Aerial-aquatic Vehicles Research Articles

A high maneuvering motion strategy and stable control method for tandem twin-rotor aerial-aquatic vehicles near the water surface

The maneuverability and stealth of aerial-aquatic vehicles (AAVs) is of significant importance for future integrated air-sea combat missions. To improve the maneuverability and stealth of AAVs near the water surface, this paper proposed a high-maneuverability skipping motion strategy for the tandem twin-rotor AAV, inspired by the motion behavior of the flying fish to avoid aquatic and aerial predators near the water surface. The novel tandem twin-rotor AAV was employed as the research subject and a strategy-based ADRC control method for validation, comparing it with a strategy-based PID control method. The results indicate that both control methods enable the designed AAV to achieve high stealth and maneuverability near the water surface with robust control stability. The strategy-based ADRC control method exhibits a certain advantage in controlling height, pitch angle, and reducing impact force. This motion strategy will offer an inspiring approach for the practical application of AAVs to some extent.

Design and Improvement of Tandem Twin-Rotor Aerial-Aquatic Vehicle Based on Numerical Analysis

Design and Improvement of Tandem Twin-Rotor Aerial-Aquatic Vehicle Based on Numerical Analysis

Design and Demonstration of a Tandem Dual-Rotor Aerial–Aquatic Vehicle

Aerial–aquatic vehicles (AAVs) hold great promise for marine applications, offering adaptability to diverse environments by seamlessly transitioning between underwater and aerial operations. Nevertheless, the design of AAVs poses inherent challenges, owing to the distinct characteristics of different fluid media. This article introduces a novel solution in the form of a tandem dual-rotor aerial–aquatic vehicle, strategically engineered to overcome these challenges. The proposed vehicle boasts a slender and streamlined body, enhancing its underwater mobility while utilizing a tandem rotor for aerial maneuvers. Outdoor scene tests were conducted to assess the tandem dual-rotor AAV’s diverse capabilities, including flying, hovering, and executing repeated cross-media locomotion. Notably, its versatility was further demonstrated through swift surface swimming on water. In addition to aerial evaluations, an underwater experiment was undertaken to evaluate the AAV’s ability to traverse narrow underwater passages. This capability was successfully validated through the creation of a narrow underwater gap. The comprehensive exploration of the tandem dual-rotor AAV’s potential is presented in this article, encompassing its foundational principles, overall design, simulation analysis, and avionics system design. The preliminary research and design outlined herein offer a proof of concept for the tandem dual-rotor AAV, establishing a robust foundation for AAVs seeking optimal performance in both water and air environments. This contribution serves as a valuable reference solution for the advancement of AAV technology.

TJ-FlyingFish: An Unmanned Morphable Aerial–Aquatic Vehicle System

In this paper, we present in this work a fairly complete process for developing an unmanned aerial–aquatic vehicle system, TJ-FlyingFish, which includes an innovative design methodology of the aerial–aquatic platform and the cross-medium localization, dynamics modeling, and flight control systems. The development faces the challenge how to manipulate locomotion effectively in both water and air which presents substantial differences in fluid properties. Additionally, there are difficulties in perception and navigation because of the discontinuity of mediums. To cope with these challenges, we designed an innovative unmanned aerial–aquatic vehicle with an optimized dual-speed and tilting propulsion configuration. The rotors/propellers operate in different ranges of rotating speed in the two different mediums, providing sufficient thrust and ensuring output efficiency. Besides, thrust vectoring is achieved by rotating each propulsion unit around its mounted arm, facilitating agile underwater cruising. Another key component of our approach is a sophisticated multi-sensor-based cross-medium localization system that combines SLAM, sensor synchronization, and data capture mechanisms, enabling seamless transitions between aerial and aquatic environments, and supporting autonomous operations. The results are fully validated through actual flight experiments.

