Mechanical Design of an Autonomous Marine Robotic System for Interaction With Divers

Stilinović, Nikola; Marković, Milan; Mišković, Nikola; Vukić, Zoran; Vasilijević, Antonio

doi:10.21278/brod67305

Cited by 3 publications

(4 citation statements)

References 12 publications

(7 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to deepen the understanding of the ocean and better develop and protect the ocean, a large number of marine equipment are used for ocean observation and development. These widely used marine equipment mainly include marine survey vessels, Remotely Operated Vehicles (ROVs) [1,2], Autonomous Underwater Gliders (AUGs) [3], AUVs [4,5,6], USVs [7,8,9], etc. The positive buoyancy diving autonomous vehicle [10] is proposed, which combines the features of USV and AUV and better meets the needs of surface and underwater measurement and monitoring in the same time scale.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Optimal Control of a Positive Buoyancy Diving Autonomous Vehicle

Wei

Cao

et al. 2023

Brodogradnja

View full text Add to dashboard Cite

The positive buoyancy diving autonomous vehicle combines the features of an Unmanned Surface Vessel (USV) and an Autonomous Underwater Vehicle (AUV) for marine measurement and monitoring. It can also be used to study reasonable and efficient positive buoyancy diving techniques for underwater robots. In order to study the optimization of low power consumption and high efficiency cruise motion of the positive buoyancy diving vehicle, its dynamic modeling has been established. The optimal cruising speed for low energy consumption of the positive buoyancy diving vehicle is determined by numerical simulation. The Linear Quadratic Regulator (LQR) controller is designed to optimize the dynamic error and the actuator energy consumption of the vehicle in order to achieve the optimal fixed depth tracking control of the positive buoyancy diving vehicle. The results demonstrate that the LQR controller has better performance than PID, and the system adjustment time of the LQR controller is reduced by approximately 56% relative to PID. The motion optimization control method proposed can improve the endurance of the positive buoyancy diving vehicle, and has a certain application value.

show abstract

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling and Optimal Control of a Positive Buoyancy Diving Autonomous Vehicle

Wei

Cao

et al. 2023

Brodogradnja

View full text Add to dashboard Cite

show abstract

“…The BUDDY AUV was developed specifically for the purpose of exploring human-robot interaction in the underwater environment [19]. It is fully-actuated in the horizontal plane and can independently control heave and pitch degree of freedoms (DoFs).…”

Section: Introductionmentioning

confidence: 99%

“…The basic payload includes: Additional components include exteroceptive sensors such as stereo and mono camera, forward looking multibeam imaging sonar (FLS), an acoustic positioning system and a tablet mounted in the underwater housing for visual feedback. More details about other components are available in [19].…”

Section: Introductionmentioning

confidence: 99%

Using Autonomous Underwater Vehicles for Diver Tracking and Navigation Aiding

Nađ

Mandić

Mišković

2020

JMSE

Self Cite

View full text Add to dashboard Cite

SCUBA diving activities are classified as high-risk due to the dangerous environment, dependency on technical equipment that ensures life support, reduced underwater navigation and communication capabilities all of which compromise diver safety. While autonomous underwater vehicles (AUVs) have become irreplaceable tools for seabed exploration, monitoring, and mapping in various applications, they still lack the higher cognitive capabilities offered by a human diver. The research presented in this paper was carried out under the EU FP7 “CADDY—Cognitive Autonomous Diving Buddy”. It aims to take advantage of both human diver and AUV complementary traits by making their synergy a potential solution for mitigation of state of the art diving challenges. The AUV increases diver safety by constantly observing the diver, provides navigation aiding by directing the diver and offers assistance (e.g., lights, tool fetching, etc.). The control algorithms proposed in the paper provide a foundation for implementing these services. These algorithms use measurements from stereo-camera, sonar and ultra-short baseline acoustic localization to ensure the vehicle constantly follows and observes the diver. Additionally, the vehicle maintains a relative formation with the diver to allow observation from multiple viewpoints and to aid underwater navigation by pointing towards the next point of interest. Performance of the proposed algorithms is evaluated using results from pool experiments.

show abstract

“…Deprived of a tether that would connect them to ships, the working state of autonomous underwater vehicles (AUVs) is difficult to observe in the underwater environment; therefore, AUVs must possess enough autonomy to accomplish missions [1]. For such vehicles to be capable of accomplishing missions, many advanced control systems are being widely applied to the guidance and control of these unmanned vehicles [2] [3].…”

Section: Introductionmentioning

confidence: 99%

Propeller Fault Diagnosis Based on a Rank Particle Filter for Autonomous Underwater Vehicles

He¹,

Li²,

Jiang³

et al. 2018

brod

View full text Add to dashboard Cite

SummaryRank particle filtering was applied to fault diagnosis technology. Control force and yaw moment losses that occurred in the corresponding degrees of freedom were estimated, and the trends of change were calculated by the multi-step-ahead prediction process in the rank particle filter. Based on the estimated results in the normal state, the situations involving failure were detected. To achieve this target, a mathematical model of the normal and faulty motion states of an underwater vehicle was first developed. Subsequently, the rank sample method combined with the particle filter was used, to obtain the importance probability density function of the autonomous underwater vehicle status. The rank particle filter obtained above realized the real-time state estimation and trend prediction of the motion state. A modified Bayesian algorithm was used to process the estimated control force and yaw moment losses in a given length of time. Based on the calculation results in the normal situations, a back propagation neural network was trained to obtain the diagnostic values. The condition of the propellers was determined based on the diagnostic values. Fault diagnosis simulation experiments were carried out using both the data obtained from the semi-physical simulation system with a hypothetical propeller failure, and the real sea trail data to verify the performance of the proposed algorithm. The results showed that the rank particle filter method could be applied to the propeller fault diagnosis of autonomous underwater vehicles, and solved the problem that arises when a single degree of freedom of the loss is utilized.

show abstract

Mechanical Design of an Autonomous Marine Robotic System for Interaction With Divers

Cited by 3 publications

References 12 publications

Dynamic Modeling and Optimal Control of a Positive Buoyancy Diving Autonomous Vehicle

Dynamic Modeling and Optimal Control of a Positive Buoyancy Diving Autonomous Vehicle

Using Autonomous Underwater Vehicles for Diver Tracking and Navigation Aiding

Propeller Fault Diagnosis Based on a Rank Particle Filter for Autonomous Underwater Vehicles

Contact Info

Product

Resources

About