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.

Amphibious trans-media aerial-aquatic vehicles (AAVs) harbor substantial potential in marine applications. This study addresses the vulnerability of slender-body AAVs to crosswind interference during hovering, proposing an active disturbance rejection control strategy (ADRC_RA) to reduce the projected area. It can improve positioning precision and lower energy consumption. This research establishes a streamlined and slender AAV mathematical model and analyzes its control constraints. A projection area reduction strategy is then designed to adjust the attitude. The angle estimation process integrates the aircraft's inherent characteristics with interference estimation via an Extended State Observer (ESO). The results demonstrate that, in most cases, the ADRC_RA method reduces position deviation by a factor of three compared to methods that do not reduce the projected area and reduce energy consumption for interference mitigation by two-thirds. The results confirm the effectiveness and robustness of ADRC_RA in improving positioning precision and reducing energy consumption.

The Solar System Boundary Exploration (SSBE) is the focal point for future far-reaching space exploration. Due to the SSBE having many scientific difficulties that need to be studied, such as super-long space exploratorydistance, super-long flighttime in orbit, and super-large communication data delay between the ground and the probe, the probe must have sufficient intelligence to realize intelligent autonomous navigation. The traditional navigation schemes have been unable to provide high accuracy autonomous intelligent navigation independent on the ground for the probe. Therefore, high accuracyintelligent astronomical integrated navigation would provide new methods and technologies for the navigation of the SSBE. The probe of the SSBE is disturbed by multiple sources of solar light pressure and the complex unknown environment during the long cruise operation in orbit. In order to ensure the position stateand velocity state error estimation high accuracy for the probe cruise phase, an autonomous intelligent integrated navigation scheme based on the X-ray pulsar / solar and target planetary Doppler velocity measurement is proposed. The reinforcement Q-learning method is introduced, and the reward mechanism is designed for trial-and-error tuning of stateand observationnoise error covariance parameters. The federated extended Kalman filter (FEKF) based on Q-learning (QLFEKF) navigation algorithm is proposed to achieve high accuracystate estimation of the autonomous intelligence for the SSBE cruise phase. The main advantage of the QLFEKF is that the Q-learning combined with the conventional federated filtering method could optimize the state parameters in real-time, and obtain high position and velocity state estimation (PVSE)accuracy. Compared with the conventional FEKF integrated navigation algorithm, the PVSEnavigation accuracy of the federated filter integrated based the Q-learning navigation algorithm are improved by 55.84% and 37.04% respectively, which present with the higher accuracy and greater capability of the raised autonomous intelligent integrated navigation algorithm. The simulation results show that the intelligent integrated navigation algorithm based on QLFEKF has higher accuracy navigation,and able to satisfy the demandsof autonomous high accuracy for the SSBE cruise phase.