The method of formation of AUV smooth trajectory in unknown environment

Filaretov, Vladimir; Yukhimets, Dmitry

doi:10.1109/oceansap.2016.7485655

Cited by 7 publications

(3 citation statements)

References 10 publications

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“…For example, in [38,39], light detection and ranging (LiDAR) sensors or stereoscopic cameras are used in obstacle avoidance control of robots. In [37,40], sonar is used as the sensor to detect obstacles for obstacle avoidance control of underwater robots. In this paper, the AUV is configured with sonaras shown in Figure 2 received data ρ, it means there is an obstacle ahead.…”

Section: Obstacle Detection and Calculation Of The Penalty Term For Omentioning

confidence: 99%

“…However, this method needs to obtain the global information of obstacles in real-time, and it has high requirements for sensor configuration. In [36,37], based on the obstacle information detected by the onboard sensors, a spline curve is applied to replan the original path near the obstacle, and real-time obstacle avoidance is realized. However, replanning the path in real time greatly increases the computational burden of the controller.…”

Section: Introductionmentioning

confidence: 99%

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Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Yao

Wang

et al. 2020

Sensors

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This paper studies three-dimensional (3D) straight line path following and obstacle avoidance control for an underactuated autonomous underwater vehicle (AUV) without lateral and vertical driving forces. Firstly, the expected angular velocities are designed by using two different methods in the kinematic controller. The first one is a traditional method based on Line-of-sight (LOS) guidance law, and the second one is an improved method based on model predictive control (MPC). At the same time, a penalty item is designed by using the obstacle information detected by onboard sensors, which can realize the real-time obstacle avoidance of the unknown obstacle. Then, in order to overcome the uncertainty of the dynamics model and the saturation of actual control input, the dynamic controller is designed by using sliding mode control (SMC) technology. Finally, in the simulation experiment, the performance of the improved control method is verified by comparison with two traditional control methods based on LOS guidance law. Since the constraint of an AUV’s angular velocities are considered in MPC, simulation results show that the improved control method uses MPC, and SMC not only improves the tracking quality of the AUV when switching paths near the waypoints and realizes real-time obstacle avoidance but also effectively reduces the mean square error (MSE) and saturation rate of the rudder angle. Therefore, this control method is more conducive to the system stability and saves energy.

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Section: Obstacle Detection and Calculation Of The Penalty Term For Omentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Yao

Wang

et al. 2020

Sensors

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“…Motion of Robotino robot in an environment with obstaclesBefore performing the work, students study the theory and publications prepared by the team of the Department of Automation and Control of the FEFU in the process of real scientific projects in Russian and English[8][9][10]. The fulfilment of technological operations with the use of vision systems in the presence of unknown deformations in the process of fixing the workpiece before machining.…”

mentioning

confidence: 99%

Features of development and using interuniversity scientific and educational network «SYNERGY» for training of engineers in the field of automation, robotics and mechatronic systems

Bobryakov¹,

Filaretov²,

Homchenko³

et al. 2018

EAI Endorsed Transactions on Energy Web

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This paper describes a features of development and using of interuniversity scientific and educational network «Synergy» for training of engineers in the field of automation, robotics and mechatronic systems. This network provides: 1) giving of distance lectures by leading professors; 2) carrying out of laboratory classes with remote using laboratory facilities; 3) remote education on project design based on results received during real scientific and commercial projects; 4) practices on laboratory and industrial equipment. Also in paper, some features of software and hardware implementation for remote work with laboratory and industrial equipment in the network is described. Several examples of laboratory work and project design by students remotely implemented using the network is presented.

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PID Based Path Follower for the MiddleAUV on the Test Polygon

Lipko

2024

Lecture Notes in Electrical Engineering

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The method of formation of AUV smooth trajectory in unknown environment

Cited by 7 publications

References 10 publications

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Path Following Based on Waypoints and Real-Time Obstacle Avoidance Control of an Autonomous Underwater Vehicle

Features of development and using interuniversity scientific and educational network «SYNERGY» for training of engineers in the field of automation, robotics and mechatronic systems

PID Based Path Follower for the MiddleAUV on the Test Polygon

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