Trajectory-Following Control of an Unmanned Aerial–Aquatic Vehicle under Complex Coupling Interferences

This article explores trajectory-following control for an unmanned aerial–aquatic vehicle (UAAV) navigating complex ocean disturbances and the interplay of air–seawater coupling factors. First, leveraging the backstepping technology, an adaptive algorithm is proposed to tackle the attitude and position following. Additionally, a nonlinear observer is crafted to estimate complex ocean disturbances. The UAAV model, characterized by six degrees of freedom (DOF) and nonlinear properties, experiences significant pose changes when emerging from water, underscoring the critical importance of precise pose control. Finally, stability analysis and numerical simulations are demonstrated to verify the feasibility and validity of the proposed control strategies.

Transmedia Performance Research and Motion Control of Unmanned Aerial–Aquatic Vehicles

This paper presents an improved motion controller based on the backstepping method to address nonlinear control challenges in unmanned aerial–aquatic vehicles (UAAVs), enabling them to navigate between two different media. The nonlinear control approach is applied to UAAV motion control, incorporating filters to improve stability. The study designs motion controllers for three UAAV phases: underwater, in the air, and transitioning between media. Fluid simulations of the emergence process of the UAAV for future field experiments were conducted. By fine-tuning the simulations, a comprehensive understanding of the vehicle’s performance is obtained, offering crucial insights for the development of subsequent control systems. Simulation results confirm the controller’s ability to achieve target trajectory tracking with control system responses that meet practical requirements. The controller’s performance in attitude control and trajectory tracking is verified in underwater gliding, transmedia transitions, and airborne phases, demonstrating its effectiveness.

Exploring storm petrel pattering and sea-anchoring using deep reinforcement learning

Developing hybrid aerial-aquatic vehicles that can interact with water surfaces while remaining aloft is valuable for various tasks, including ecological monitoring, water quality sampling, and search and rescue operations. Storm petrels are a group of pelagic seabirds that exhibit a unique locomotion pattern known as ‘pattering’ or ‘sea-anchoring,’ which is hypothesized to support forward locomotion and/or stationary posture at the water surface. In this study, we use morphological measurements of three storm petrel species and aero/hydrodynamic models to develop a computational storm petrel model and interact it with a hybrid fluid environment. Using deep reinforcement learning algorithms, we find that the storm petrel model exhibits high maneuverability and stability under a wide range of constant wind velocities after training. We also verify in the simulation that the storm petrel can use its ‘pattering’ or ‘sea-anchoring’ behavior to achieve different biomechanical sub-tasks (e.g. weight support, forward locomotion, stabilization) and adapt it under different wind speeds and optimization objectives. Specifically, we observe an adjustment in storm petrel’s movement patterns as wind velocity increases and quantitively analyze its biomechanics underneath. Our results provide new insights into how storm petrels achieve efficient locomotion and dynamic stability at the air–water interface and adapt their behaviors to different wind velocities and tasks in open environments. Ultimately, our study will guide the design of next-generation biomimetic petrel-inspired robots for tasks requiring proximity to the water interface and efficiency.

Design and Theoretical Research on Aerial-Aquatic Vehicles: A Review

Design and Theoretical Research on Aerial-Aquatic Vehicles: A Review

Use of Superhydrophobic Surfaces for Performance Enhancement of Aerial–Aquatic Vehicles

Use of Superhydrophobic Surfaces for Performance Enhancement of Aerial–Aquatic Vehicles

Off-shore and underwater sampling of aquatic environments with the aerial-aquatic drone MEDUSA

Monitoring of aquatic habitats for water quality and biodiversity requires regular sampling, often in off-shore locations and underwater. Such sampling is commonly performed manually from research vessels, or if autonomous, is constrained to permanent installations. Consequentially, high frequency ecological monitoring, such as for harmful algal blooms, are limited to few sites and/or temporally infrequent. Here, we demonstrate the use of MEDUSA, an Unmanned Aerial-Aquatic Vehicle which is capable of performing underwater sampling and inspection at up to 10 m depth, and is composed of a multirotor platform, a tether management unit and a tethered micro Underwater Vehicle. The system is validated in the task of vertical profiling of Chlorophyll-a levels in freshwater systems by means of a custom solid sample filtering mechanism. This mechanism can collect up to two independent samples per mission by pumping water through a pair of glass-fibre GF/F filters. Chlorophyll levels measured from the solid deposits on the filters are consistent and on par with traditional sampling methods, highlighting the potential of using UAAVs to sample aquatic locations at high frequency and high spatial resolution.

