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Waypoint Tracking Control for Autonomous Mobile Sampling and Dissolved Oxygen Enrichment of Unmanned Surface Vehicle

Waypoint Tracking Control for Autonomous Mobile Sampling and Dissolved Oxygen Enrichment of... Hindawi Journal of Robotics Volume 2022, Article ID 3652329, 10 pages https://doi.org/10.1155/2022/3652329 Research Article WaypointTrackingControlforAutonomousMobileSamplingand Dissolved Oxygen Enrichment of Unmanned Surface Vehicle 1,2 1 3 Jian Yuan , Hailin Liu, and Wenxia Zhang Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao, China Key Laboratory of Ocean Observation Technology, MNR, Beijing, China Department of Mechanical and Electrical Engineering, Qingdao City University, Qingdao, China Correspondence should be addressed to Jian Yuan; yuanjian@qlu.edu.cn Received 1 March 2022; Accepted 18 March 2022; Published 31 March 2022 Academic Editor: L. Fortuna Copyright © 2022 Jian Yuan et al. (is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An autonomous monitoring and control system of unmanned surface vehicle (USV) with mobile water quality monitoring, sampling, and oxygenation functions is constructed. (e control hardware and monitoring configuration software of the system is designed, respectively, which can be installed on USV and its remote control and monitoring terminal. (e kinematic modeling of USV, waypoint trajectory-tracking control, distributed controller, simulation of tracking control, and verification of software and hardware design are carried out. In order to reject the system noise and external noise, a states estimation method with fully observable states is considered in the control law design. (e software and hardware are also implemented to verify the ef- fectiveness of the monitoring platform. (rough setting a series of monitoring target points and monitoring parameters in the configuration software of the remote user terminal or in the APP of the mobile user terminal, the USV can realize the automatic cruise monitoring using an autonomous navigation and tracking control algorithm, and quantitative water sampling collection. (e reliability of the system is verified by the experiment of the shore test station, and the waypoint trajectory tracking and sensors data are replaying in a logview GUI of MOOS-Ivp and APP. unmanned boat does not have the automatic oxygen en- 1. Introduction richment and fixed depth water sampling function, which (e online monitoring system of water quality can monitor does not have the autonomous navigation and control the water quality all day. (e data from the water quality ability. (e fixed-point jetting mode is often used in the monitoring system can reflect the water quality or water water body aerator, which has great disturbance to the water pollution, and the system can provide reliable data for water body. (e single monitoring, sampling, and aeration environmental management [1]. At present, the monitoring equipment cannot complete a large range of monitoring and equipment of water quality on the market mostly are usually water sample collection of water area in a short time, so a in a shore monitoring station for a fixed-point monitoring in networked monitoring mobile platform containing many the water, which has not the mobile monitoring capability USVs can be used for urgent monitoring. (e researchers for a large area of water surface. Currently, the water quality from the University of California, Berkeley, has developed a monitoring technology can be carried out manually, but it floating cylindrical device to monitor the water quality [5]; It lacks autonomous, frequent, and efficient monitoring has sensors installed at the device bottom and sends sensors schemes [2, 3] and various USV platforms [4]. Recently, the information wirelessly. It has the ability of positioning itself remote-control unmanned boat with sensor monitoring is with GPS device and moving around with small propellers designed to realize the water surface monitoring, but the powered by lithium-ion batteries. Kaizu et al. has developed 2 Journal of Robotics reversed and forward. (e USV body changes the course of an unmanned hovercraft for water quality mapping, which can collect temperature, dissolved oxygen, turbidity, con- rotation moment caused by the different thrust generated by two culvert thrusters, which has good maneuverability and ductivity, pH, and chlorophyll data [6]. For water real-time monitoring and control, the integration of microfluidic chip maneuverability. (e USV has functions of PWM motor and micro-optical system suggests possibilities. In [7], for speed control, automatic heading keeping, waypoint the spatially distributed characterization of microfluidic tracking, motion attitude monitoring, remote data com- two-phase phenomena, the authors present a polymeric munication, water quality sensing, and sampling. (e USV micro-optical system that consists of two coupled minia- has the characteristics of stable hull structure, lightweight, turized devices. In [8], the authors proposed a low-cost, flexible control, and long endurance. At the same time, it can realize the expandable design of measurement module to completely open source, USV for real-time measurement and detection for the surface water quality in complex meet the requirements of multilevel, diversified, and various advanced control algorithm verification experiments and environments. (e platform is equipped with various water sensors to collect pH, turbidity, and temperature data for teaching. actual water quality investigation. Trajectory-tracking con- trol of USV waypoint has important theoretical and engi- 2. Design of Control and Monitoring System neering significance for the water quality monitoring of USV [9]. However, because of nonholonomic constraints, the (e self-powered, oxygen enrichment control system for USV control system does not satisfy the Brockett necessary water surface movement monitoring contains two subsys- condition for stable control [10]. (erefore, Kanayama et al. tems, that is, the power supply electronic subsystem and the adopts Taylor linearization and dynamic feedback lineari- data communication and monitoring control subsystem. zation methods to design a stable tracking controller based (e control system consists of the control and navigation on tracking error model [11]. Jiang and Nijmeujer designed a system for motion control and the data communication and trajectory-tracking control law using the backstepping monitoring control system for water sampling. (e former is methodwhich solved both the local and global tracking working for trajectory-tracking and motion control, and the control problems [12]. In [13], the kinematic and dynamic latter is designed for water sampling and monitoring. (e models are first transformed into a unified standard form, structure of the monitoring system is shown in Figure 1. (e and then, a new dynamic tracking controller is proposed to monitoring system is divided into control terminal on solve the global tracking problem of the nonholonomic USV. working ship and the USV control terminal. (e USV In [14], a global tracking control law is constructed by using terminal consists of main controller, motion controller, Lyapunov direct method, and the nonholonomic constraint driver module, thrust, navigation