Maneuvering Ability-Based Weighted Potential Field Framework for Multi-USV Navigation, Guidance, and Control

Abstract

Numerous types of unmanned surface vehicles (USVs) are currently available for different applications with a wide spectrum of maneuvering capabilities. We present a generalized multi-USV navigation, guidance, and control framework adaptable to specific USV maneuvering response capabilities for dynamic obstacle avoidance. The proposed method integrates offline optimal path planning with a safety distance constrained A* algorithm, and an online extended adaptively weighted (EAW) artificial potential field-based path following approach with dynamic collision avoidance, based on USV maneuvering response times. The framework adaptively weighs inter-USV interaction, waypoint following, and collision avoidance based on USV maneuvering capabilities. The EAW system allows USVs with fast maneuvering abilities to react late and slow USVs to react sooner to oncoming moving obstacles gradually, with a carefully designed series of repulsive potential with diminishing weighting along the predicted path of detected moving obstacles, such that a smooth path is followed by the USV group with reduced cross-track error and reduced maneuvering effort. We emphasize the importance of such requirements in constrained and busy maritime environments such as narrow channels in busy harbors. Simulation results validate the proposed EAW artificial potential field framework for different sized multi-USV teams showing reduced cross-track error and maneuvering effort compared to the unweighted or traditional approach, for both slow- and fast-maneuvering multi-USV teams.

Navigating high‐speed unmanned surface vehicles: System approach and validations

“…Singh et al (2020) integrated a constrained A* approach with a decentralised virtual target (VT) guidance approach combined with a potential field based swarm aggregation technique for the cooperative navigation of multi‐USVs. Mina et al (2020) proposed method integrating offline optimal path planning with a safety distance constrained A* algorithm, together with an online extended adaptively weighted (EAW) artificial potential field‐based path following approach with dynamic collision avoidance, based on USV manoeuvring response times. It should be noted that the processes of grid‐based map construction and path searching require a great deal of time with the grid‐based world model needed to be updated according to the change of sensor data.…”

Synchronization Control of Dynamic Positioning Ships Using Model Predictive Control

“…Underway replenishment operation involves cargo transfer between two ships in transit, which is essential for ships performing long-term missions at sea to avoid returning to the base for replenishing storage or personnel [11][12][13]. It is normally formulated into the leader-tracking configuration [14][15][16][17][18][19][20]. Specifically, the leader vessel is controlled to sail on the steady velocity and course independently; however, the follower vessel requires to move upward alongside with a predefined offset to receive cargos.…”

Integral Sliding Mode Based Finite-Time Tracking Control for Underactuated Surface Vessels with External Disturbances

“…A majority of the current literature concentrates on endowing fully actuated surface vessels with attractive properties, such as collision avoidance capability [10], excellent anti-saturation capability [11][12][13], and anti-interference [14]. A practical adaptive slidingmode control scheme is proposed in [11] for an unmanned surface vehicle.…”