基于Octomap的仿人机器人局部环境与能力图模型算法

基于3D点云数据的机器人三维空间能力图模型算法存在体素网格搜索计算量大的问题,由于OcTree在三维空间细分时的层次化优势,提出一种基于Octomap的局部环境与能力图模型算法。首先,根据NAO机器人的关节组成、正向运动学、逆向运动学和刚体坐标变换,对NAO仿人机器人构建全身二叉树状运动学模型;其次在此基础上使用前向运动学在笛卡儿空间计算离散的三维可达点云,并将其作为机器人终端效应器的基础工作空间;然后重点描述将点云空间表示转化为Octomap空间节点表示的方法,尤其是空间节点的概率更新方法;最后提出根据节点几何关系进行空间节点更新顺序选择的优化方法,从而高效地实现了仿人机器人能力图的空间优化表示。实验结果表明,相对于之前的原始Octomap更新方法,优化后的算法能降低近30%空间节点数,提高计算效率。

Humanoid robot local environment and capability map model based on Octomap

The 3D capability map model of humanoid robot based on 3D point cloud data has the disadvantage of large voxel mesh searching computation. Considering the hierarchical advantage of OcTree in 3D space subdivision, a local environment and capability map model based on Octomap was proposed. Firstly, a binary-tree-like kinematics model of NAO humanoid robot was constructed according to the joint composition, forward kinematics, inverse kinematics and rigid body coordinate transformation of NAO robot. Secondly, the forward kinematics was used to calculate the 3D discrete reachable point clouds in Cartesian space, which were used as the basic workspace of the robot terminal effector. Thirdly, the methods of transforming the point cloud space representation into Octomap space node representation, especially the probability updating method of space node, were described emphatically. Finally, an optimization method of space node updating order selection was proposed according to the geometric relationship of nodes. With this optimization method, the space optimization representation of the humanoid robot's capability map was realized efficiently. Experimental results show that compared with the original Octomap updating method, the proposed algorithm can reduce the number of space nodes by nearly 30% and improve the computional efficiency.