sensors, and other con- control is solved. In [15, 16], the authors proposed the troller terminals. adaptive neural network control method to design the (e data communication and monitoring system for tracking control law. In [17], the authors presented a der- water sampling consists of a remote upper computer or ivation of autonomous control algorithm, which can reg- mobile phone and a lower computer installed in the USV, as ulate the vehicle speed to a time-varying reference speed to shown in Figure 2. (e lower computer is composed of Delta improve the forward speed control. For open-loop operation PLC and analog quantity expansion module, intermediate mode of outdoor diving with wind speed limit, Roberts Luke relay, aeration device, water quality sampling equipment, et al. [18] used a low-power on-board processor and pro- and mps-400 water quality sensor unit, which completes the posed a calculation model with an accuracy of 5 m. real-time data collection of dissolved oxygen, turbidity, To realize a large of water monitoring in a short time, we temperature, pH, and conductivity water quality sensors. design a self-powered oxygen enrichment control system Both a computer and a mobile phone can be used as the that can be installed in USV cabin, and the USV can cruise upper control equipment. (e software installed on the independently according to the monitoring points of users, computer is mainly composed of the configuration software also can realize the real-time monitoring of water temper- of Delta, SQLServer2005, and OPC server provided by the ature, pH, turbidity, oxygen, and conductivity of the GiantControl Co. Ltd. (rough the real-time configuration, monitoring points. Meanwhile, the monitoring and control the user can master whether the operation status of the system can collect water sampling. (e control system in- aeration pump, push rod, and sensor is normal. (e cludes a photovoltaic module, a remote communication and alarming threshold and aeration time of dissolved oxygen monitoring module, a sensor module, an aeration module, can be set in the upper computer. (e communication and a water sample collection module. (e system can run between the upper and lower computers is carried out autonomously according to the dissolved oxygen value. (e through the data transmission module GRM DTU203G. (e reliability of the system is verified by the experiment of the remote monitoring and control can also be carried out by a shore test station. It can be applied to the water quality mobile APP accessing the cloud server. (e data collected by monitoring of a large range of shore surface aquaculture. It the system can be stored in the database of the upper can be applied to the water quality monitoring of a large computer and the cloud server at the same time, and the range of shore surface aquaculture. Furthermore, we develop synchronization control and display can be realized. Because a double-pushing USV with electric propulsions for mon- of the 4G Internet of (ings mode, the system can realize the itoring. Its thrusters consist of two culvert thrusters sym- networked monitoring of multiple terminals and realize the metrically arranged at the bottom of the USV, which can be integrated monitoring and task scheduling for multiple Journal of Robotics 3 Control Terminal on working ship Communication equipment Communication equipment Navigation sensors Main controller Motion controller Driver module Thrust Other sensors and controller terminals Figure 1: (e control system structure. mobile monitoring platforms only on one upper computer. designed to improve the robustness of the control system to In this way, multiple mobile monitoring platforms work in external disturbances. By designing the closed-loop feed- parallel and can realize the monitoring, sampling, and back system composed of a shore station control terminal, aeration of large-scale water. on-board controller, navigation and positioning sensor, and driving motor, the closed-loop feedback servo con- troller of the control system of USV is realized. (e specific 3. Waypoint Trajectory-Tracking process is kinematic modeling of USV, waypoint trajec- Control Algorithm tory-tracking control, distributed controller, simulation of (e marine environment is extremely complex, and the tracking control method, and verification of software and hardware design. According to the principle of functional external factors such as wind, waves, and current bring modular design and conducive to all kinds of sensors for challenges to the intelligent design level of the control system data measurement and communication, each functional of USV [19, 20]. Also, the performance of a USV is influ- module is designed. (e designing of the monitoring and enced by the surface currents, risk of collision with the control system can be divided into two parts: the control civilian traffic, and varying depths due to tides and weather hardware designing and the control software designing. [21]. In [22], the performance of a novel control strategy for Considering the mechanical structure of USV, the hard- imperfect systems associated with physical realizations is ware equipment layout is carried out to meet the re- investigated, which takes advantage of the unavoidable quirements of installation space and internal structure imperfections. In [23], we have investigated the waypoints layout of the USV. trajectory-tracking problem for unmanned surface vehicle Firstly, two coordinate frames are adopted in this paper, with environment disturbance. An upper-triangle and di- which are inertial reference frame and body reference agonal matrix decomposition method of Unscented Kalman frame of the USV, respectively. (e origin of the inertial Filter for the environment noise is proposed. (e estimated reference frame is the coordinates of the starting point of states are adopted to design state feedback control laws to the USV. (e state vectors in the inertial frame are posi- achieve the finite-time stabilization. In this paper, in order to tions and steering angle of the USV, which are shown in improve the adaptability of the USV to complex ocean Figure 3. conditions, a distributed intelligent control system is 4 Journal of Robotics Upper Upper configuration soware computer OPC Server PC database Internet Mobile APP Cloud server Mobile Network Gateway Mobile network Lower Water temperature sensor 4G DTU Instrument cabin temperature sensor computer COM 485 Delta DVP PLC Intermediate Intermediate relay relay PLC analog acquisition expansion Intermediate Limit module relay switch Liquid level Solenoid valve, switch Aeration pump sampling pump Dissolved Conduct PH Turbidity Water sample linear oxygen ivity Aeration device sensor sensor collection container Sensor actuator sensor Figure 2: Composition of the sensors monitoring system. x _ cos θ 0 ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ _ ⎢ _ ⎥ ⎢ ⎥ θ Σ : p � ⎢ y ⎥ � ⎢ sin θ 0 ⎥q, ⎢ ⎥ ⎢ ⎥ 1 ⎢ ⎥ ⎢ ⎥ e ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ (1) θ 0 1 � p, where p � [x, y, θ] denotes the states vector of the USV, (x, y) denotes the position coordinates in inertial frame, q � [], ω] denotes the control input, θ � arctan((y _(t))/(x _(t))) denotes the heading angle, and ] and ω denote the linear velocity and angular velocity, respectively. y denotes the output of the control system. (e