基于仿射变换的多UUV自适应队形变换路径规划方法

A path planning method for multi-UUV adaptive formation reshaping based on affine transformation

  • 摘要: 【目的】针对多水下无人航行器(UUV)编队在复杂水下环境中面临的编队保持与灵活避障难以同时实现的难题,提出一种能够自适应队形变换的全局路径规划方法。【方法】所提方法基于仿射变换框架,将多UUV系统的协同路径规划问题映射至二维平面仿射参数空间进行求解。首先,采用一种改进的快速随机搜索树*(RRT*)算法进行前端路径搜索,该算法通过整合快速探索与迭代优化阶段、加权KD(k-dimensional)树、混合采样机制及自适应采样参数调整,实现了初始仿射状态序列的高效生成。随后,利用基于B样条曲线的后端优化器,采用梯度下降法最小化一个综合考虑轨迹平滑性、UUV运动学可行性、环境碰撞安全性及自适应编队缩放代价的目标函数,优化得到连续光滑且满足多重约束的仿射参数轨迹。【结果】湖上试验结果表明,所提规划方法能够生成安全、可行的编队路径,成功引导多UUV编队穿越了模拟的狭窄障碍区域,且UUV的实际速度和加速度均满足设定的可行性约束。【结论】所提出的基于仿射变换的全局规划方法能够通过自适应队形变换为航行于复杂障碍场景下的多UUV编队提供安全可行的编队路径,实现多UUV编队的安全航行。同时,该方法能够显著提升海洋无人航行器的自主性与环境适应性,对推动海洋无人装备技术的发展和实际应用具有积极意义。

     

    Abstract: Objective To address the challenge of simultaneously achieving formation keeping and flexible obstacle avoidance for multi-Unmanned Underwater Vehicle (multi-UUV) formations in complex underwater environments, this paper proposes a global path planning method that enables adaptive formation transformation.Method The proposed method is grounded in an affine transformation framework, which maps the cooperative path planning problem of the multi-UUV system into a two-dimensional affine parameter space for resolution. First, a front-end path search is conducted using an improved Rapidly-exploring Random Tree* (RRT*) algorithm. By integrating fast exploration and iterative optimization phases, a weighted k-dimensional (KD) tree, a hybrid sampling mechanism, and adaptive adjustment of sampling parameters, this algorithm efficiently generates an initial sequence of affine states. Subsequently, a B-spline-based back-end optimizer employs the gradient descent method to minimize a comprehensive objective function. This function considers trajectory smoothness, UUV kinematic feasibility, environmental collision safety, and the cost of adaptive formation scaling. The optimization yields a continuous and smooth trajectory of affine parameters that satisfies multiple constraints.Results Results from lake experiments demonstrate that the proposed planning method can generate safe and feasible formation paths. It successfully guided the multi-UUV formation through a simulated narrow obstacle area, and the actual velocities and accelerations of the UUVs remained within the predefined feasibility constraints.Conclusion The proposed global planning method, based on affine transformation, effectively provides safe and feasible paths for multi-UUV formations navigating through complex obstacle scenarios by enabling adaptive formation changes. This method significantly enhances the autonomy and environmental adaptability of marine unmanned vehicles, holding positive significance for advancing the development and practical application of marine unmanned systems technology.

     

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