Due to the nonlinearity of the deep-seafloor and complexity of the hydrodynamic force of novel structure platforms, realising an accurate motion mechanism modelling of a deep-sea landing vehicle (DSLV) is difficult. The support vector regression (SVR) model optimised through particle swarm optimisation (PSO) was used to complete the black-box motion modelling and vehicle prediction. In this study, first, the prototype and system composition of the DSLV were proposed, and subsequently, the high-dimensional nonlinear mapping relationship between the motion state and the driving forces was constructed using the SVR of radial basis function. The high-precision model parameter combination was obtained using PSO, and, subsequently, the black-box modelling and prediction of the vehicle were realised. Finally, the effectiveness of the method was verified through multi-body dynamics simulation and scaled test prototype data. The experimental results confirmed that the proposed PSO–SVR model could establish an accurate motion model of the vehicle, and provided a high-precision motion state prediction. Furthermore, with less calculation, the proposed method can reliably apply the model prediction results to the intelligent behaviour control and planning of the vehicle, accelerate the development progress of the prototype, and minimise the economic cost of the research and development process.

The deep-sea landing vehicle (DSLV) swarm exploration system is a novel platform for the detection of marine mineral resources. A high-precision cooperative localization system with Ultra-Short Baseline (USBL), Doppler Velocity Log (DVL), and electronic compass (EC) plays a vital role in the DSLV swarm exploration system. However, DVL measurements can be seriously interrupted due to the complex operational underwater environment, leading to unstable localization performance. The accuracy of the cooperative localization system could be further degraded by the persistent rubber track slippage during the vehicle’s movement over the soft seabed. In this study, a data-driven cooperative localization algorithm with a velocity prediction model is proposed to improve the positioning accuracy of DSLV under track slippage. First, a velocity prediction model for DVL measurements is constructed using multi-output least squares support vector regression (MLSSVR), and a genetic algorithm (GA) is further employed to optimize the model’s hyperparameters in order to enhance the robustness of the framework. Furthermore, the outputs of MLSSVR are fed into a DSLV position estimation framework based on the Unscented Kalman Filter (UKF) to improve localization accuracy in the presence of DVL failures. To validate the proposed method, the RecurDyn multibody dynamics simulation platform is applied for data synthesis, accounting for both the impact of the soft seabed and real-world motion simulation. The experimental results indicate that during DVL failure, the proposed algorithm can effectively compensate for the cooperative localization errors caused by track slippage, thereby significantly improving the accuracy and reliability of the DSLV cooperative localization system.

To ensure the safety and energy efficiency of autonomous sampling operations for a deep-sea landing vehicle (DSLV), the Safety Energy-Dynamic Window Approach (SE-DWA) algorithm was proposed. The safety assessment sub-function formed from the warning obstacle zone and safety factor addresses the safety issue arising from the excessive range measurement error of forward-looking sonar. The trajectory comparison evaluation sub-function with the effect of reducing energy consumption achieves a reduction in path length by causing the predicted trajectory to deviate from the historical trajectory when encountering “U”-shaped obstacles. The pseudo-power evaluation sub-function with further energy consumption reduction ensures optimal linear and angular velocities by minimizing variables when encountering unknown obstacles. The simulation results demonstrate that compared with the Minimum Energy Consumption-DWA algorithm, the SE-DWA algorithm improves the minimum distance to an actual obstacle zone by 68% while reducing energy consumption by 11%. Both the SE-DWA algorithm and the Maximum Safety-DWA (MS-DWA) algorithm ensure operational safety with minimal distance to the actual obstacle zone, yet the SE-DWA algorithm achieves a 24% decrease in energy consumption. In conclusion, the path planned by the SE-DWA algorithm ensures not only safety but also energy consumption reduction during autonomous sampling operations by a DSLV in the deep sea.