1. Introduction
In recent years, the use of autonomous offshore vehicles, which includes autonomous underwater vehicles (AUVs) and unmanned surface vehicles (USVs), for marine interventions has attracted increasing interest from research scientists, maritime industries, and the military. These interventions include several activities such as offshore surveillance, offshore target detection, seabed explorations, or search and rescue (SAR) missions. Additionally, the use of multi-robot platforms can improve the performance in these activities, as they can include above and below-water characterization. Regarding a multi-robot platform, Vasilijević et al. [
1] presented the co-operative robotic system consisting of an AUV and a USV for ocean sampling and environmental monitoring. In [
2], the study used a heterogeneous collaborative system of above, surface, and underwater robots to obtain a multi-domain awareness on a floating target. The heterogeneous system consists of a USV, an AUV, and an unmanned aerial vehicle (UAV). Additionally, Gu et al. [
3] presented a homogeneous study, where a guidance and control law design method for coordinated path following of networked under-actuated robotic USVs under directed communication links. In [
4], the control scenario simulated a homogeneous AUV fleet to study formation tracking control and collision-obstacle avoidance.
To accomplish the target detection in the offshore environment, the availability of accurate USV and AUV mathematical models is crucial for simulation study purposes, controller design, and development. The theoretical six-degrees-of-freedom (DOFs) dynamic model [
5], based on nonlinear equations of motion, can be used for the design and modeling of the AUV. Equally, the USV can use the same dynamic model of the AUV but with reduced order for the three DOFs horizontal plane control (surge, sway, and yaw motions). Several tools can help to obtain the coefficients of the dynamic model equations and the necessary transfer functions of each vehicle. These tools can include the parameter estimation from MATLAB-Simulink [
6], and the system identification (SI) [
7,
8], introduced to develop the mathematical model using field test data. In [
9], SI of the maneuvering data determined the hydrodynamic coefficients of a USV. Also, the mathematical model of the USV includes the propulsion and power system. Commonly, the rudder and propeller, or waterjet propulsion systems provide the heading and the speed control of most existing USVs. In [
10], a twin waterjet propelled USV was modeled based on SI, but it neglects the calculation for the dynamics of the propulsion system.
Target detection in offshore environments is a fundamental activity that combines different perception sensors. Numerous studies use passive (stereo cameras) or active (LiDAR or radar) perception methods to obtain situational awareness of a USV. Nonetheless, most of the obstacle detection methods rely on depth measurements, in which LiDAR sensors are the most reliable method of obtaining depth data. Correspondingly, sonar devices are still the most convenient option for collecting data on underwater environments. Mechanical imaging sonar, multibeam, profiler, or sidescan are some of the main sonar imaging and ranging devices. For the target detection with sonar devices, how detectable is a target is mainly dependent on the physical characteristics of the target and acoustic signal. Some studies use sonar devices for target detection capabilities, as in [
11], where a profiler sonar was adopted for obstacle detection. According to [
12], a method for underwater obstacle detection (standard buoy) was developed using forward-looking sonar and a probabilistic local occupancy grid.
Correct localization and navigation are crucial to ensure the accuracy of the gathered data for all these applications. Above the water surface, most of the autonomous systems rely on radio or global positioning and spread-spectrum communications, as a GPS-compass installed in the USV platform. However, those signals propagate only in short distances in an underwater scenario, where acoustic-based systems perform better. Regarding underwater navigation, the three fundamental methods are dead-reckoning (DR) and inertial navigation systems (INS), acoustic navigation, and geophysical navigation techniques [
13]. These navigation methods require specific survey and navigation sensors installed in the AUV. The Girona 500 [
14] is an example of AUV that performs the traditional dead-reckoning navigation utilizing a doppler velocity log (DVL) and a solid-state attitude and heading reference system (AHRS). Also, the absolute position can be obtained through a GPS when the vehicle is on the surface and using an ultra-short baseline (USBL) while underwater. The high-accuracy USBL system allows the localization of the AUV and the communication between the vehicle and the surface unit. In [
15], the study provided a navigation algorithm for an underwater vehicle with a Kalman filter to estimate the error state via measurement residuals from aiding sensors. These aiding sensors incorporate an attitude sensor, a DVL, a long-baseline (LBL) system, and a pressure sensor. In acoustic navigation techniques, acoustic transponders and modems perform localization by measuring the time-of-flight of signals from acoustic beacons or modems. USBL navigation allows an AUV to localize itself relative to a USV, and it provides an efficient and reliable acoustic communication network [
16]. In [
17], the study presented the design and implementation of an USBL-aided navigation approach for an AUV in a two-parallel extended Kalman filter (EKF). It also includes the measurements provided by a DVL, a Visual Odometer, an inertial measurement unit (IMU), a pressure sensor, and a GPS.
