Collision Avoidance Based on Stochastic Model Predictive Control in Collaboration Between ROV and AUV

Due to certain technical limitations of autonomous underwater vehicle (AUV), they cannot completely perform complex tasks independently. When performing complex tasks, coordination between the remote operated vehicle (ROV) and AUV is required. Therefore, collision avoidance is a key technology to ensure vehicle safety. During collision avoidance, AUV need to understand human intentions, make decisions, and perform the corresponding actions. To solve the problems of human intention uncertainty and random noise interference, an AUV collision avoidance strategy based on a dynamic Bayesian network and stochastic model predictive control (SMPC) is proposed in this paper. First, a dynamic Bayesian network is used to assess the probability of AUV collisions in the system. Then, using the properties of Gaussian distribution and related theorems, the objective function is simplified and transformed into a deterministic model predictive control problem. Finally, the intention-exploration item is added to the objective function to better understand human intention. Through the simulations and experiments in specific scenarios, it is verified that the proposed collision avoidance control strategy can safely and effectively control a hybrid system with the coexistence of ROV and AUV.