Formation Control of Multiple Autonomous Underwater Vehicles under Communication Delay, Packet Discreteness and Dropout

Li, Liang; Li, Yiping; Zhang, Yuexing; Xu, Gaopeng; Zeng, Jian; Feng, Xisheng

doi:10.3390/jmse10070920

Cited by 20 publications

(11 citation statements)

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“…In the future, we will verify the proposed net avoidance controls by doing experiments with real underwater robots. Also, in the future, we will extend the proposed controls to multi-robot systems [ 54 , 55 , 56 ], so that a group of multiple underwater robots can move towards a goal while avoiding underwater nets.…”

Section: Discussionmentioning

confidence: 99%

Camera-Based Net Avoidance Controls of Underwater Robots

Kim

2024

Sensors

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Fishing nets are dangerous obstacles for an underwater robot whose aim is to reach a goal in unknown underwater environments. This paper proposes how to make the robot reach its goal, while avoiding fishing nets that are detected using the robot’s camera sensors. For the detection of underwater nets based on camera measurements of the robot, we can use deep neural networks. Passive camera sensors do not provide the distance information between the robot and a net. Camera sensors only provide the bearing angle of a net, with respect to the robot’s camera pose. There may be trailing wires that extend from a net, and the wires can entangle the robot before the robot detects the net. Moreover, light, viewpoint, and sea floor condition can decrease the net detection probability in practice. Therefore, whenever a net is detected by the robot’s camera, we make the robot avoid the detected net by moving away from the net abruptly. For moving away from the net, the robot uses the bounding box for the detected net in the camera image. After the robot moves backward for a certain distance, the robot makes a large circular turn to approach the goal, while avoiding the net. A large circular turn is used, since moving close to a net is too dangerous for the robot. As far as we know, our paper is unique in addressing reactive control laws for approaching the goal, while avoiding fishing nets detected using camera sensors. The effectiveness of the proposed net avoidance controls is verified using simulations.

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Section: Discussionmentioning

confidence: 99%

Camera-Based Net Avoidance Controls of Underwater Robots

Kim

2024

Sensors

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“…However, acoustic communication also has non-negligible limitations, including delay, path loss, limited bandwidth, multipath and so on [8,9]. In practice, the propagation delay is related to the speed and distance of propagation [10]. The propagation speed of radio waves in air is equivalent to the speed of light.…”

Section: Introductionmentioning

confidence: 99%

A High-Precision Positioning Method for Autonomous Underwater Vehicles with Communication Delays

Li,

Chen

et al. 2024

Electronics

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In underwater navigation of autonomous underwater vehicles (AUVs), communication delays frequently occur, leading to a reduction in positioning accuracy. To mitigate this challenge, this work introduces a novel method for relative angle correction, aiming to reconstruct measurement information. Initially, Doppler measurement data are assimilated into the reconstruction of measurement equations to determine the relative angle between the AUV and the observatory. Subsequently, the obtained angle information is integrated into the Extended Kalman Filter (EKF) for the reconstruction of measurement equations. The proposed method effectively reduces positioning errors caused by hydroacoustic communication delays, consequently enhancing AUV positioning accuracy. The efficacy of the proposed method is demonstrated through a simulation study. Simulation results reveal that the incorporation of Doppler angle correction in the reconstructed measurement information method significantly decreases the localization error by approximately 50% compared to EKF and by around 20% compared to the method lacking angle correction.

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“…This attracted attention in the above-mentioned study [20], and a solution was found by combining measurement data from acoustic sensors with information from other sensors in the onboard inertial navigation system. A more typical way to deal with delayed observations is to estimate the delay time and consider it by adjusting the usual filter [35]. Interestingly, the delay in observations is explained by nature not only for underwater observations but also in some chemical processes [36].…”

Section: Introductionmentioning

confidence: 99%

Tracking a Maneuvering Object by Indirect Observations with Random Delays

Bosov

2023

Drones

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A mathematical model for the target tracking problem is proposed. The model makes it possible to describe conditions when the time for an observer to receive the results of indirect observations of a moving object depends not only on the state of the observation environment but also on the state of the object itself. The source of such a model is the observation process, by stationary means, of an autonomous underwater vehicle, in which the time for obtaining up-to-date data depends on the unknown distance between the object and the observer. As part of the study of the problem, the equations of the optimal Bayesian filter are obtained. But this filter is not possible to implement. For practical purposes, it is proposed to use the conditionally minimax nonlinear filter, which has shown promising results in other complex tracking models. The conditions for the filter’s evaluation and its accuracy characteristics are given. A large-scale numerical experiment illustrating the filter’s operation and the observation system’s features with random delays are described.

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Formation Control of Multiple Autonomous Underwater Vehicles under Communication Delay, Packet Discreteness and Dropout

Cited by 20 publications

References 50 publications

Camera-Based Net Avoidance Controls of Underwater Robots

Camera-Based Net Avoidance Controls of Underwater Robots

A High-Precision Positioning Method for Autonomous Underwater Vehicles with Communication Delays

Tracking a Maneuvering Object by Indirect Observations with Random Delays

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