多AUV水下协作跟踪围捕方法与实验验证

The tracking hunting problem of Multi-AUV for an escaping target is to study the method for multi-pursuer tracking and capturing the escaper. Due to the complexity of the deep-sea environment and the particularity of hunting AUVs, multi-AUV cooperative tracking hunting is a very challenging research area. In this project, it is focused on the dynamic time-varying underwater environment. Firstly, the multi-sensor information fusion technology is adopted into the AUV underwater signal processing, and the underwater noise interference can be eliminated. At the same time, based on the hardware of ultra short baseline (USBL) positioning system and the underwater acoustic transfer mode of “juggling stop-and-wait”, the information communication and acoustic positioning between the AUVs can be realized. The AUV information can be obtained accurately and the underwater environment map can be generated. By considering the movement velocity constraint of hunting AUVs, the multi-AUV hunting alliance and the path planning algorithm based on a self-organizing map and neighborhood unit navigation are proposed. The principle of neighborhood unit navigation is employed into the method, and the path of hunting AUVs in the alliance can be adaptively updated to ensure that each hunting AUV can satisfy the kinematic constraints, the shortest path and automatic obstacle avoidance in the hunting process. On this basis, considering the deep-sea ocean current and own model uncertainty of hunting AUVs , the tracking control law is designed by combining of the bio-inspired neurodynamics model and sliding mode control ; Finally, a cooperative quantum optimization method is used to multi-AUV hunting posittion allocation, and efficient tracking and hunting for the escaping target are realized.

多AUV水下协作跟踪围捕是研究多个围捕者追踪捕获逃逸者的方法，深海环境的复杂性和围捕AUV自身的特殊性，使得多AUV水下跟踪围捕成为一个极具挑战性研究领域。本项目针对动态时变水下环境，应用多传感器信息融合技术感知环境信息，消除水下噪音干扰，以超短基线定位系统为硬件基础，采用“交互停等”水声传输模式实现AUV之间的信息通信和水声定位，准确获取AUV水下位置与姿态信息，生成水下环境地图；考虑围捕AUV的运动速度约束，提出自组织邻域航行围捕联盟生成与路径规划方法，应用邻域单元航行原则，自适应更新围捕联盟内AUV路径，保证AUV围捕过程中满足运动学约束、路径最短和自动避障；在此基础上考虑到深海海流与AUV模型不确定性，将生物激励神经动力学模型与变结构滑模控制相结合，提出生物激励滑模串级跟踪控制方法，采用量子协同优化策略完成多AUV围捕占位，实现对逃逸目标的高效跟踪围捕。

本项目针对动态时变水下环境，应用多传感器信息融合技术感知环境信息，消除水下噪音干扰，以超短基线定位系统为硬件基础，采用新型水声传输模式实现AUV之间的信息通信和水声定位，准确获取AUV水下位置与姿态信息，生成水下环境地图；考虑围捕AUV的运动速度约束，提出自组织邻域航行围捕联盟生成与路径规划方法，应用邻域单元航行原则，自适应更新围捕联盟内AUV路径，保证AUV围捕过程中满足运动学约束、路径最短和自动避障；在此基础上考虑到深海海流与AUV模型不确定性，将模型预测控制与变结构滑模控制相结合，提出模型预测滑模串级跟踪控制方法，采用量子协同优化策略完成多AUV围捕占位，实现对逃逸目标的高效跟踪围捕。