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"Oceanography Observers"
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Towards Automated Annotation of Benthic Survey Images: Variability of Human Experts and Operational Modes of Automation
Global climate change and other anthropogenic stressors have heightened the need to rapidly characterize ecological changes in marine benthic communities across large scales. Digital photography enables rapid collection of survey images to meet this need, but the subsequent image annotation is typically a time consuming, manual task. We investigated the feasibility of using automated point-annotation to expedite cover estimation of the 17 dominant benthic categories from survey-images captured at four Pacific coral reefs. Inter- and intra- annotator variability among six human experts was quantified and compared to semi- and fully- automated annotation methods, which are made available at coralnet.ucsd.edu. Our results indicate high expert agreement for identification of coral genera, but lower agreement for algal functional groups, in particular between turf algae and crustose coralline algae. This indicates the need for unequivocal definitions of algal groups, careful training of multiple annotators, and enhanced imaging technology. Semi-automated annotation, where 50% of the annotation decisions were performed automatically, yielded cover estimate errors comparable to those of the human experts. Furthermore, fully-automated annotation yielded rapid, unbiased cover estimates but with increased variance. These results show that automated annotation can increase spatial coverage and decrease time and financial outlay for image-based reef surveys.
Journal Article
Adaptive robust trajectory tracking control with states-estimation for DJI-F450 quadrotor under multiple unknown disturbances
A quadrotor unmanned aerial vehicle (UAV) must achieve desired flight missions despite internal uncertainties and external disturbances. This paper proposes an adaptive trajectory tracking control method that attenuates unknown uncertainties and disturbances. Although the quadrotor is underactuated, a fully actuated controller is designed using backstepping control. To avoid repeated derivatives of control inputs, a dynamic surface method introduces a filter and auxiliary controller. Lyapunov criteria guide adaptive laws for tuning controller gain and filters. A low-power observer is integrated for state estimation. Additionally, a disturbance observer is developed and combined with the control scheme to handle unknown disturbances. Simulations on a DJI F450 quadrotor demonstrate that the proposed control algorithm offers strong trajectory-tracking performance and system stability under multiple uncertainties and external disturbances during flight.
Journal Article
An Integrated Design of Course-Keeping Control and Extended State Observers for Nonlinear USVs with Disturbances
The integrated design problem of non-fragile controllers and extended state observers (ESOs) for nonlinear unmanned surface vehicles (USVs) under mismatched disturbances is addressed in this paper. First, an integrated model combining the USV system and the rudder system is developed, which includes a second-order underdamped system and a Norrbin nonlinear model incorporating uncertainties. Due to the coupling issues in the design of controllers and observers caused by parameter perturbations or other unmodeled dynamics, an integrated design method, which enables the simultaneous computation of controller gains, observer gains, and disturbance compensation gains, is proposed, effectively addressing these issues. Ultimately, the performance of the designed strategy is verified through a simulation, with the data used in the simulation derived from the real Qingshan USV.
Journal Article
ALOS-Based USV Path-Following Control with Obstacle Avoidance Strategy
Path following and obstacle avoidances are used in heading control and path replanning for unmanned surface vessels (USVs), which have attracted the interest of many researchers over the years. This paper investigates the path-following and obstacle avoidance problems for USVs. First, on the basis of the current position and desired path, an adaptive line-of-sight (ALOS) algorithm is used to obtain the desired heading angle, and the sideslip angle compensation is considered. Then, to ensure that the USV follows the desired path. Model predictive control (MPC) is used to reduce the lateral error. The event-triggered mechanism (ETM) strategy is utilized to reduce the computational cost of MPC. Moreover, to obtain accurate state quantities in real time, a linear extended state observer (LESO) is used to counteract the effects of external disturbances and the nonlinear term of the model. Furthermore, an improved obstacle avoidance algorithm based on the geometric relationship is proposed. This method can better ensure USV navigation safety and reduce consumption and computation. Lastly, multiple simulation experiments illustrate that the algorithm improves the path-following capability and security and ensures smooth input changes by setting input constraints. Therefore, the designed controller has better feasibility and robustness.
