Unstructured Underwater Environment Research Articles

Octopus-Inspired Underwater Soft Robotic Gripper with Crawling and Swimming Capabilities.

Can a robotic gripper only operate when attached to a robotic arm? The application space of the traditional gripper is limited by the robotic arm. Giving robot grippers the ability to move will expand their range of applications. Inspired by rich behavioral repertoire observed in octopus, we implement an integrated multifunctional soft robotic gripper with 6 independently controlled Arms. It can execute 8 different gripping actions for different objects, such as irregular rigid/soft objects, elongated objects with arbitrary orientation, and plane/curved objects with larger sizes than the grippers. Moreover, the soft gripper can realize omnidirectional crawling and swimming by itself. The soft gripper can perform highly integrated tasks of releasing, crawling, swimming, grasping, and retrieving objects in a confined underwater environment. Experimental results demonstrate that the integrated capabilities of multimodal adaptive grasping and omnidirectional motions enable dexterous manipulations that traditional robotic arms cannot achieve. The soft gripper may apply to highly integrated and labor-intensive tasks in unstructured underwater environments, including ocean litter collecting, capture fishery, and archeological exploration.

Underwater Loop-Closure Detection for Mechanical Scanning Imaging Sonar by Filtering the Similarity Matrix With Probability Hypothesis Density Filter

Robust and accurate estimation of position and attitude of a UUV (Unmanned Underwater Vehicle) from sonar scans is essential for simultaneous localization and mapping (SLAM). Both dead-reckoning based on the inertial navigation system and the motion parameter estimation based on the registration of the ultrasound scan sequence can contribute to the performance of the system. However, the rapidly-growing accumulated error tends to counteract the precise localization of the vehicle. In this paper, a method for loop-closure detection is proposed that adjusts the accumulated error for the underwater acoustic SLAM when the vehicle scans the underwater environment using an Mechanical Scanning Imaging Sonar (MSIS). Firstly, a similarity matrix for pairs of scans is constructed to represent the loop-closing tracks. In the registration step, two novel features, namely the intensity projection histograms and a polar gradient matrix, are extracted to calculate the translational and rotational parameters respectively. Secondly, the probability hypothesis density (PHD) filter is used to extract the possible loop-closure constraints from the similarity matrix, removing the random noise brought by accidental correlation and refining the concurrent loop-closing tracks resulted from long-range scanning. Lastly, the loop-closure constraints from the refined loop-closing tracks are fed into the GraphSLAM system to adjust the pose of each scan by constraint optimization. Experiments on the MSIS sonar images collected in structured and unstructured underwater environments validate the effectiveness of the proposed loop-closure detection method.

Floating Underwater Manipulation: Developed Control Methodology and Experimental Validation within the TRIDENT Project

This paper presents the control framework that has been proposed and successfully employed within the TRIDENT EU FP7 project, the aim of which is to develop a multipurpose Intervention Autonomous Underwater Vehicle (I-AUV) exhibiting smart manipulation capabilities, for interventions within unstructured underwater environments. In particular, the work focuses on the exploitation of the highly redundant system for achieving a dexterous object grasping, while also satisfying a set of conditions of scalar inequality type to be achieved ultimately. These represent safety and/or operational-enabling conditions for the overall system itself, such as, for instance, respecting joint limits and keeping the object grossly centered in the camera system. Thus the design of a control architecture exhibiting such a property first required an extension of the classical task priority framework, to be performed in such a way as to also account, in a uniform manner, for inequality conditions to be achieved ultimately. Then, following a description on how such an extension has been made, both simulations and experimental trials are successively presented to show how the developed TRIDENT I-AUV system is able to properly exploit all the redundant degrees of freedom for achieving all the established objectives.

A Task Priority and Dynamic Programming Based Approach to Agile Underwater Floating Manipulation

The present paper deals with a specific part of the activities performed within the currently on going EU-FP7 funded project TRIDENT (http://www.irs.uji.es/trident), whose objectives include the development of a multipurpose Intervention Autonomous Underwater Vehicle (I-AUV) exhibiting smart manipulation capabilities within unstructured underwater environments. The considered activities are those developed within WP 5 of the project, aiming to design the functional and algorithmic real-time control architecture (and the relative real-time control software) in charge of coordinating both vehicle and arm motions in a concurrent way; this is done to improve the overall performances via the system agility obtained by reducing at most the need for separate sequential motions, while also guaranteeing the fulfilment of all the enabling and safety system conditions.

On the closed-form covariance estimation for the pIC algorithm

A number of existing methods for estimating the displacement of a robotic vehicle incorporates scan matching algorithms. There are many different scan matching algorithms but only few of them base their estimation on a probabilistic framework. To correctly integrate the scan matching estimate in a Simultaneous Localization And Mapping (SLAM) algorithm under Gaussian assumption, it is needed to know not only the displacement estimation but also its covariance. Robust methods of estimating the covariance are based on the analysis of the cost function being minimized and are independent of the minimization algorithm. In this paper, we study those methods and properly apply them in a closed form to the Probabilistic Iterative Correspondence (pIC) scan matching algorithm. The pIC is the base algorithm used in a previous work where the authors had proposed a pose-based algorithm to solve the full SLAM problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured underwater environment. This approach is verified with Monte Carlo simulations and numerical calculations.

Unsupervised Real-time Obstacle Avoidance Technique based on a Hybrid Fuzzy Method for AUVs

The article presents ARTMAP and Fuzzy BK-Product approach underwater obstacle avoidance for the Autonomous underwater Vehicles (AUV). The AUV moves an unstructured area of underwater and could be met with obstacles in its way. The AUVs are equipped with complex sensorial systems like camera, aquatic sonar system, and transducers. A Neural integrated Fuzzy BK-Product controller, which integrates Fuzzy logic representation of the human thinking procedure with the learning capabilities of neural-networks (ARTMAP), is developed for obstacle avoidance in the case of unstructured areas. In this paper, ARTMAP-Fuzzy BK-Product controller architecture comprises of two distinct elements, are 1) Fuzzy Logic Membership Function and 2) Feed-Forward ART component. Feed-Forward ART component is used to understanding the unstructured underwater environment and Fuzzy BK-Product interpolates the Fuzzy rule set and after the defuzzyfication, the output is used to take the decision for safety direction to go for avoiding the obstacle collision with the AUV. An on-line reinforcement learning method is introduced which adapts the performance of the fuzzy units continuously to any changes in the environment and make decision for the optimal path from source to destination.

An experimental investigation into the fault-tolerant control of an autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) are expected to function in an unstructured underwater environment. One of the main requirements for effective operation in such an environment is to accommodate faults. In this paper, we have investigated a new approach to the allocation of thruster forces of an autonomous underwater vehicle under thruster faults. Generally, an AUV is equipped with more thrusters than what is minimally required to produce the desired motion. The proposed framework exploits the excess number of thrusters to accommodate thruster faults during operation. First, a redundancy resolution scheme is presented that considers the presence of excess number of thrusters along with any thruster faults and determines the reference thruster forces to produce the desired motion. These reference thruster forces are then utilized in the thruster controller to generate the required motion. This approach resolves the thruster redundancy in the Cartesian space and allows the AUV to track the task-space trajectories with asymptotic reduction of the task-space errors. Results from actual underwater experiments are provided to demonstrate the viability of the proposed scheme.