Control and visual navigation for unmanned underwater vehicles

Ph. D. Thesis.Control and navigation systems are key for any autonomous robot. Due to environmental disturbances, model uncertainties and nonlinear dynamic systems, reliable functional control is essential and improvements in the controller design can significantly benefit the overall performance of Unmanned Underwater Vehicles (UUVs). Analogously, due to electromagnetic attenuation in underwater environments, the navigation of UUVs is always a challenging problem. In this thesis, control and navigation systems for UUVs are investigated. In the control field, four different control strategies have been considered: Proportional-Integral-Derivative Control (PID), Improved Sliding Mode Control (SMC), Backstepping Control (BC) and customised Fuzzy Logic Control (FLC). The performances of these four controllers were initially simulated and subsequently evaluated by practical experiments in different conditions using an underwater vehicle in a tank. The results show that the improved SMC is more robust than the others with small settling time, overshoot, and error. In the navigation field, three underwater visual navigation systems have been developed in the thesis: ArUco Underwater Navigation systems, a novel Integrated Visual Odometry with Monocular camera (IVO-M), and a novel Integrated Visual Odometry with Stereo camera (IVO-S). Compared with conventional underwater navigation, these methods are relatively low-cost solutions and unlike other visual or inertial-visual navigation methods, they are able to work well in an underwater sparse-feature environment. The results show the following: the ArUco underwater navigation system does not suffer from cumulative error, but some segments in the estimated trajectory are not consistent; IVO-M suffers from cumulative error (error ratio is about 3 - 4%) and is limited by the assumption that the “seabed is locally flat”; IVO-S suffers from small cumulative errors (error ratio is less than 2%). Overall, this thesis contributes to the control and navigation systems of UUVs, presenting the comparison between controllers, the improved SMC, and low-cost underwater visual navigation methods

An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately

An Underwater Positioning System for UUVs Based on LiDAR Camera and Inertial Measurement Unit

Underwater positioning presents a challenging issue, because of the rapid attenuation of electronic magnetic waves, the disturbances and uncertainties in the environment. Conventional methods usually employed acoustic devices to localize Unmanned Underwater Vehicles (UUVs), which suffer from a slow refresh rate, low resolution, and are susceptible to the environmental noise. In addition, the complex terrain can also degrade the accuracy of the acoustic navigation systems. The applications of underwater positioning methods based on visual sensors are prevented by difficulties of acquiring the depth maps due to the sparse features, the changing illumination condition, and the scattering phenomenon. In the paper, a novel visual-based underwater positioning system is proposed based on a Light Detection and Ranging (LiDAR) camera and an inertial measurement unit. The LiDAR camera, benefiting from the laser scanning techniques, could simultaneously generate the associated depth maps. The inertial sensor would offer information about its altitudes. Through the fusion of the data from multiple sensors, the positions of the UUVs can be predicted. After that, the Bundle Adjustment (BA) method is used to recalculate the rotation matrix and the translation vector to improve the accuracy. The experiments are carried out in a tank to illustrate the effects and accuracy of the investigated method, in which the ultra-wideband (UWB) positioning system is used to provide reference trajectories. It is concluded that the developed positioning system is able to estimate the trajectory of UUVs accurately, whilst being stable and robust

Dioscorea Bulbifera var. Albotuberosa (Dioscoreaceae), A New Variety from Yunnan, China

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An Underwater Visual Navigation Method Based on Multiple ArUco Markers

Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equipment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately

A low-cost visual inertial odometry system for underwater vehicles

The localization is a crucial issue for underwater vehicles. In the paper, a lightweight visual-inertial odometry is proposed. With dual inertial sensors giving the information of acceleration and attitude, an optical camera providing the seabed images where feature points are tracked by an optical flow algorithm, linear motion of the vehicle can be obtained by computing coordinate transformations and in the fusion section, the control input is also considered. The computational complexity of the proposed method is reduced dramatically relative to other methodologies, and the optical flow algorithm can allow the system to work in poor context environment conditions. The results evaluated by practical experiments show that the method is an effective, low-cost solution for underwater localization. </p

An Integrated Visual Odometry System With Stereo Camera for Unmanned Underwater Vehicles

Navigation is a challenging problem in the area of underwater unmanned vehicles, due to the significant electronmagnetic wave attenuation and the uncertainties in underwater environments. The conventional methods, mainly implemented by acoustic devices, suffer limitations such as high cost, terrain effects and low refresh rate. In this paper, a novel low-cost underwater visual navigation method, named Integrated Visual Odometry with a Stereo Camera (IVO-S), has been investigated. Unlike pure visual odometry, the proposed method fuses the information from inertial sensors and a sonar so that it is able to work in context-sparse environments. In practical experiments, the vehicle was operated to follow specific closed-loop shapes. The Integrated Visual Odoemtry with Monocular Camera (IVO-M) method and other popular open source Visual SLAMs (Simultaneous Localisation and Mappings), such as ORB-SLAM2 and VINS-Mono, have been used to provide comparative results. The cumulative error ratio is used as the quantitative evaluation method to analyse the practical test results. It is shown that the IVO-S method is able to work in underwater sparse-feature environments with high accuracy, whilst also being a low cost solution. </p

A comparison of functional control strategies for underwater vehicles: theories, simulations and experiments

Functional control is key for any autonomous robot, linking high-level artificial intelligence with the robot actuators. Due to environmental disturbances, model uncertainties and nonlinear dynamic systems, reliable functional control is essential and improvements in the controller design can significantly benefit the overall vehicle performance. Even though there are many published studies considering the design of various advanced controllers, most of them are not evaluated in physical experiments. In this research, four different control strategies have been investigated: Proportional-Integral-Derivative Control (PID), Sliding Mode Control (SMC), Backstepping Control (BC) and Fuzzy Logic Control (FLC). The performances of these four controllers were simulated initially and evaluated by practical experiments in different conditions, including various environmental disturbances and hydrodynamic coefficients. The main contributions are as follows: Firstly, this paper reports a comparison study between different types of controllers based on simulations and physical experiments in various conditions; Secondly, this paper provides an improved SMC algorithm combining the merits from linear control and nonlinear control, and a customized second-order FLC method.</p

An underwater visual navigation method based on multiple ArUco markers

Underwater navigation presents crucial issues because of the rapid attenuation of electronic magnetic waves. The conventional underwater navigation methods are achieved by acoustic equip-ment, such as the ultra-short-baseline localisation systems and Doppler velocity logs, etc. However, they suffer from low fresh rate, low bandwidth, environmental disturbance and high cost. In the paper, a novel underwater visual navigation is investigated based on the multiple ArUco markers. Unlike other underwater navigation approaches based on the artificial markers, the noise model of the pose estimation of a single marker and an optimal algorithm of the multiple markers are developed to increase the precision of the method. The experimental tests are conducted in the towing tank. The results show that the proposed method is able to localise the underwater vehicle accurately.</p