trajectory-tracking problem of USV is described as the one how to design a control law about ] and ω to make the control system of the USV to track the reference tra- jectories [x , y , θ ] and the reference velocities ] and ω . d d d d d So, in the body-fixed reference frame, the error-based 0 xx x control system model shown in Figure 3 is given. Figure 3: Control system modeling for trajectory-tracking control. x cos θ sin θ 0 x − x x − x e d d ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ p � ⎢ y ⎥ � ⎢ −sin θ cos θ 0 ⎥⎢ y − y ⎥ � T ⎢ y − y ⎥, ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ e ⎢ e ⎥ ⎢ ⎥⎢ d ⎥ e⎢ d ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦⎣ ⎦ ⎣ ⎦ After modeling the control system for trajectory- θ 0 0 1 θ − θ θ − θ e d d tracking control, the kinematics equation of the USV is further described as follows [12]: (2) Journal of Robotics 5 where T is the transformation matrix in the inertial ref- To carry out the rapid reach the predefined trajectory of erence frame with respect to the body-fixed frame. Fur- the USV, we study the finite-time tracking control law. Also, thermore, we take the time-derivative along the solution of the finite-time trajectory-tracking problem is how to find an the system (2), so the kinematics equation of error-based appropriate velocity control laws about 􏽢] and ω 􏽢 of the model is rewritten as following form. x _ � ωy − ] + ] cos θ , e e d e 􏽢] � f􏼐x 􏽢 , y 􏽢 , θ , ] , ω 􏼑, e e e d d (6) Σ : y _ � −ωx + ] sin θ , (3) e e e d e ω 􏽢 � g􏼐x 􏽢 , y 􏽢 , θ , ] , ω 􏼑, e e e d d θ � ω − ω. e d where f(·) is the nonlinear function on x 􏽢 , y 􏽢 , θ , ] , and ω . e e e d d Actually, because of the external sensors noise and in- Also, g(·) is the nonlinear function on x 􏽢 , y 􏽢 , θ , ] , and ω . e e e d d ternal system noise, the control system is easily influenced by For arbitrary initial errors [x 􏽢 (0), y 􏽢 (0), θ (0)] , the closed- e e e them. In order to reject the noise influence on the trajectory- loop trajectories of equation (5) can be stabilized in finite tracking system, a fully observable states based filtering time. estimation are considered in this paper. (e output of the (e designed block structure of the controlled error error system is y � p , so the error system equations with system is illustrated in Figure 4. (e reference trajectory e e external and internal noise is written as vector p is the output of the reference system which is controlled by the control input q . So, the system block ωy − ] + ] cos θ e d e ⎡ ⎢ ⎤ ⎥ structure of Figure 4 is further transformed into the one ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ _ ⎢ ⎥ p � ⎢ −ωx + ] sin θ ⎥ + W, ⎢ ⎥ ev ⎢ e d e ⎥ ⎢ ⎥ shown in Figure 5. ⎣ ⎦ (4) In the Figures 4 and 5, NC denotes the nonlinear ω − ω controller, Σ is the controlled system (2), Σ is the con- 1 e y � p + V, ev e trolled error system, and Σ denotes the following reference kinematics system of the USV. where W and V are the system noise and measurement noise, respectively, which are both Gaussian white noises. x _ cos θ 0 d d ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ (e covariance matrix of W and V are Q and R, respectively. ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ _ ⎢ _ ⎥ ⎢ ⎥ p � ⎢ y ⎥ � ⎢ sin θ 0 ⎥q . (7) ⎢ ⎥ ⎢ ⎥ d ⎢ d ⎥ ⎢ d ⎥ d ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ (e vector p denotes the estimated value of vector p, vector 􏽢 θ 0 1 p denotes the estimated value of p , and y 􏽢 denotes the e e e estimated value of y . (erefore, the stable error system For the control system with system noise and sensors equation with estimations is written as noise, we can design the following control laws about 􏽢] in ω 􏽢 y 􏽢 − 􏽢] + ] cos θ equation (7) and ω 􏽢 in equation (8). (e control law of 􏽢] is e d e ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ designed as _ ⎢ ⎥ ⎢ 􏽢 ⎥ 􏽢 ⎢ ⎥ p � ⎢ ⎥, ⎢ −ω 􏽢 x 􏽢 + ] sin θ ⎥ e ⎢ ⎥ ⎢ e d e ⎥ ⎣ ⎦ (5) ω − ω 􏽢 􏽢 􏽢 y � p , e e 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌β 􏼌 􏼌β ⎧ ⎪ 4 1 2 􏽢 􏼌 􏼌 􏼌 􏼌 􏽢 􏽢 ⎪ ] cos θ + ω y 􏽢 − k sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k sgn x 􏽢 􏼁 􏼌x 􏽢 􏼌 , when θ sgn􏼐θ 􏼑 > , d e d e 1 e e 2 e e e e ⎪ 3 􏽢] � (8) ⎪ 􏼌 􏼌 ⎪ 􏼌 􏼌β 2 4 ⎪ 􏽢 􏼌 􏼌 􏽢 􏽢 ] cos θ − k sgn x 􏽢 x 􏽢 , when θ sgn θ ≤ . ⎩ 􏼁 􏼌 􏼌 􏼐 􏼑 d e 3 e e e e (e control law of ω 􏽢 is designed as ⎧ ⎪ 􏽢 􏽢 􏽢 􏽢 ω − k θ − k sgn􏼐θ 􏼑, when θ sgn􏼐θ 􏼑 > , d 1 e 3 e e e ⎪ k ω 􏽢 � (9) ⎪ 􏼌 􏼌 􏼌 􏼌β ⎪ 4 􏼌 􏼌 􏽢 􏽢 􏽢 ω − k v y 􏽢 sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k v sin θ , when θ sgn􏼐θ 􏼑 ≤ . d 1 d e e e 4 d e e e 3 6 Journal of Robotics q p e p qq p pˆ p v d d p e w T NC ∫ Σ Filter Figure 4: (e control block for the USV trajectory-tracking. q q y yˆ d + w + ev e NC Σ Filter + + Figure 5: (e translated control block for the USV trajectory-tracking. with k > 0, k > 0, k < 0, k < 0, and 0 < β < 1, β � 2β / (1) When |θ | > k /k , 1 2 3 4 1 2 1 e 4 3 (1 + β ). With the control law (7) and (8), the close-loop error system is transformed as _ 􏽢 ⎪ p � f􏼐x 􏽢 , y 􏽢 , θ 􏼑, e e e e ⎪ 􏼌 􏼌 􏼌 􏼌 ⎪ 􏼌 􏼌β 􏼌 􏼌β 1 2 􏽢 􏽢 ⎪ 􏼌 􏼌 􏼌 􏼌 k sgn y 􏽢 􏼁 y 􏽢 + 􏼐−k θ − k sgn􏼐θ 􏼑􏼑y + k sgn x 􏽢 􏼁 x 􏽢 ⎨ 􏼌 􏼌 􏼌 􏼌 1 e e 3 e 4 e e 2 e e ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ � ⎣ ⎦, (10) 􏽢 􏽢 􏽢 􏽢 􏽢 ] sin θ − ω − k θ − k sgn θ x 􏽢 + k θ + k sgn θ 􏼐 􏼐 􏼑􏼑 􏼐 􏼑 d e d 1 e 2 e e 1 e 2 e 􏽢 􏽢 􏽢 􏽢 y � h􏼐x , y , θ 􏼑 � p . e e e e e (2) When |θ | ≤ k /k , e 4 3 _ 􏽢 ⎧ ⎪ p � f􏼐x 􏽢 , y 􏽢 , θ 􏼑, e e e e 􏼌 􏼌 􏼌 􏼌 ⎪ 1 􏼌 􏼌 􏼒ω − k v y 􏽢 sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k v sin θ 􏼓y 􏽢 + k x 􏽢 ⎡ ⎢ d 1 d e e e 4 d e e 3 e ⎤ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎨ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 􏼌 􏼌 ⎥ ⎢ ⎥ ⎢ 􏼌 􏼌β ⎢ ⎥ ⎢ 1 ⎥ � ⎢ 􏼌 􏼌 􏽢 􏽢 ⎥, (11) ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ −􏼒ω − k v y 􏽢 sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k v sin θ 􏼓x 􏽢 + ] sin θ ⎥ ⎢ ⎥ ⎢ d 1 d e e e 4 d e e d e ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ +􏼐k v y 􏽢 + k v sin θ 􏼑 ⎪ 3 d e 4 d e ⎩ 􏽢 y 􏽢 � h􏼐x 􏽢 , y 􏽢 , θ 􏼑 � p . e e e e e (en, the filtering algorithm can be used to filter the sensor. (e RS232 serial port is used to communicate the system noise and sensors noise. (en, we can obtain the main controller of the lower computer with the STM32 estimated values of the error system states. With the esti- processor which receives the control command from the mation of error system states, we can further obtain the main controller of the lower computer. (en, it unpacks the feedback control laws of the error system based on the information according to the predefined protocol format, estimated states. extracts the propeller speed command of the corresponding control field, and generates two PWM control signals according to the propeller speed command, which are sent to 4. Implement and Experiment the electronic governors of the two channel underwater propellers through the PWM port, and then controls the 4.1. Motion Controller. (e core processor is STM32 pro- cessor, the power supply voltage is 5VDC, with multi PWM propulsion. (e motor rotates according to the given speed. interface and two serial ports, and the on-board temperature At the same time, the collected lithium battery voltage and Journal of Robotics 7 Figure 6: Shore experiment of USV. Figure 7: (e waypoints tracking control result, nav_heading, and desired_heading plots in MOOS-IvP alogview. the output