Use of Superhydrophobic Surfaces for Performance Enhancement of Aerial–Aquatic Vehicles

Aerial–aquatic robotic vehicles show great potential in assisting in disaster response and environmental monitoring. However, to undertake these missions, they need to overcome the challenges of power requirements for takeoff and the difficulty of transitioning reliably between the air and water media. The use of superhydrophobic surfaces offers solutions to these challenges by reducing the wetted surface area of such robotic vehicles. In this article, a range of superhydrophobic surfaces is analyzed for wettability and robustness performance to ascertain their benefits as a design feature for drag reduction in aerial–aquatic robotic vehicles. The silicon dioxide nanoparticle spray coating show the most superhydrophobicity measuring a static water contact angle of 174.8°. The coating's robustness tests yield a similar performance to that of laser‐engraved brass with 200 μm groove separation, displaying a contact angle of 133.0° after ten finger strokes. The silicon dioxide nanoparticle spray is then used for drag reduction testing due to its ease of coating complex 3D geometries among the techniques explored in this study. The spray is applied to the hull of a sailing–flying robot, which resulted in the robot's drag reduction averaging 40% in the hydroplaning regime.

Cooperative path planning for air–sea heterogeneous unmanned vehicles using search-and-tracking mission

The cooperative path planning problem for air–sea heterogeneous unmanned vehicles using search-and-tracking mission is studied in this paper. Due to the limitations of single unmanned vehicle, the air–sea heterogeneous system, composed of an unmanned aerial–aquatic vehicle (UAAV), an unmanned surface vehicle (USV) and an autonomous underwater vehicle (AUV), is introduced to compensate for the singleness of unmanned vehicle and complete more complicated marine missions. One search-and-tracking mission containing the search phase and tracking phase is presented to accomplish the underwater target tracking mission with goals of minimizing the search time and finding the shortest tracking path. In the search phase, according to different initial positions of three unmanned vehicles, the corresponding task allocation algorithms are proposed to determine the location of the target efficiently and minimize the search time by allocating different task for three unmanned vehicles. Meanwhile, the unmanned vehicles only know the approximate area containing the target before the task execution and move in the direction of the area for searching target. In the tracking phase, an improved particle swarm optimization algorithm is addressed to solve the path planning problem with obstacles. Simulation results show that the underwater target can be detected using search-and-tracking mission in an air–sea heterogeneous system efficiently and accurately.

An Adaptable Flying Fish Robotic Model for Aero- and Hydrodynamic Experimentation.

Flying fishes (family Exocoetidae) are known for achieving multi-modal locomotion through air and water. Previous work on understanding this animal's aerodynamic and hydrodynamic nature has been based on observations, numerical simulations, or experiments on preserved dead fish, and has focused primarily on flying pectoral fins. The first half of this paper details the design and validation of a modular flying fish inspired robotic model organism (RMO). The second half delves into a parametric aerodynamic study of flying fish pelvic fins, which to date have not been studied in-depth. Using wind tunnel experiments at a Reynolds number of 30,000, we investigated the effect of the pelvic fin geometric parameters on aerodynamic efficiency and longitudinal stability. The pelvic fin parameters investigated in this study include the pelvic fin pitch angle and its location along the body. Results show that the aerodynamic efficiency is maximized for pelvic fins located directly behind the pectoral fins and is higher for more positive pitch angles. In contrast, pitching stability is neither achievable for positive pitching angles nor pelvic fins located directly below the pectoral fin. Thus, there is a clear a trade-off between stability and lift generation, and an optimal pelvic fin configuration depends on the flying fish locomotion stage, be it gliding, taxiing, or taking off. The results garnered from the RMO experiments are insightful for understanding the physics principles governing flying fish locomotion and designing flying fish inspired aerial-aquatic vehicles.

