynamic target by modeling the scenario as a factor graph optimization-based Simultaneous Localization and Mapping problem. We present a set of boundary factors that aid the inference process by seamlessly transitioning the target’s state between the different observability stages, intrinsic to any dynamic docking scenario. We benchmark the performance of our approach using the Stonefish simulated environment.

Open data are scarce in underwater robotics, making it more difficult for researchers to develop new methods that address real-world problems. In this work, we present the experimental design, execution, and curation of three datasets that represent different conditions in a realistic underwater dynamic proximity operation. The raw datasets gathered during the deployments are post-processed to improve consistency. We detail and use a joint batch optimization technique that uses a probabilistic approach to iteratively search for the set of optimal agent trajectories that are in best agreement with the relative measurements provided by a USBL positioning system and optical pose measurements from a fiducial light array. Error analysis of the relative measurements with respect to the baseline and optimized trajectories validate our results, effectively providing ground-truth trajectories of the agents. The resulting datasets, together with their documentation, are publicly available at github.com/aldoteran/asko_2024_datasets

Estimating a target’s 6-DoF motion in underwater proximity operations is difficult because the chaser lacks target-side proprioception and the available relative observations are sparse, noisy, and often partial (e.g., Ultra-Short Baseline (USBL) positions). Without a motion prior, factor-graph maximum a posteriori estimation is underconstrained: consecutive target states are weakly linked and orientation can drift.    We propose a generalized constant-twist motion prior defined on the tangent space of Lie groups that enforces temporally consistent trajectories across all degrees of freedom; in SE(3) it couples translation and rotation in the body frame. We present a ternary factor and derive its closed-form Jacobians based on standard Lie group operations, enabling drop-in use for trajectories on arbitrary Lie groups.    We evaluate two deployment modes: (A) an SE(3)-only representation that regularizes orientation even when only position is measured, and (B) a mode with boundary factors that switches the target representation between SE(3) and 3D position while applying the same generalized constant-twist prior across representation changes.    Validation on a real-world dynamic docking scenario dataset shows consistent ego-target trajectory estimation through USBL-only and optical relative measurement segments with an improved relative tracking accuracy compared to the noisy measurements to the target. Because the construction relies on standard Lie group primitives, it is portable across state manifolds and sensing modalities.