current signal of electric regulation are packaged “M,” it is the manual remote-control mode. (e duration of according to the predefined communication protocol format PWM signal high level is 1000 us–2000 us, 1500 us denotes and sent to the main controller of the lower computer stopping, less than 1500 us means the thruster is reversed, through the serial port. Two 1500 W ducted thrusters are and more than 1500 us indicates the propeller is positive used in the propulsion device which can be used for forward turn. (e multistage forward and reverse control of the and reverse propulsion, and differential speed regulation is propeller is realized by dividing 1000–2000 into several carried out by using the forward and reverse rotation of the segments. (e monitoring software of the upper computer is thrusters. (ere are three control signals, two of which are installed on the ARK2250 control board of the onboard 5 V·DC power supply and the other is PWM control signal. controller. (e 5.8 G wireless bridge terminal can receive (e driving module adopts the 120 A bidirectional electric measurement and control data within 6 km of the water regulation module, and the power supply voltage is surface. (e motion control software of the upper computer 24 V–50 VDC, with three-phase output. It receives the PWM is written in C++ language in MOOS-Ivp [24] and Ubuntu signal from the lower motion controller. After receiving the system. (e waypoints tracking control result and heading control command from the main controller of the lower and yaw control using MOOS-IvP pMarineViewer, pHel- computer, the corresponding control information of the mivp, and pCompass, et al. modules. protocol is extracted. If the control information is “A,” it is (e tracking control experiment was carried out at the the autonomous control mode. If the control information is shore experimental station of SDIOI. (e experiment of the 8 Journal of Robotics (a) (b) (c) Figure 8: (e monitoring parameters plots of SENSOR_tem (a), SENSOR_hum (b), and SENSOR_vol (c) in MOOS-IvP alogview. Figure 9: Control interface of APP. tracking control algorithm carried out inpAct module of minimum dissolved oxygen is defined as 5 mg/L. (e start MOOS is shown in Figure 6. (e tracking effect of the and stop of the oxygen enrichment equipment is based on proposed control laws is shown in Figure 7. (e monitoring the average value of dissolved oxygen detected by the sensor parameters such as temperature, humidity, and voltage with within 5 minutes. When the average value of dissolved variables SENSOR_tem, SENSOR_hum, and SENSOR_vol oxygen is lower than 5 mg/L, the oxygen enrichment in MOOS-IvP alogview are shown in Figures 8 and 9. equipment will start and run automatically. When the av- erage value of dissolved oxygen in this period is higher than 5.5 mg/L, it will stop. When the dissolved oxygen is lower 4.2. Dissolved Oxygen Control. (e minimum value of dis- than 5 mg/L again, it will start again until the accumulated solved oxygen can be set according to the actual situation of time is 30 minutes. (erefore, the dissolved oxygen in the the water body. In the software and APP, the default value of water is not less than 5 mg/L, and the self-purification Journal of Robotics 9 capacity of the water is forced to be improved. (e minimum Wenxia Zhang analyzed and interpreted the obtained data value of dissolved oxygen and automatic aeration time can regarding the navigation and control experiment. be set on the remote terminal. (e minimum value of dissolved oxygen can be set to 4 mg/L, and the maximum Acknowledgments value can be set to 6 mg/L. Moreover, the dissolved oxygen value when the aeration pump is shut down is 0.5 mg/L (e project is financially supported by the Open Fund higher than the minimum value, which can prevent frequent Project of Key Laboratory of Ocean Observation Technol- start-up and stop of the aeration device. (e maximum ogy, MNR (2021klootA10). aeration time is 30 min, and the minimum aeration time is 30 min. (rough the 4G Internet of (ings technology, the References remote interconnection and monitoring of multiple moni- toring USV platforms are realized. (rough the software [1] B. Qin, G. Zhu, G. Gao et al., “A drinking water crisis in lake configuration, the functions of remote start and stop, and taihu, China: linkage to climatic variability and lake man- automatic water sampling system fault alarming are realized, agement,” Environmental Management, vol. 45, no. 1, which can provide users with accurate data on site and pp. 105–112, 2010. improve the work efficiency. [2] J. B. De Sousa and G. Andrade Gonçalves, “Unmanned ve- hicles for environmental data collection,” Clean Technologies and Environmental Policy, vol. 13, no. 2, pp. 369–380, 2011. 5. Conclusion [3] D. F. Carlson, A. Fursterling, ¨ L. Vesterled et al., “An affordable and portable autonomous surface vehicle with obstacle Autonomous control system for mobile water quality avoidance for coastal ocean monitoring,” HardwareX, vol. 5, monitoring, sampling, and oxygenation is designed. (e Article ID e00059, 2019. proposed trajectory-tracking algorithm is carried for the [4] J. E. Manley, “Unmanned maritime vehicles, 20 years of heading control of USV. (e tracking error is acceptable commercial and technical evolution,” in Proceedings of the with proposed control law. (e experiment shows that the Oceans, pp. 1–6, Monterey, CA, USA, September 2016. USV can stop in the target location although the error is [5] A. Tinka, M. Rafiee, and A. M. Bayen, “Floating sensor larger during its deploying. (e control and monitoring networks for river studies,” IEEE Systems Journal, vol. 7, no. 1, pp. 36–49, 2013. system can transmit the collecting water quality data to [6] Y. Kaizu, M. Iio, H. Yamada, and N. Noguchi, “Development the cloud server and PC with OPC server and share the of unmanned airboat for water-quality mapping,” Biosystems water quality information with the public through the Engineering, vol. 109, no. 4, pp. 338–347, 2011. application program. It shows the start-stop exchange for [7] F. Sapuppo, F. Schembri, L. Fortuna, A. 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Guedes Soares, “Pre-filtered sliding mode sibilities of using lab-on-chip innovative devices and control for nonlinear ship steering associated with distur- equipment based on microfluidics should be made. In bances,” Ocean Engineering, vol. 51, pp. 49–62, 2012. addition, the proposed USV platform can become an [10] H. Kolmanovsky and N. H. McClamroch, “Developments in effective and interesting teaching tool for students in the nonholonomic controlsystems,” IEEE Control Systems Mag- field of marine robot’s motion control and autonomous azine, vol. 15, no. 6, pp. 20–36, 1997. navigation. [11] Y. Kanayama, Y. Kimura, F. Miyazaki, and T. Noguchi, “A stable tracking control scheme for anautonomous mobile robot,” in Proceedings of the IEEE International Conference on Data Availability Robotics and Automation, pp. 384–389, Cincinnati, OH, USA, (e data used to support the findings of this study are [12] Z.