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This study presents a swarm-control algorithm to overcome the limitations inherent to single-object systems. The leader–follower swarm-control method was selected for its ease of mathematical interpretation and theoretical potential for the unlimited expansion of followers. However, a known drawback of this method is the risk of swarm collapse when the leader breaks down. To address this, a fault-coping algorithm was developed and supplemented to the leader–follower swarm-control method, which enabled the detection and responsive handling of failures, thereby ensuring mission continuity. Comprehensive data, including voltage, current, thruster speed, position, and heading angle were acquired and analyzed using sensors on unmanned surface vehicles (USVs) to monitor potential failures. In the case of a failure, such as thruster malfunction, the nearest USV seamlessly takes charge of the mission under the guidance of the fault-coping algorithm. The leader–follower swarm-control and fault-coping algorithms were successfully validated through actual sea area tests, which confirmed their operational efficacy. This study affirms the well-formed nature of the USV swarm formation and demonstrates the effectiveness of the fault-coping algorithm in ensuring normal mission performance under the virtual failure scenarios applied to the leader USV.

To overcome the limitations of a single unmanned surface vehicle (USV), this study investigated the swarm control algorithms of USVs. Among various swarm control methods, a leader–follower swarm control method was selected and studied. The performance of the swarm algorithm proposed in this study was verified through an actual sea area test. A USV was designed and manufactured by dividing the power and communication parts. The power system was equipped with a coulometer to monitor the battery state in real time to protect the system through a switch linked to the coulometer in the case of abnormalities in the battery and to prevent accidents. In addition, a communication system was established to process the sensor data and camera image data of the USV in real time. Consequently, the desired swarm formation was achieved through the separately constructed swarm control algorithm. Before the actual sea area test, individual performance tests of each sensor were conducted. Finally, the performance of the swarm algorithm was verified by conducting a sea area test. The performance of the controllers was confirmed to be good, and the swarm formation was confirmed to be successful.