3D trajectory optimization of the slender body freely falling through water using cuckoo search algorithm

The problem of trajectory optimization of underwater slender bodies is commonly encountered during offshore operations such as deploying the anchor into seabed at the minimum locating error and releasing unmanned aerial-aquatic vehicle (UAAV) into water for the largest terminal speed. To solve these problems, an accurate trajectory prediction of the freely falling slender body together with an efficient optimization algorithm are required to build up the optimization framework which aims at satisfying a specific task.In this paper, firstly, the improved 3D dynamic model is proposed which can properly capture the 3D effect by taking into account lateral forces and motions induced by the asymmetric vortex shedding and axial rotations during crossing flow. By simulating the trajectory of a freely falling cylinder, the proposed 3D dynamic model shows much better agreement with the experiment compared with traditional 2D dynamical models which validates its capability into modelling the freely falling motion of the cylinder. The effects of axial rotating rates are systematically studied and discussed for freely falling with different drop angles.In order to obtain the optimal trajectory under a specific optimization index, the Cuckoo Search (CS) optimization algorithm is implemented into the 3D dynamical model by using random walk and Lévy flight respectively and compared with two popular algorithms: Genetic Algorithm (GA) and Particle Swarming Optimization (PSO). Because of the stronger exploration ability induced by Lévy flight, the proposed CS algorithm with Lévy flight is found performing much better than the CS algorithm with random walk and even GA and PSO both in optimizing the unconstrained single objective optimization problem: terminal position error and the constrained single objective optimization problem: the maximum terminal velocity.

Trajectory optimization of an unmanned aerial–aquatic rotorcraft navigating between air and water

Unmanned aerial–aquatic vehicles are a new type of aircraft that can navigate in air and underwater. An unmanned aerial–aquatic rotorcraft (UAAR) is introduced to complete the task of navigating between air and underwater, and the trajectory optimization problem for this task is focused on in this study. The dynamics of a four-axle rotorcraft with eight rotors operating in air and underwater is described. On this basis, the trajectory optimization model is established, wherein the constraints on control variables and states in different media are included. The optimization index is denoted as the weighted sum of the terminal states. In view of the weakness of the teaching- and learning-based optimization (TLBO) algorithm, the formula for updating the individual grade in the teaching process is modified. Thus, this ensures that the algorithm avoids converging at the local optimum and improves the solution quality. Finally, an improved TLBO (ITLBO)-based trajectory optimization method for UAAR navigating between air and water is developed. The control variables are discretized with respect to height at a set of Chebyshev collocation points to reduce the terminal error of states, and the values of control variables at other heights are obtained via interpolation. In the simulation studies, the ITLBO-based method exhibits better performance in terms of optimizing the index when compared to the other two algorithms. Furthermore, the effects of the distribution and number of collocation points on the results are analyzed.

Survey on the Development of Aerial–Aquatic Hybrid Vehicles

As the development of mobile robots matures, there is an increasing amount of interest in expanding the functionality of such robots through developing multimodal locomotion. As compared to land–water or land–air hybrids, the design of air–water vehicles is much less straightforward due to the fact that both mediums are three-dimensional fluid spaces and there is inherent disparity in fluid properties between them. As such, the development of these vehicles has received limited attention until very recently. Nevertheless, the potential applications of such vehicles range widely from military surveillance, oceanic data collection to heterogeneous robot team operation, which has led to an increasing number of projects working on aerial–aquatic hybrid mobility. In this paper, we discuss the fundamental challenges associated with aerial–aquatic hybrid locomotion as well as the necessary trade-offs in design decisions. We also summarize and review the existing work and prototypes of aerial–aquatic vehicles that have been designed thus far, analyzing the range of solutions that have been adopted to solve the aforementioned challenges. Lastly, the limitations of these solutions are analyzed to offer a perspective on how future developments in the area can enable greater functionality for the concept.

Thruster Allocation and Mapping of Aerial and Aquatic Modes for a Morphable Multimodal Quadrotor

Although there have been several proposed methods and existing prototypes demonstrating the functionality of aerial–aquatic vehicles, most are fundamentally aerial vehicles with varying degrees of operability when submerged. We aim to improve the balance of aerial–aquatic functionality by proposing a morphable vehicle structure, where the rotors can be tilted to allow thrust vectoring when underwater. Here, in this article, we present a revised prototype implementing the idea and define the details of the aerial and aquatic modes as they are distinctly different from any existing configurations and require unique RC input to actuator output mappings. Based on the first principles model derived from the vehicle structure, we developed an appropriate thruster allocation and mapping system that will allow a single user to control the vehicle manually in both aerial and aquatic modes.