-P. Jiang and H. Nijmeujer, “Tracking control of mobile available on request. robots: a case study in Backstepping,” Automatica, vol. 33, no. 7, pp. 1393–1399, 1997. [13] W. J. Dong and W. Huo, “Tracking control of mobile robots Conflicts of Interest with nonholonomic constraint,” Acta Automatica Sinica, vol. 26, no. 1, pp. 1–6, 2000. (e authors declare that they have no conflicts of interest. [14] Y. Hong, Y. Xu, and J. Huang, “Finite-time control for robot manipulators,” Systems & Control Letters, vol. 46, no. 4, Authors’ Contributions pp. 243–253, 2002. [15] Z. Zhao, W. He, and S. S. Ge, “Adaptive neural network Jian Yuan proposed the control scheme and constructed the control of a fully actuated marine surface vessel with multiple control algorithms, hardware, and software. Hailin Liu output constraints,” IEEE Transactions on Control Systems performed the algorithm examination of PWM control. Technology, vol. 22, no. 4, pp. 1536–1543, 2014. 10 Journal of Robotics [16] N. Wang, M. J. Er, and M. Han, “Dynamic tanker steering control using generalized ellipsoidal-basis-function-based fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, vol. 23, no. 5, pp. 1414–1427, 2015. [17] J. Han, Y. Cho, J. Kim, J. Kim, N. S. Son, and S. Y. Kim, “Autonomous collision detection and avoidance for ARA- GON USV: development and field tests,” Journal of Field Robotics, vol. 37, no. 6, pp. 987–1002, 2020. [18] J. Roberts Luke, A. Bruck Hugh, and S. K. Gupta, “Modeling of dive maneuvers for executing autonomous dives with a flapping wing air vehicle,” Journal of Mechanisms and Ro- botics, vol. 9, 2017. [19] A. Perez-Rosado, H. Bruck, and S. K. Gupta, “Integrating solar cells into flapping wing air vehicles for enhanced flight en- durance,” Journal of Mechanisms and Robotics, vol. 8, 2016. [20] D. Levi, D. M. Kutzer Michael, B. Archie, and R. Richmond, “Hull shape actuation for speed regulation in an underwater vehicle,” Journal of Mechanisms and Robotics, vol. 12, 2020. [21] S. Shaurya, B. C. Shah, and S. K. Gupta, “Decomposition of collaborative surveillance tasks for execution in marine en- vironments by a team of unmanned surface vehicles,” Journal of Mechanisms and Robotics, vol. 10, 2018. [22] M. Bucolo, A. Buscarino, C. Famoso, L. Fortuna, and M. Frasca, “Control of imperfect dynamical systems,” Non- linear Dynamics, vol. 98, no. 4, pp. 2989–2999, 2019. [23] Y. Jian, L. Hailin, and Z. Wenxia, “Waypoints trajectory tracking control for nonholonomic unmanned surface vehicle under environment disturbance,” in Proceedings of the 2020 Chinese Control and Decision Conference (CCDC), pp. 3050– 3054, Hefei, China, 2020. [24] MOOS-Ivp Autonomy Tools Users Manual, https://oceanai. mit.edu/ivpman/pmwiki/pmwiki.Php?n�IvPTools.Cover. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Robotics Hindawi Publishing Corporation

Waypoint Tracking Control for Autonomous Mobile Sampling and Dissolved Oxygen Enrichment of Unmanned Surface Vehicle

Yuan, Jian; Liu, Hailin; Zhang, Wenxia
Journal of Robotics , Volume 2022 – Mar 31, 2022
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Hindawi Publishing Corporation
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Copyright © 2022 Jian Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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1687-9600
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10.1155/2022/3652329
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Abstract

Hindawi Journal of Robotics Volume 2022, Article ID 3652329, 10 pages https://doi.org/10.1155/2022/3652329 Research Article WaypointTrackingControlforAutonomousMobileSamplingand Dissolved Oxygen Enrichment of Unmanned Surface Vehicle 1,2 1 3 Jian Yuan , Hailin Liu, and Wenxia Zhang Institute of Oceanographic Instrumentation, Qilu University of Technology (Shandong Academy of Sciences), Shandong Provincial Key Laboratory of Ocean Environment Monitoring Technology, National Engineering and Technological Research Center of Marine Monitoring Equipment, Qingdao, China Key Laboratory of Ocean Observation Technology, MNR, Beijing, China Department of Mechanical and Electrical Engineering, Qingdao City University, Qingdao, China Correspondence should be addressed to Jian Yuan; yuanjian@qlu.edu.cn Received 1 March 2022; Accepted 18 March 2022; Published 31 March 2022 Academic Editor: L. Fortuna Copyright © 2022 Jian Yuan et al. (is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. An autonomous monitoring and control system of unmanned surface vehicle (USV) with mobile water quality monitoring, sampling, and oxygenation functions is constructed. (e control hardware and monitoring configuration software of the system is designed, respectively, which can be installed on USV and its remote control and monitoring terminal. (e kinematic modeling of USV, waypoint trajectory-tracking control, distributed controller, simulation of tracking control, and verification of software and hardware design are carried out. In order to reject the system noise and external noise, a states estimation method with fully observable states is considered in the control law design. (e software and hardware are also implemented to verify the ef- fectiveness of the monitoring platform. (rough setting a series of monitoring target points and monitoring parameters in the configuration software of the remote user terminal or in the APP of the mobile user terminal, the USV can realize the automatic cruise monitoring using an autonomous navigation and tracking control algorithm, and quantitative water sampling collection. (e reliability of the system is verified by the experiment of the shore test station, and the waypoint trajectory tracking and sensors data are replaying in a logview GUI of MOOS-Ivp and APP. unmanned boat does not have the automatic oxygen en- 1. Introduction richment and fixed depth water sampling function, which (e online monitoring system of water quality can monitor does not have the autonomous navigation and control the water quality all day. (e data from the water quality ability. (e fixed-point jetting mode is often used in the monitoring system can reflect the water quality or water water body aerator, which has great disturbance to the water pollution, and the system can provide reliable data for water body. (e single monitoring, sampling, and aeration environmental management [1]. At present, the monitoring equipment cannot complete a large range of monitoring and equipment of water quality on the market mostly are usually water sample collection of water area in a short time, so a in a shore monitoring station for a fixed-point monitoring in networked monitoring mobile platform containing many the water, which has not the mobile monitoring capability USVs can be used for urgent monitoring. (e researchers for a large area of water surface. Currently, the water quality from the University of California, Berkeley, has developed a monitoring technology can be carried out manually, but it floating cylindrical device to monitor the water quality [5]; It lacks autonomous, frequent, and efficient monitoring has sensors installed at the device bottom and sends sensors schemes [2, 3] and various USV platforms [4]. Recently, the information wirelessly. It has the ability of positioning itself remote-control unmanned boat with sensor monitoring is with GPS device and moving around with small propellers designed to realize the water surface monitoring, but the powered by lithium-ion batteries. Kaizu et al. has developed 2 Journal of Robotics reversed and forward. (e USV body changes the course of an unmanned hovercraft for water quality mapping, which