Stable Bin Packing of Non-convex 3D Objects with a Robot Manipulator

被引:0
|
作者
Wang, Fan [1 ]
Hauser, Kris [1 ]
机构
[1] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA
来源
2019 INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA) | 2019年
关键词
SEARCH; HEURISTICS; ALGORITHM;
D O I
10.1109/icra.2019.8794049
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Recent progress in the field of robotic manipulation has generated interest in fully automatic object packing in warehouses. This paper proposes a formulation of the packing problem that is tailored to the automated warehousing domain. Besides minimizing waste space inside a container, the problem requires stability of the object pile during packing and the feasibility of the robot motion executing the placement plans. To address this problem, a set of constraints are formulated, and a constructive packing pipeline is proposed to solve these constraints. The pipeline is able to pack geometrically complex, non-convex objects while satisfying stability and robot packability constraints. In particular, a new 3D positioning heuristic called Heightmap-Minimization heuristic is proposed, and heightmaps are used to speed up the search. Experimental evaluation of the method is conducted with a realistic physical simulator on a dataset of scanned real-world items, demonstrating stable and high-quality packing plans compared with other 3D packing methods.
引用
收藏
页码:8698 / 8704
页数:7
相关论文
共 50 条
  • [21] Maximum packing densities of basic 3D objects
    LI ShuiXiangZHAO JianLU Peng XIE Yu State Key Laboratory for Turbulence and Complex SystemsCollege of EngineeringPeking UniversityBeijing China
    Chinese Science Bulletin, 2010, 55 (02) : 114 - 119
  • [22] Maximum packing densities of basic 3D objects
    LI ShuiXiang
    Science Bulletin, 2010, (02) : 114 - 119
  • [23] Online 3D Bin Packing Reinforcement Learning Solution with Buffer
    Puche, Aaron Valero
    Lee, Sukhan
    2022 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2022, : 8902 - 8909
  • [24] Online 3D Bin Packing with Constrained Deep Reinforcement Learning
    Zhao, Hang
    She, Qijin
    Zhu, Chenyang
    Yang, Yin
    Xu, Kai
    THIRTY-FIFTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THIRTY-THIRD CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE AND THE ELEVENTH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2021, 35 : 741 - 749
  • [25] Learning practically feasible policies for online 3D bin packing
    Hang ZHAO
    Chenyang ZHU
    Xin XU
    Hui HUANG
    Kai XU
    ScienceChina(InformationSciences), 2022, 65 (01) : 175 - 191
  • [26] Learning practically feasible policies for online 3D bin packing
    Zhao, Hang
    Zhu, Chenyang
    Xu, Xin
    Huang, Hui
    Xu, Kai
    SCIENCE CHINA-INFORMATION SCIENCES, 2022, 65 (01)
  • [27] Learning practically feasible policies for online 3D bin packing
    Hang Zhao
    Chenyang Zhu
    Xin Xu
    Hui Huang
    Kai Xu
    Science China Information Sciences, 2022, 65
  • [28] Effectiveness of inside/outside determination in relation to 3D non-convex shapes using CUDA
    Kodama, Satoshi
    IMAGING SCIENCE JOURNAL, 2018, 66 (07): : 409 - 418
  • [29] Shape tracing: An extension of sphere tracing for 3D non-convex collision in protein docking
    Leach, Adam
    Rudden, Lucas S. P.
    Bond-Taylor, Sam
    Brigham, John C.
    Degiacomi, Matteo T.
    Willcocks, Chris G.
    2020 IEEE 20TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING (BIBE 2020), 2020, : 49 - 52
  • [30] 3D surface-tracking with a robot manipulator
    Araujo, R
    Nunes, U
    deAlmeida, AT
    JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS, 1996, 15 (04) : 401 - 417