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 present study proposes a low-energy consumption multipoint sampler carried by a deep-sea landing vehicle (DSLV) to meet the requirements of time series sampling in local areas and location series sampling in wide areas, and an optimization method of sampling structure based on least-squares support-vector machine (LSSVM) surrogate model and a multi-objective particle swarm optimization (MOPSO) algorithm. First, the overall structure and core components, such as the multipoint sampler’s sampling structure, were designed. The optimization variables were the cone angle, sampling tube inner diameter, and sampling tube inner hole length, which were determined by considering the force with which the sampling structure penetrates the seafloor sediment. Then, the sampling process was simulated by the finite element method-smoothed particle hydrodynamics (FEM-SPH) method, while the accurate LSSVM model of force required for sampling and sampling tube volume was established. Finally, the MOPSO algorithm was used for multi-objective optimization of model parameters of sampling structure. The optimal model of sampling structure that can provide theoretical support for the optimal design of multipoint sampler effectively reduces energy consumption and improves sampling efficiency by force required for sampling 25.89% lower and sampling tube volume 34.81% higher than the original model.