can collect temperature, dissolved oxygen, turbidity, con- rotation moment caused by the different thrust generated by two culvert thrusters, which has good maneuverability and ductivity, pH, and chlorophyll data [6]. For water real-time monitoring and control, the integration of microfluidic chip maneuverability. (e USV has functions of PWM motor and micro-optical system suggests possibilities. In [7], for speed control, automatic heading keeping, waypoint the spatially distributed characterization of microfluidic tracking, motion attitude monitoring, remote data com- two-phase phenomena, the authors present a polymeric munication, water quality sensing, and sampling. (e USV micro-optical system that consists of two coupled minia- has the characteristics of stable hull structure, lightweight, turized devices. In [8], the authors proposed a low-cost, flexible control, and long endurance. At the same time, it can realize the expandable design of measurement module to completely open source, USV for real-time measurement and detection for the surface water quality in complex meet the requirements of multilevel, diversified, and various advanced control algorithm verification experiments and environments. (e platform is equipped with various water sensors to collect pH, turbidity, and temperature data for teaching. actual water quality investigation. Trajectory-tracking con- trol of USV waypoint has important theoretical and engi- 2. Design of Control and Monitoring System neering significance for the water quality monitoring of USV [9]. However, because of nonholonomic constraints, the (e self-powered, oxygen enrichment control system for USV control system does not satisfy the Brockett necessary water surface movement monitoring contains two subsys- condition for stable control [10]. (erefore, Kanayama et al. tems, that is, the power supply electronic subsystem and the adopts Taylor linearization and dynamic feedback lineari- data communication and monitoring control subsystem. zation methods to design a stable tracking controller based (e control system consists of the control and navigation on tracking error model [11]. Jiang and Nijmeujer designed a system for motion control and the data communication and trajectory-tracking control law using the backstepping monitoring control system for water sampling. (e former is methodwhich solved both the local and global tracking working for trajectory-tracking and motion control, and the control problems [12]. In [13], the kinematic and dynamic latter is designed for water sampling and monitoring. (e models are first transformed into a unified standard form, structure of the monitoring system is shown in Figure 1. (e and then, a new dynamic tracking controller is proposed to monitoring system is divided into control terminal on solve the global tracking problem of the nonholonomic USV. working ship and the USV control terminal. (e USV In [14], a global tracking control law is constructed by using terminal consists of main controller, motion controller, Lyapunov direct method, and the nonholonomic constraint driver module, thrust, navigation sensors, and other con- control is solved. In [15, 16], the authors proposed the troller terminals. adaptive neural network control method to design the (e data communication and monitoring system for tracking control law. In [17], the authors presented a der- water sampling consists of a remote upper computer or ivation of autonomous control algorithm, which can reg- mobile phone and a lower computer installed in the USV, as ulate the vehicle speed to a time-varying reference speed to shown in Figure 2. (e lower computer is composed of Delta improve the forward speed control. For open-loop operation PLC and analog quantity expansion module, intermediate mode of outdoor diving with wind speed limit, Roberts Luke relay, aeration device, water quality sampling equipment, et al. [18] used a low-power on-board processor and pro- and mps-400 water quality sensor unit, which completes the posed a calculation model with an accuracy of 5 m. real-time data collection of dissolved oxygen, turbidity, To realize a large of water monitoring in a short time, we temperature, pH, and conductivity water quality sensors. design a self-powered oxygen enrichment control system Both a computer and a mobile phone can be used as the that can be installed in USV cabin, and the USV can cruise upper control equipment. (e software installed on the independently according to the monitoring points of users, computer is mainly composed of the configuration software also can realize the real-time monitoring of water temper- of Delta, SQLServer2005, and OPC server provided by the ature, pH, turbidity, oxygen, and conductivity of the GiantControl Co. Ltd. (rough the real-time configuration, monitoring points. Meanwhile, the monitoring and control the user can master whether the operation status of the system can collect water sampling. (e control system in- aeration pump, push rod, and sensor is normal. (e cludes a photovoltaic module, a remote communication and alarming threshold and aeration time of dissolved oxygen monitoring module, a sensor module, an aeration module, can be set in the upper computer. (e communication and a water sample collection module. (e system can run between the upper and lower computers is carried out autonomously according to the dissolved oxygen value. (e through the data transmission module GRM DTU203G. (e reliability of the system is verified by the experiment of the remote monitoring and control can also be carried out by a shore test station. It can be applied to the water quality mobile APP accessing the cloud server. (e data collected by monitoring of a large range of shore surface aquaculture. It the system can be stored in the database of the upper can be applied to the water quality monitoring of a large computer and the cloud server at the same time, and the range of shore surface aquaculture. Furthermore, we develop synchronization control and display can be realized. Because a double-pushing USV with electric propulsions for mon- of the 4G Internet of (ings mode, the system can realize the itoring. Its thrusters consist of two culvert thrusters sym- networked monitoring of multiple terminals and realize the metrically arranged at the bottom of the USV, which can be integrated monitoring and task scheduling for multiple Journal of Robotics 3 Control Terminal on working ship Communication equipment Communication equipment Navigation sensors Main controller Motion controller Driver module Thrust Other sensors and controller terminals Figure 1: (e control system structure. mobile monitoring platforms only on one upper computer. designed to improve the robustness of the control system to In this way, multiple mobile monitoring platforms work in external disturbances. By designing the closed-loop feed- parallel and can realize the monitoring, sampling, and back system composed of a shore station control terminal, aeration of large-scale water. on-board controller, navigation and positioning sensor, and driving motor, the closed-loop feedback servo con- troller of the control system of USV is realized. (e specific 3. Waypoint Trajectory-Tracking process is kinematic modeling of USV, waypoint trajec- Control Algorithm tory-tracking control, distributed controller, simulation