Autonomous docking of hovering type AUV to seafloor charging station based on acoustic and visual sensing

In this paper we present an extension to the hybrid A* (HA*) path planner. This extension allows autonomous underwater vehicle (AUVs) to plan paths in 3-dimensional (3D) environments. The proposed approach enables the robot to operate in a safe manner by accounting for the vehicle’s motion constraints, thus avoiding collisions and ensuring that the calculated paths are feasible. Secondly, we propose an improvement for operations in unexplored or partially known environments by endowing the planner with a tree pruning procedure, which maintains a valid and feasible search-tree during operation. When the robot senses new obstacles in the environment that invalidate its current path, the planner prunes the tree of branches which collides with the environment. The path planning algorithm is then initialised with the pruned tree, enabling it to find a solution in a lower time than replanning from scratch. We present results obtained through simulation which show that HA* performs better in known underwater environments than compared algorithms in regards to planning time, path length and success rate. For unknown environments, we show that the tree pruning procedure reduces the total planning time needed in a variety of environments compared to running the full planning algorithm during replanning.

“…HA* allows states to have continuous values within the cells as can be seen in Figure 1c. It has mainly been used for terrestrial vehicles operating in SE (2).…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online 3-Dimensional Path Planning with Kinematic Constraints in Unknown Environments Using Hybrid A* with Tree Pruning

Willners

Gonzalez-Adell

Hernández

et al. 2021

“…Positional errors of optical sensors, depending on the algorithms, could be less than 0.01 m [16,18]. Taking advantage of their positional accuracy, optical sensors are usually in charge of the final short-range stage precise positioning and combined with acoustic sensors [13,15,19], which are good in sensing range, but bad at resolution. Following this strategy, in this work, we also combined acoustic and optical sensors for AUV recovery.…”

Section: Introductionmentioning

confidence: 99%

“…A landmark is viewed as a feature point. A points-based feature was leveraged in [1,3,4,9,16,19,21] for pose estimation. Perspective-n-points (PnP) algorithms can accurately solve points-based pose estimation, but a drawback of PnP algorithms is that they fail in estimating poses in cases where not all predefined landmarks are detected, namely incomplete observations.…”

Section: Introductionmentioning

confidence: 99%

Visual Navigation for Recovering an AUV by Another AUV in Shallow Water

Liu

Yang

et al. 2019

Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.

“…The docking stage, which is also called the terminal guidance stage, is important but difficult because the AUV needs to steer into the docking station precisely under certain guidance. Several methodologies address docking guidance, and these include optical or visual guidance [2,4,5,6,7,8,9], acoustic guidance [10,11,12,13,14,15], electromagnetic (EM) guidance [16,17], electric sense [18], and different combinations of these techniques [19,20,21,22,23].…”

Section: Introductionmentioning

confidence: 99%

A Low-cost Electromagnetic Docking Guidance System for Micro Autonomous Underwater Vehicles

Peng

Liu

et al. 2019

As important observational platforms for the Smart Ocean concept, autonomous underwater vehicles (AUVs) that perform long-term observation in fleets are beneficial because they provide large-scale sampling data with a sufficient spatiotemporal resolution. Therefore, a large number of low-cost micro AUVs with docking capability for power recharge and data transmission are essential. This study designed a low-cost electromagnetic docking guidance (EMDG) system for micro AUVs. The EMDG system is composed of a transmitter coil located on the dock and a three-axial search coil magnetometer acting as a receiver. The search coil magnetometer was optimized for small sizes while maintaining sufficient sensitivity. The signal conditioning and processing subsystem was designed to calculate the deflection angle (β) for docking guidance. Underwater docking tests showed that the system can detect the electromagnetic signal and successfully guide AUV docking. The AUV can still perform docking in extreme positions, which cannot be realized through normal optical or acoustic guidance. This study is the first to focus on the EM guidance system for low-cost micro AUVs. The search coil sensor in the AUV is inexpensive and compact so that the system can be equipped on a wide range of AUVs.