In automated fiber placement (AFP), addressing the continuous motion planning challenge of redundant layup manipulators in complex environments, this paper proposes an offline redundancy optimization algorithm based on improved RRT* (Rapidly-exploring Random Trees). This algorithm maximizes the utilization of kinematic redundancy to derive smooth joint trajectories devoid of collisions and singularities. Firstly, the algorithm entails constructing a search map by eliminating joint configurations that violate constraints, and subsequently planning and optimizing the joint path by minimizing a multi-objective cost under the map constraint. Furthermore, several strategies are introduced to enhance RRT* for redundancy optimization. These strategies include a piecewise Gaussian sampling strategy (PGSS) to guide efficient tree growth within complex channels and enable joint sampling constrained by task coordinates. Additionally, the improved Steering and Local Optimization method are proposed to plan joint motion while considering intermediate task sequences. The effectiveness of the proposed algorithm is demonstrated in handling complex motion planning scenarios, such as layup involving complex path curves or dense obstacles. Experimental results validate the algorithm's capability to find feasible collision-free and singularity-free paths in relevant scenarios, provided such paths exist. Moreover, trajectory smoothness is optimized with increasing iterations.

中文翻译：

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基于改进的 RRT* 的冗余纤维铺放机械手的平滑关节运动规划


在自动纤维铺放（AFP）中，为了解决复杂环境下冗余敷层机械手的连续运动规划挑战，本文提出了一种基于改进的RRT*（快速探索随机树）的离线冗余优化算法。该算法最大限度地利用运动学冗余来导出没有碰撞和奇点的平滑关节轨迹。首先，该算法需要通过消除违反约束的关节配置来构建搜索地图，然后通过在地图约束下最小化多目标成本来规划和优化关节路径。此外，还引入了多种策略来增强 RRT* 以实现冗余优化。这些策略包括分段高斯采样策略（PGSS），用于指导复杂通道内的高效树生长，并实现受任务坐标约束的联合采样。此外，还提出了改进的转向和局部优化方法来规划关节运动，同时考虑中间任务序列。该算法的有效性在处理复杂的运动规划场景中得到了证明，例如涉及复杂路径曲线或密集障碍物的铺放。实验结果验证了算法在相关场景中找到可行的无碰撞和无奇点路径的能力，前提是此类路径存在。此外，轨迹平滑度随着迭代次数的增加而优化。