of (e marine environment is extremely complex, and the tracking control method, and verification of software and hardware design. According to the principle of functional external factors such as wind, waves, and current bring modular design and conducive to all kinds of sensors for challenges to the intelligent design level of the control system data measurement and communication, each functional of USV [19, 20]. Also, the performance of a USV is influ- module is designed. (e designing of the monitoring and enced by the surface currents, risk of collision with the control system can be divided into two parts: the control civilian traffic, and varying depths due to tides and weather hardware designing and the control software designing. [21]. In [22], the performance of a novel control strategy for Considering the mechanical structure of USV, the hard- imperfect systems associated with physical realizations is ware equipment layout is carried out to meet the re- investigated, which takes advantage of the unavoidable quirements of installation space and internal structure imperfections. In [23], we have investigated the waypoints layout of the USV. trajectory-tracking problem for unmanned surface vehicle Firstly, two coordinate frames are adopted in this paper, with environment disturbance. An upper-triangle and di- which are inertial reference frame and body reference agonal matrix decomposition method of Unscented Kalman frame of the USV, respectively. (e origin of the inertial Filter for the environment noise is proposed. (e estimated reference frame is the coordinates of the starting point of states are adopted to design state feedback control laws to the USV. (e state vectors in the inertial frame are posi- achieve the finite-time stabilization. In this paper, in order to tions and steering angle of the USV, which are shown in improve the adaptability of the USV to complex ocean Figure 3. conditions, a distributed intelligent control system is 4 Journal of Robotics Upper Upper configuration soware computer OPC Server PC database Internet Mobile APP Cloud server Mobile Network Gateway Mobile network Lower Water temperature sensor 4G DTU Instrument cabin temperature sensor computer COM 485 Delta DVP PLC Intermediate Intermediate relay relay PLC analog acquisition expansion Intermediate Limit module relay switch Liquid level Solenoid valve, switch Aeration pump sampling pump Dissolved Conduct PH Turbidity Water sample linear oxygen ivity Aeration device sensor sensor collection container Sensor actuator sensor Figure 2: Composition of the sensors monitoring system. x _ cos θ 0 ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ _ ⎢ _ ⎥ ⎢ ⎥ θ Σ : p � ⎢ y ⎥ � ⎢ sin θ 0 ⎥q, ⎢ ⎥ ⎢ ⎥ 1 ⎢ ⎥ ⎢ ⎥ e ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ (1) θ 0 1 � p, where p � [x, y, θ] denotes the states vector of the USV, (x, y) denotes the position coordinates in inertial frame, q � [], ω] denotes the control input, θ � arctan((y _(t))/(x _(t))) denotes the heading angle, and ] and ω denote the linear velocity and angular velocity, respectively. y denotes the output of the control system. (e trajectory-tracking problem of USV is described as the one how to design a control law about ] and ω to make the control system of the USV to track the reference tra- jectories [x , y , θ ] and the reference velocities ] and ω . d d d d d So, in the body-fixed reference frame, the error-based 0 xx x control system model shown in Figure 3 is given. Figure 3: Control system modeling for trajectory-tracking control. x cos θ sin θ 0 x − x x − x e d d ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ p � ⎢ y ⎥ � ⎢ −sin θ cos θ 0 ⎥⎢ y − y ⎥ � T ⎢ y − y ⎥, ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ e ⎢ e ⎥ ⎢ ⎥⎢ d ⎥ e⎢ d ⎥ ⎢ ⎥ ⎢ ⎥⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦⎣ ⎦ ⎣ ⎦ After modeling the control system for trajectory- θ 0 0 1 θ − θ θ − θ e d d tracking control, the kinematics equation of the USV is further described as follows [12]: (2) Journal of Robotics 5 where T is the transformation matrix in the inertial ref- To carry out the rapid reach the predefined trajectory of erence frame with respect to the body-fixed frame. Fur- the USV, we study the finite-time tracking control law. Also, thermore, we take the time-derivative along the solution of the finite-time trajectory-tracking problem is how to find an the system (2), so the kinematics equation of error-based appropriate velocity control laws about 􏽢] and ω 􏽢 of the model is rewritten as following form. x _ � ωy − ] + ] cos θ , e e d e 􏽢] � f􏼐x 􏽢 , y 􏽢 , θ , ] , ω 􏼑, e e e d d (6) Σ : y _ � −ωx + ] sin θ , (3) e e e d e ω 􏽢 � g􏼐x 􏽢 , y 􏽢 , θ , ] , ω 􏼑, e e e d d θ � ω − ω. e d where f(·) is the nonlinear function on x 􏽢 , y 􏽢 , θ , ] , and ω . e e e d d Actually, because of the external sensors noise and in- Also, g(·) is the nonlinear function on x 􏽢 , y 􏽢 , θ , ] , and ω . e e e d d ternal system noise, the control system is easily influenced by For arbitrary initial errors [x 􏽢 (0), y 􏽢 (0), θ (0)] , the closed- e e e them. In order to reject the noise influence on the trajectory- loop trajectories of equation (5) can be stabilized in finite tracking system, a fully observable states based filtering time. estimation are considered in this paper. (e output of the (e designed block structure of the controlled error error system is y � p , so the error system equations with system is illustrated in Figure 4. (e reference trajectory e e external and internal noise is written as vector p is the output of the reference system which is controlled by the control input q . So, the system block ωy − ] + ] cos θ e d e ⎡ ⎢ ⎤ ⎥ structure of Figure 4 is further transformed into the one ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ _ ⎢ ⎥ p � ⎢ −ωx + ] sin θ ⎥ + W, ⎢ ⎥ ev ⎢ e d e ⎥ ⎢ ⎥ shown in Figure 5. ⎣ ⎦ (4) In the Figures 4 and 5, NC denotes the nonlinear ω − ω controller, Σ is the controlled system (2), Σ is the con- 1 e y � p + V, ev e trolled error system, and Σ denotes the following reference kinematics system of the USV. where W and V are the system noise and measurement noise, respectively, which are both Gaussian white noises. x _ cos θ 0 d d ⎢ ⎥ ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ (e covariance matrix of W and V are Q and R, respectively. ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ _ ⎢ _ ⎥ ⎢ ⎥ p � ⎢ y ⎥ � ⎢ sin θ 0 ⎥q . (7) ⎢ ⎥ ⎢ ⎥ d ⎢ d ⎥ ⎢ d ⎥ d ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ ⎣ ⎦ (e vector p denotes the estimated value of vector p, vector 􏽢 θ 0 1 p denotes the estimated value of p , and y 􏽢 denotes the e e e estimated value of y . (erefore, the stable error system For the control system with system noise and sensors equation with estimations is written as noise, we can design the following control laws about 􏽢] in ω 􏽢 y 􏽢 − 􏽢] + ] cos θ equation (7) and ω 􏽢 in equation (8). (e control law of 􏽢] is e d e ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ designed as _ ⎢ ⎥ ⎢ 􏽢 ⎥ 􏽢 ⎢ ⎥ p � ⎢ ⎥, ⎢ −ω 􏽢 x 􏽢 + ] sin θ ⎥ e ⎢ ⎥ ⎢ e d e ⎥ ⎣ ⎦ (5) ω − ω 􏽢 􏽢 􏽢 y � p , e e 􏼌 􏼌 􏼌 􏼌 􏼌 􏼌β 􏼌 􏼌β ⎧ ⎪ 4 1 2 􏽢 􏼌 􏼌 􏼌 􏼌 􏽢 􏽢 ⎪ ] cos θ + ω y 􏽢 − k sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k sgn x 􏽢 􏼁 􏼌x 􏽢 􏼌 , when θ sgn􏼐θ 􏼑 > , d e d e 1 e e 2 e e e e ⎪ 3 􏽢] � (8) ⎪ 􏼌 􏼌 ⎪ 􏼌 􏼌β 2 4 ⎪ 􏽢 􏼌 􏼌 􏽢 􏽢 ] cos θ − k sgn x 􏽢 x 􏽢 , when θ sgn θ ≤ . ⎩ 􏼁 􏼌 􏼌 􏼐 􏼑 d e 3 e e e e (e control law of ω 􏽢 is designed as ⎧ ⎪ 􏽢 􏽢 􏽢 􏽢 ω − k θ − k sgn􏼐θ 􏼑, when θ sgn􏼐θ 􏼑 > , d 1 e 3 e e e ⎪ k ω 􏽢 � (9) ⎪ 􏼌 􏼌 􏼌 􏼌β ⎪ 4 􏼌 􏼌 􏽢 􏽢 􏽢 ω − k v y 􏽢 sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k v sin θ , when θ sgn􏼐θ 􏼑 ≤ . d 1 d e e e 4 d e e e 3 6 Journal of Robotics q p e p qq p pˆ p v d d p e w T NC ∫ Σ Filter Figure 4: (e control block for the USV trajectory-tracking. q q y yˆ d + w + ev e NC Σ Filter + + Figure 5: (e translated control block for the USV trajectory-tracking. with k > 0, k > 0, k < 0, k < 0, and 0 < β < 1, β � 2β / (1) When |θ | > k /k , 1 2 3 4 1 2 1 e 4 3 (1 + β ). With the control law (7) and (8), the close-loop error system is transformed as _ 􏽢 ⎪ p � f􏼐x 􏽢 , y 􏽢 , θ 􏼑, e e e e ⎪ 􏼌 􏼌 􏼌 􏼌 ⎪ 􏼌 􏼌β 􏼌 􏼌β 1 2 􏽢 􏽢 ⎪ 􏼌 􏼌 􏼌 􏼌 k sgn y 􏽢 􏼁 y 􏽢 + 􏼐−k θ − k sgn􏼐θ 􏼑􏼑y + k sgn x 􏽢 􏼁 x 􏽢 ⎨ 􏼌 􏼌 􏼌 􏼌 1 e e 3 e 4 e e 2 e e ⎢ ⎥ ⎡ ⎢ ⎤ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ � ⎣ ⎦, (10) 􏽢 􏽢 􏽢 􏽢 􏽢 ] sin θ − ω − k θ − k sgn θ x 􏽢 + k θ + k sgn θ 􏼐 􏼐 􏼑􏼑 􏼐 􏼑 d e d 1 e 2 e e 1 e 2 e 􏽢 􏽢 􏽢 􏽢 y � h􏼐x , y , θ 􏼑 � p . e e e e e (2) When |θ | ≤ k /k , e 4 3 _ 􏽢 ⎧ ⎪ p � f􏼐x 􏽢 , y 􏽢 , θ 􏼑, e e e e 􏼌 􏼌 􏼌 􏼌 ⎪ 1 􏼌 􏼌 􏼒ω − k v y 􏽢 sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k v sin θ 􏼓y 􏽢 + k x 􏽢 ⎡ ⎢ d 1 d e e e 4 d e e 3 e ⎤ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎨ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ 􏼌 􏼌 ⎥ ⎢ ⎥ ⎢ 􏼌 􏼌β ⎢ ⎥ ⎢ 1 ⎥ � ⎢ 􏼌 􏼌 􏽢 􏽢 ⎥, (11) ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ −􏼒ω − k v y 􏽢 sgn y 􏽢 􏼁 􏼌y 􏽢 􏼌 − k v sin θ 􏼓x 􏽢 + ] sin θ ⎥ ⎢ ⎥ ⎢ d 1 d e e e 4 d e e d e ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎪ ⎢ ⎥ ⎢ ⎥ ⎣ ⎦ +􏼐k v y 􏽢 + k v sin θ 􏼑 ⎪ 3 d e 4 d e ⎩ 􏽢 y 􏽢 � h􏼐x 􏽢 , y 􏽢 , θ 􏼑 � p . e e e e e (en, the filtering algorithm can be used to filter the sensor. (e RS232 serial port is used to communicate the system noise and sensors noise. (en, we can obtain the main controller of the lower computer with the STM32 estimated values of the error system states. With the esti- processor which receives the control command from the mation of error system states, we can further obtain the main controller of the lower computer. (en, it unpacks the feedback control laws of the error system based on the information according to the predefined protocol format, estimated states. extracts the propeller speed command of the corresponding control field, and generates two PWM control signals according to the propeller speed command, which are sent to 4. Implement and Experiment the electronic governors of the two channel underwater propellers through the PWM port, and then controls the 4.1. Motion Controller. (e core processor is STM32 pro- cessor, the power supply voltage is 5VDC, with multi PWM propulsion. (e motor rotates according to the given speed. interface and two serial ports, and the on-board temperature At the same time, the collected lithium battery voltage and Journal of Robotics 7 Figure 6: Shore experiment of USV. Figure 7: (e waypoints tracking control result, nav_heading, and desired_heading plots in MOOS-IvP alogview. the output current signal of electric regulation are packaged “M,” it is the manual remote-control mode. (e duration of according to the predefined communication protocol format PWM signal high level is 1000 us–2000 us, 1500 us denotes and sent to the main controller of the lower computer stopping, less than 1500 us means the thruster is reversed, through the serial port. Two 1500 W ducted thrusters are and more than 1500 us indicates the propeller is positive used in the propulsion device which can be used for forward turn. (e multistage forward and reverse control of the and reverse propulsion, and differential speed regulation is propeller is realized by dividing 1000–2000 into several carried out by using the forward and reverse rotation of the segments. (e monitoring software of the upper computer is thrusters. (ere are three control signals, two of which are installed on the ARK2250 control board of the onboard 5 V·DC power supply and the other is PWM control signal. controller. (e 5.8 G wireless bridge terminal can receive (e driving module adopts the 120 A bidirectional electric measurement and control data within 6 km of the water regulation module, and the power supply voltage is surface. (e motion control software of the upper computer 24 V–50 VDC, with three-phase output. It receives the PWM is written in C++ language in MOOS-Ivp [24] and Ubuntu signal from the lower motion controller. After receiving the system. (e waypoints tracking control result and heading control command from the main controller of the lower and yaw control using MOOS-IvP pMarineViewer, pHel- computer, the corresponding control information of the mivp, and pCompass, et al. modules. protocol is extracted. If the control information is “A,” it is (e tracking control experiment was carried out at the the autonomous control mode. If the control information is shore experimental station of SDIOI. (e experiment of the 8 Journal of Robotics (a) (b) (c) Figure 8: (e monitoring parameters plots of SENSOR_tem (a), SENSOR_hum (b), and SENSOR_vol (c) in MOOS-IvP alogview. Figure 9: Control interface of APP. tracking control algorithm carried out inpAct module of minimum dissolved oxygen is defined as 5 mg/L. (e start MOOS is shown in Figure 6. (e tracking effect of the and stop of the oxygen enrichment equipment is based on proposed control laws is shown in Figure 7. (e monitoring the average value of dissolved oxygen detected by the sensor parameters such as temperature, humidity, and voltage with within 5 minutes. When the average value of dissolved variables SENSOR_tem, SENSOR_hum, and SENSOR_vol oxygen is lower than 5 mg/L, the oxygen enrichment in MOOS-IvP alogview are shown in Figures 8 and 9. equipment will start and run automatically. When the av- erage value of dissolved oxygen in this period is higher than 5.5 mg/L, it will stop. When the dissolved oxygen is lower 4.2. Dissolved Oxygen Control. (e minimum value of dis- than 5 mg/L again, it will start again until the accumulated solved oxygen can be set according to the actual situation of time is 30 minutes. (erefore, the dissolved oxygen in the the water body. In the software and APP, the default value of water is not less than 5 mg/L, and the self-purification Journal of Robotics 9 capacity of the water is forced to be improved. (e minimum Wenxia Zhang analyzed and interpreted the obtained data value of dissolved oxygen and automatic aeration time can regarding the navigation and control experiment. be set on the remote terminal. (e minimum value of dissolved oxygen can be set to 4 mg/L, and the maximum Acknowledgments value can be set to 6 mg/L. Moreover, the dissolved oxygen value when the aeration pump is shut down is 0.5 mg/L (e project is financially supported by the Open Fund higher than the minimum value, which can prevent frequent Project of Key Laboratory of Ocean Observation Technol- start-up and stop of the aeration device. (e maximum ogy, MNR (2021klootA10). aeration time is 30 min, and the minimum aeration time is 30 min. (rough the 4G Internet of (ings technology, the References remote interconnection and monitoring of multiple moni- toring USV platforms are realized. (rough the software [1] B. Qin, G. Zhu, G. 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