The intelligent decision-making system of a single-body multi-purpose unmanned boat is the core module for its autonomous navigation and mission execution. Through a multi-layered architecture and the integration of multiple technologies, it enables dynamic strategy adjustment, risk avoidance, and the completion of diverse tasks in complex aquatic environments. Based on environmental perception data, combined with mission requirements and preset rules, the system generates optimal action plans through algorithmic models and continuously optimizes the decision-making process. Its operational mechanism can be broken down into five key stages: environmental perception, data fusion, decision generation, mission execution, and dynamic adjustment.
Environmental perception is the prerequisite for intelligent decision-making. Single-body multi-purpose unmanned boats typically carry multiple sensors, including lidar, millimeter-wave radar, optical cameras, infrared thermal imagers, sonar, and inertial navigation systems, forming a multimodal perception network. LiDAR and cameras are responsible for identifying surface obstacles, buoys, shorelines, and dynamic targets; sonar is used to detect underwater terrain and potential threats; and the inertial navigation system provides the boat's attitude and motion parameters. Different sensors complement each other in terms of data accuracy, refresh rate, and applicable scenarios. For example, radar can maintain stable detection even in rainy or foggy weather, while cameras can provide richer visual information in well-lit conditions.
Data fusion is key to improving decision-making accuracy. Raw data collected by multiple sensors needs to be preprocessed, including noise reduction, calibration, and time synchronization, and then integrated using feature-level or target-level fusion algorithms. Feature-level fusion maps data from different sensors to a unified coordinate system, extracting key features (such as obstacle contours and motion trajectories). Target-level fusion, based on the independent detection results of each sensor, uses probabilistic models (such as Bayesian networks) or deep learning algorithms (such as convolutional neural networks) to correlate and deduplicate data, ultimately generating a global environment model. For example, when lidar and cameras simultaneously detect the same obstacle, the system fuses their position and velocity information to form a more accurate obstacle state estimate.
Decision generation depends on the algorithm model and task logic. The system loads the corresponding decision rule base according to the task type (such as patrol, reconnaissance, rescue, or attack). For example, in patrol missions, the system prioritizes planning routes covering designated areas while maintaining a safe distance from the shoreline; reconnaissance missions require adjusting detection strategies based on target characteristics (such as vessel type and activity patterns). Decision-making algorithms typically combine path planning (such as A* and Dijkstra's algorithms) with obstacle avoidance strategies (such as dynamic windowing and artificial potential field methods) to generate the optimal action sequence while meeting mission constraints (such as time and energy consumption). For complex scenarios, the system incorporates reinforcement learning models to optimize decision logic through simulation training or real-world mission data, such as learning how to efficiently avoid other vessels in narrow waterways.
During mission execution, the decision results are translated into control commands, driving the propulsion system, servo motors, and mission payloads (such as weapons and sensor gimbals) to work collaboratively. For example, when the system decides to bypass an obstacle, it adjusts the thruster speed and rudder angle while simultaneously controlling the camera to continuously track the obstacle's movement, ensuring safe passage. During execution, the system continuously monitors the vessel's status (such as remaining battery power and equipment health) and environmental changes. If an anomaly is detected (such as propeller malfunction or sudden severe weather), emergency decisions are triggered, such as switching to a backup power source or returning to port.
Dynamic adjustment is the core advantage of intelligent decision-making. The system uses a closed-loop feedback mechanism to evaluate the effectiveness of decisions in real time and optimize subsequent actions. For example, during obstacle avoidance, if the original planned path becomes invalid due to the appearance of a new obstacle, the system will recalculate a feasible path; if the task priority changes (such as receiving an emergency rescue order), the system will interrupt the current task and reallocate resources. This flexibility allows the single-body multi-purpose unmanned boat to adapt to highly dynamic aquatic environments. For example, in areas where multiple boats converge, the system can predict potential collision risks based on the movement trends of other vessels and adjust its course in advance.
The intelligent decision-making system of the single-body multi-purpose unmanned boat achieves a complete closed loop from environmental understanding to task execution through multi-sensor fusion perception, algorithm model optimization, and a dynamic feedback mechanism. Its core value lies in transforming human experience into reusable decision-making logic while retaining the ability to adapt to emergencies, providing technical support for the widespread application of single-body multi-purpose unmanned boats in fields such as military reconnaissance, marine surveying, and emergency rescue.