Multi-phase trajectory optimization for an aerial-aquatic vehicle considering the influence of navigation error

The environment-induced multi-phase trajectory optimization problem is studied in this paper, and the underwater target tracking task is focused on. The task is finished by an aerial-aquatic coaxial eight-rotor vehicle and is divided into two phases, i.e., the diving phase and the underwater navigation phase. The dynamic model and constraints on angular velocity of rotor in each phase are established to understand the motion characteristic. Then the model of navigation information and terrain matching are contained in the trajectory optimization model to reflect the influence of underwater navigation error on the quality of trajectory. Correspondingly, the forms of collision detection and cost function are changed to adapt to the inaccurate navigation information. To obtain the trajectory with the minimum terminal position error, an improved teach & learn-based optimization (ITLBO) algorithm is developed to strengthen the influence of individual historical optimal solution. Besides, Chebyshev collocation points are applied to determine the locations of control variables. Simulation results demonstrate that the established navigation error-based trajectory optimization model can reflect the real situation of multi-phase task. Especially, it is able to calculate the collision probability between the vehicle and the obstacle when GPS is unavailable underwater, thus ensuring the safety of underwater navigation. Compare to other common effective algorithms, the proposed ITLBO algorithm is in general more suitable for solving this problem because it is swarm-based and can obtain good solution without worrying about the inappropriate values of user-defined parameters.

Coordinated path planning for an unmanned aerial-aquatic vehicle (UAAV) and an autonomous underwater vehicle (AUV) in an underwater target strike mission

Unmanned system has become more and more popular as it can adapt to diverse environments and has prospective applications. Especially, the coordination among heterogeneous vehicles is capable of completing complicated tasks, which is often beyond the ability of homogeneous vehicles. In this paper, the underwater target strike mission is concentrated, and the mission is completed by the coordination between a UAAV and an AUV. UAAV and AUV are deployed in this mission because UAAV has strong search ability in air and can communicate with AUV directly after it dives into water. Firstly, to decompose the problem, the mission is divided into two phases, i.e., single flying of UAAV and underwater coordination between UAAV and AUV. In the coordinated path planning model, the motion of vehicles, the constraints in different media and the optimization index in each phase are all formulated into mathematical forms. Based on the particle swarm optimization (PSO) algorithm, the collocation points are used to determine the locations of control variables. Those points can reduce the computation load and improve the solution quality, and they are distributed by height and moment according to the forms of constraints in each phase. Besides, the strategy of addressing infeasible solutions is generated to guarantee the normal operation of PSO-based algorithm. Simulation results demonstrate that the proposed two-phase coordinated path planning method can generate coordinated paths, and the obtained results is very close to the optimal solution in theory. Compared to the whole method, the two-phase method can better deal with the complicated constraints in each phase.

Dynamics modeling and trajectory optimization for unmanned aerial-aquatic vehicle diving into the water

Unmanned aerial-aquatic vehicle (UAAV) is a new type of aircraft that can navigate both in air and underwater. Considering that the diving motion of UAAV plays an important role in the performance of UAAV and is under exploration, the dynamics modeling and trajectory optimization problem are studied in this paper. The UAAV model used in this study is introduced firstly, and folded wings are adopted to reduce the drag in the diving process. Among the forces imposed on UAAV, fluid force is the most complicated and is calculated by the forces induced by ideal fluid and viscous fluid respectively. Based on the established dynamic model, the diving process is regarded as a free motion to avoid the instability during the control switch between air and water. Therefore, the trajectory of UAAV is determined by the initial states of diving process. To obtain the satisfactory trajectory under certain optimization index, an adaptive and global-best guided CS algorithm, named as improved cuckoo search (ICS) algorithm, is developed to strength the exploitation ability and search efficiency. Simulation results demonstrate that the established dynamical model of UAAV is rational and can reflect the characteristic of the diving motion. The proposed ICS algorithm performs better than the particle swarm optimization (PSO) algorithm and the standard CS algorithm both in optimizing the elapsed time of diving process and the terminal position error.