Event-Triggered Dual-Mode Adaptive MPC Obstacle Avoidance for ROVs

To address the challenge of balancing path-tracking performance and obstacle-avoidance safety for remotely operated vehicles (ROVs) in complex underwater environments, this paper proposes an autonomous control method integrating model predictive control (MPC), a disturbance-observer-based event-triggered mechanism, and dual-mode intelligent obstacle avoidance. First, a discrete-time prediction model is derived from the four-degree-of-freedom dynamic model of the ROV, and an MPC-based path-tracking controller is developed to ensure stable tracking of the reference trajectory. Second, a dual-mode obstacle-avoidance controller is designed for two typical hazardous scenarios: underwater obstacles and seabed peaks. Based on obstacle-risk assessment, forward guidance points, and a penalty function, the controller guides the ROV safely around obstacles and enables it to rapidly return to the reference trajectory after completing the avoidance maneuver. Furthermore, the disturbance-observer-based event-triggered mechanism improves computational efficiency while maintaining real-time control performance. Simulation shows that the minimum distances from obstacle safety boundaries and the seabed are 0.08 m and 3.54 m, respectively. The event-triggered ratios of the three axes are 6.57%, 15.46%, and 41.44% in simulation, and 4.90%, 20.62%, and 7.29% in pool experiments. Pool tests further verify safe obstacle avoidance with a minimum obstacle-boundary distance of 0.08 m and a minimum seabed distance of 0.33 m, demonstrating the feasibility of the proposed method.