Safe and adequate control of the offshore vehicles depends notably on proper guidance, navigation, and control (GNC) systems. This study adopts a path-following as the guidance system for both offshore platforms. The path-following approach is closer to practical engineering, and it is easier to implement than trajectory tracking. A generally used method for path-following in autonomous vehicles is the named line-of-sight (LOS) guidance. LOS guidance is classified as a three-point guidance scheme, involving a commonly stationary reference point along with the interceptor and the target [
5]. In [
18], the study developed a guidance-based algorithm for path-following using the LOS algorithm in offshore operations. Additionally, in [
10], a path-following with obstacle avoidance based on the safety boundary box approach was implemented in a USV with a LOS-based guidance system.
Due to the co-operative offshore system in this study, it becomes necessary to fuse information obtained from the individual vehicles. Robot Operating System (ROS) has been an effective tool when working with multi-robot systems. This tool is a flexible framework for writing robot software and provides the tools to acquire sensors’ data, process it, and generate the necessary response for the vehicle actuators [
19]. Multi-robot systems can either be centralized with a ROS master node at the ground control station (GCS) or decentralized with each autonomous vehicle (AV) running an independent ROS master. In the case of the decentralized control techniques, they are more flexible, profitable, and generally reduce the communication network requirements compared with centralized control [
20]. However, they are also more challenging due to obstacles, uncertainties, and communication constraints, such as noises, delays, dropouts, or failures. In this case, the multi-master approach provides a solution where each vehicle keeps its own ROS master and also exchange the necessary information with other components of the multi-robot system. In [
21], they proposed a package that efficiently developed multi-master architectures.
In the presented manuscript, the mathematical model of the USV consists of the simplified three DOFs dynamic model [
5], where their parameters are obtained from field test data using the parameter estimation tool. Additionally, the waterjet model has been included in the mathematical model of the USV using data from the manufacturer and transfer functions based on SI. The AUV platform considered in this study does not incorporate a DVL, neglecting the velocity feedback of the vehicle. However, the installed USBL provides an absolute position and a communication link between the USV and the AUV. Thus, the AUV platform includes a basic setup for underwater localization, but it is not able to precisely locate the vehicle underwater. The path-following algorithm uses the LOS approach for heading control to simplify the guidance control of the AUV, keeping a constant depth and constant surge speed. The target detection algorithm uses a modular ROS architecture to provide a computationally cheap and simple implementation in both offshore platforms. Furthermore, the offshore system includes two different perception sensors based on the same target detection algorithm. Finally, a multi-master architecture is in charge of the interaction between the AUV and USV, providing an easy plug-and-play solution for the multi-robot system.
In this work, a model-based GNC architecture for a co-operative autonomous offshore system is proposed for target detection using multi-sensor technology. In
Section 2, the USV modeling and simulation are presented using the parameter estimation tool to define the waterjet and USV maneuvering model. Furthermore, this section includes an overview of the USV and AUV platforms. Then, in
Section 3, the GNC system for the co-operative tasks is included using the LOS-based guidance system for control. The target detection algorithm is developed using a mechanical imaging sonar at the AUV and a LiDAR at the USV as the primary perception sensor for underwater and surface inspection, respectively. Finally, in
Section 4, the implementation of a GNC architecture is described as modular and multilayer for the multi-robot system. A control scenario in a field test is shown in this section to validate the proposed target detection algorithm.
5. Conclusions and Future Work
This article was concerned with the target detection using multi-sensor technology in a co-operative autonomous offshore system. The offshore system had a USV and an AUV, and the fundamental purpose of the algorithm was to detect an underwater target in a preplanned coverage area. The mathematical model of the USV, including also the waterjet propulsion system model, was presented to verify the designed GNC architecture. This model included parameter estimation methods to obtain the dynamic coefficients using field test data for both surge and yaw motions. This study developed a basic target detection algorithm for any offshore perception sensors, showing the results for a mechanical imaging sonar at the AUV and a LiDAR at the USV. The guidance system included the LOS model for path-following on both platforms. After designing the GNC architecture, both vehicles incorporated a system implementation of the modular approach with high, intermediate, and low-level controls. The experimental results showed a field test control scenario that presents the capabilities and adequate performance of the target detection algorithm.
Future work will include an accurate mathematical model of the AUV for simulation, which requires the complete navigation data (position, velocity, and acceleration feedback) from the vehicle. Additionally, the coverage path planning can replace the straight-line trajectory of this study, having more coverage area and increasing the capabilities of the system. The AUV scenario will include the capabilities of making decisions in the presence of several obstacles, and further navigational sensors will be installed for more precise localization of the AUV (e.g., DVL). Finally, future work will also include additional platforms into the system, as it could be other USV or AUV, or even a UAV, which would increase the capabilities of the system working in the air.