Journal Article
Formation Control for UAV-USVs Heterogeneous System with Collision Avoidance Performance
This paper investigates the cooperative formation trajectory tracking problem for heterogeneous unmanned aerial vehicle (UAV) and multiple unmanned surface vessel (USV) systems with collision avoidance performance. Firstly, a formation control protocol based on extended state observer (ESO) is proposed to ensure that the UAV and the USVs track the target trajectory simultaneously in the XY plane. Then, the collision avoidance control strategy of USV formation based on artificial potential field (APF) theory is designed. Specifically, the APF method is improved by reconstructing the repulsive potential field to make the collision avoidance action of USVs more in line with the requirements of International Regulations for Preventing Collisions at Sea (COLREGs). Following that, an altitude controller for the UAV is proposed to maintain the cooperative formation of the heterogeneous systems. Based on the input-to-state stability, the stability of the proposed control structure is proven, and all the signals in the closed-loop system are ultimately bounded. Finally, a simulation study is provided to show the efficacy of the proposed strategy.
Journal Article
Fixed-Time Event-Triggered Sliding Mode Consensus Control for Multi-AUV Formation Under External Disturbances and Communication Delays
This paper addresses the consensus control challenge for multiple autonomous underwater vehicles’ (AUVs) formation operating under external disturbances and communication delays. A fixed-time disturbance observer (FxTDO) is developed to precisely estimate external disturbances within a fixed time. A fixed-time state observer (FxTSO) is designed to reconstruct the leader’s position and velocity states, effectively compensating for communication delays. Building upon these observer estimates, an event-triggered sliding mode controller is proposed to achieve formation consensus with guaranteed convergence time while significantly reducing communication frequency through its triggering mechanism. The entire approach ensures fixed-time convergence of the closed-loop system, and rigorous theoretical proof of this stability is provided. Simulation results confirm the effectiveness of the proposed scheme in handling external disturbances and delays, achieving accurate formation tracking with improved communication efficiency. This work provides a robust solution for multi-AUV coordination in challenging environments.
Journal Article
Disturbance Observer-Based Model Predictive Control for an Unmanned Underwater Vehicle
This work addresses the motion control problem for a 4-degree-of-freedom unmanned underwater vehicle (UUV) in the presence of nonlinear dynamics, parametric uncertainties, system constraints, and time-varying external disturbances. A disturbance observer-based control scheme is proposed, which is structured around the model predictive control (MPC) method integrated with an extended active observer (EAOB). Compared to the conventional disturbance observer, the developed EAOB has the ability to handle both external disturbances and system/measurement noises simultaneously. The EAOB leverages a combination of sensor measurements and a system dynamic model to estimate disturbances in real-time, which allows continuous estimation and compensation of time-varying disturbances back to the controller. The proposed disturbance observer-based MPC is implemented by feeding the estimated disturbances back into the MPC’s prediction model, which forms an effective adaptive controller with a parameter-varying model. The proposed control strategy is validated through simulations in a Gazebo and robot operating system environment. The results show that the proposed method can effectively reject unpredictable disturbances and improve the UUV’s control performance.
Journal Article
Robust Model Predictive Control Based on Active Disturbance Rejection Control for a Robotic Autonomous Underwater Vehicle
This work aims to develop a robust model predictive control (MPC) based on the active disturbance rejection control (ADRC) approach by using a discrete extended disturbance observer (ESO). The proposed technique uses the ADRC approach to lump disturbances and uncertainties into a total disturbance, which is estimated with a discrete ESO and rejected through feedback control. Thus, the effects of the disturbances are attenuated, and a model predictive control is designed based on a canonical model free of uncertainties and disturbances. The proposed control technique is tested through simulation into a robotic autonomous underwater vehicle (AUV). The AUV’s dynamic model is used to compare the performance of a classical MPC and the combined MPC-ADRC. The evaluation results show evidence of the superiority of the MPC-ADRC over the classical MPC under tests of reference tracking, external disturbances rejection, and model uncertainties attenuation.
Journal Article