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This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications. [ABSTRACT FROM AUTHOR]

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

The local path planning technology of wave gliders (WGs) is faced with two inherent difficulties: the impact of the complex marine environment on the path and the weak maneuverability. In this paper, a fusion algorithm based on the time-stamped collision detection (TCD) and the environmental improved artificial potential field (EAPF) is proposed to solve the above problems, which combines the dynamic model of the WG to guide it by generating a desired heading output. The improved APF (IAPF) is developed to solve the problems of local minimum and target unreachability. The influence of the complex marine environment on the WG is considered during dynamic obstacle avoidance, and the proposed EAPF improves the maneuverability of the WG with the help of environmental field, while reducing the impact of the environment on the path. For dynamic obstacle prediction, different dynamic obstacle models are used, and the TCD is proposed to predict the collision risk between WG and dynamic obstacles to adapt to the weak maneuverability of the WG. The simulation results show that the proposed TCD-EAPF can realize the early avoidance of dynamic obstacles and improve the maneuverability of the WG in obstacle avoidance. • Local path planning algorithm for wave gliders. • A novel time-stamped collision detection based on different obstacle models. • Environmental gravitational potential field was added based on the improved APF. • An effective method of avoiding dynamic obstacles in advance. [ABSTRACT FROM AUTHOR]