基于HS-RRV算法的空间机械臂在轨装配运动规划

Motion Planning of Space Manipulator for On-orbit Assembly Based on HS-RRV Algorithm

提出一种基于RRV(rapidly-exploring random vine)的混合采样算法(HS-RRV),以解决空间机械臂在拥挤杂乱环境下进行装配操作时运动规划效率低下的问题。首先,提出同时在工作空间和构形空间采样的策略,且采样权重随着算法进程动态调节,从而能够在保证算法完备性的前提下充分利用工作空间信息缩小采样范围以提高算法规划效率。其次,在局部规划器设计中,利用分层二次最小二乘规划方法,考虑了机械臂运动学和动力学特性与约束,提高了所规划轨迹的可执行性。当算法陷入局部复杂区域时,利用桥测试和主成分分析法对局部空间类型进行辨识,从而获取更加高效的拓展方向。最后,对所提方法与现有方法在多种装配场景下进行对比仿真,结果表明本方法有更高的运行效率和规划成功率,以及较短的轨迹长度。

A hybrid sampling-based RRV(rapidly-exploring random vine)algorithm named HS-RRV is proposed to solve the inefficient motion planning problems of the space manipulator for on-orbit assembly in crowded and cluttered environments.Firstly,a strategy for sampling in the workspace and configuration space simultaneously is proposed,where the sampling weight is dynamically adjusted according to the algorithm’s progress,and thus the workspace information is fully utilized to reduce the sampling regions in premise of the algorithm completeness,so as to improve the planning efficiency.Furthermore,the hierarchical quadratic least-square programming(HQLP)method is utilized in the local planner,where the kinematic and dynamic properties and constraints of the manipulator are considered to improve the viability of the planned trajectory.When the algorithm is trapped in complex regions,the methods of the bridge test and the principal component analysis(PCA)are used to identify the types of the local space so as to obtain more efficient expansion directions.Finally,the proposed method is compared with several known methods in multiple assembly scenarios through simulation,and the results show that the proposed method has higher computation efficiency and planning success rate,as well as a relatively shorter trajectory length.