This article studies the design, modeling, and implementation challenges of a path-following with obstacle avoidance algorithms as Guidance, Navigation, and Control (GNC) architecture of an Unmanned Surface Vehicle (USV) in harbor conditions. First, an effective mathematical model is developed based on System Identification (SI), validating the USV model with field-test data. Then, a guidance system is addressed based on a Line-Of-Sight (LOS) algorithm, which uses a LiDAR as the main perception sensor for the obstacle avoidance algorithm. The GNC architecture uses a modular approach, including obstacle detection, path-following, and control in the USV platform. Finally, an implementation challenge in two control scenarios, simulation and field-test, is addressed to validate the designed GNC architecture.

This article studies the design, modeling, and implementation challenges for a target detection algorithm using multi-sensor technology of a co-operative autonomous offshore system, formed by an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV). First, the study develops an accurate mathematical model of the USV to be included as a simulation environment for testing the guidance, navigation, and control (GNC) algorithm. Then, a guidance system is addressed based on an underwater coverage path for the AUV, which uses a mechanical imaging sonar as the primary AUV perception sensor and ultra-short baseline (USBL) as a positioning system. Once the target is detected, the AUV sends its location to the USV, which creates a straight-line for path following with obstacle avoidance capabilities, using a LiDAR as the main USV perception sensor. This communication in the co-operative autonomous offshore system includes a decentralized Robot Operating System (ROS) framework with a master node at each vehicle. Additionally, each vehicle uses a modular approach for the GNC architecture, including target detection, path-following, and guidance control modules. Finally, implementation challenges in a field test scenario involving both AUV and USV are addressed to validate the target detection algorithm.

This paper presents a high performance autonomous underwater robot under development for inspection of flooded mines up to 500 meters depth. Underwater robots have multiple advanced subsystems and mechanisms. Initial structure design of subsystems and their functions are demonstrated here briefly. In addition, Hydrodynamic coefficients that contribute to robot equation of motion are addressed. The advantage of utilizing spherical design is verified by applying simplification to determine the main coefficient of motion theoretically.

Efficient suction of slurry is crucial in the mining industry where multiphase flows of different volume fractions and particle sizes are encountered. Annular pet pump (AJP) is popular for slurry suction due to its simple design and lack of moving parts. This parametric study comprehensively investigates the slurry transport dynamics within a modular annular jet pump using a .multiphase mixture model with the realizable k–ϵ turbulence model. Here, the multiphase mixture model incorporates the interactions between solid particles (sand) and carrier fluid (water), and a parametric study is conducted to study the effects of input volumetric flow rate, nozzle convergence angle, particle size, and solid volume fraction on slurry transport efficiency. The computational fluid dynamic simulations are conducted to predict the performance characteristics, which are then validated against literature data. The realizable k–ɛ turbulence model is implemented for the turbulence modeling. The pressure drop is compared with the literature's result of slurry transport through pipelines. The results demonstrate the effectiveness of the AJP in various operational conditions, providing insights into good operation for enhanced slurry transport in efficient mining. This study contributes to an interpretation of slurry transport in complex pump systems, with implications for applications in mining, dredging, and other industries where efficient material transport is critical.

This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture.

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