基于Octomap的仿人机器人局部环境与能力图模型算法
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摘要
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。
The 3 D capability map model of humanoid robot based on 3 D point cloud data has the disadvantage of large voxel mesh searching computation. Considering the hierarchical advantage of OcTree in 3 D 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 3 D 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.
The 3 D capability map model of humanoid robot based on 3 D point cloud data has the disadvantage of large voxel mesh searching computation. Considering the hierarchical advantage of OcTree in 3 D 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 3 D 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.
引文
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[8]温淑慧,王同辉,薛红香,等. NAO机器人的手臂建模及模糊控制算法研究[J].控制工程, 2018, 25(4):559-564.(WEN S H, WANG T H, XUE H X, et al. Study of arm dynamics modeling and fuzzy control of humanoid robots[J]. Control Engineering of China, 2018, 25(4):559-564.)
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[11] HORNUNG A, WURM K M, BENNEWITZ M, et al. OctoM ap:an efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous Robots, 2013, 34(3):189-206.
[12] LEIDNER D, DIETRICH A, SCHMIDT F, et al. Object-centered hybrid reasoning for whole-body mobile manipulation[C]//Proceedings of the 2014 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2014:1828-1835.
[13]王秋玥,方明,张海涛.基于多空间混合约束的NAO机器人抓取轨迹规划[J].长春理工大学学报(自然科学版), 2014,37(6):107-110.(WANG Q Y, FANG M, ZHANG H T. Grasp track planning with the NAO robot under mixed constraints of multispace[J]. Journal of Changchun University of Science and Technology(Natural Science Edition), 2014, 37(6):107-110.)
[2] TAN M, WANG S. Research progress on robotics[J]. Acta Automatica Sinica, 2013, 39(7):963-972.
[3]梶田秀司.仿人机器人[M].北京:清华大学出版社, 2007:44-50.(SHUUJI K. Humanoid Robots[M]. Beijing:Tsinghua University Press, 2007:44-50.)
[4] KOFINAS N, ORFANOUDAKIS E, LAGOUDAKIS M G. Complete analytical forward and inverse kinematics for the NAO humanoid robot[J]. Journal of Intelligent&Robotic Systems, 2015, 77(2):251-264.
[5] ZACHARIAS F, BORST C, HIRZINGER G. Capturing robot workspace structure:representing robot capabilities[C]//Proceedings of the 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ:IEEE, 2007:3229-3236.
[6] MELLMANN H, COTUGNO G. Dynamic Motion Control:Adaptive Bimanual Grasping for a Humanoid Robot[M]. Amsterdam:IOS Press, 2011:89-101.
[7] BURGET F, BENNEWITZ M. Stance selection for humanoid grasping tasks by inverse reachability maps[C]//Proceedings of the 2015IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2015:5669-5674.
[8]温淑慧,王同辉,薛红香,等. NAO机器人的手臂建模及模糊控制算法研究[J].控制工程, 2018, 25(4):559-564.(WEN S H, WANG T H, XUE H X, et al. Study of arm dynamics modeling and fuzzy control of humanoid robots[J]. Control Engineering of China, 2018, 25(4):559-564.)
[9] SAKAGAMI Y, WATANABE R, AOYAMA C, et al. The intelligent ASIMO:system overview and integration[C]//Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems. Piscataway, NJ:IEEE, 2002:2478-2483.
[10] MELLMANN H, SCHEUNEMANN M, STADIE O. Adaptive grasping for a small humanoid robot utilizing force and electric current sensors[EB/OL].[2018-05-10]. https://www2. informatik.hu-berlin. de/~mellmann/content/publications/data/CSP-Mellma nn Scheunemann EtA l-13. pdf.
[11] HORNUNG A, WURM K M, BENNEWITZ M, et al. OctoM ap:an efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous Robots, 2013, 34(3):189-206.
[12] LEIDNER D, DIETRICH A, SCHMIDT F, et al. Object-centered hybrid reasoning for whole-body mobile manipulation[C]//Proceedings of the 2014 IEEE International Conference on Robotics and Automation. Piscataway, NJ:IEEE, 2014:1828-1835.
[13]王秋玥,方明,张海涛.基于多空间混合约束的NAO机器人抓取轨迹规划[J].长春理工大学学报(自然科学版), 2014,37(6):107-110.(WANG Q Y, FANG M, ZHANG H T. Grasp track planning with the NAO robot under mixed constraints of multispace[J]. Journal of Changchun University of Science and Technology(Natural Science Edition), 2014, 37(6):107-110.)