Smart Pack: Online Autonomous Object-Packing System Using RGB-D Sensor Data

Hong, Young-Dae; Kim, Young-Joo; Lee, Ki-Baek

doi:10.3390/s20164448

Cited by 9 publications

(2 citation statements)

References 18 publications

(20 reference statements)

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“…Additionally, authors claim that their work was only practical with a small subset of objects. Hong et al went one step further and solved the irregular 3D object bin-packing in an online manner [17]. In that approach, a depth camera was used to estimate the area (highest contour) of the picked object.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic mosaic planning for a robotic bin-packing system based on picked part and target box monitoring

Iriondo

Lazkano

Ansuategi

et al. 2023

Int J Adv Manuf Technol

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This paper describes the dynamic mosaic planning method developed in the context of the PICKPLACE European project. The dynamic planner has allowed the development of a robotic system capable of packing a wide variety of objects without having to adjust to each reference. The mosaic planning system consists of three modules: First, the picked item monitoring module monitors the grabbed item to find out how the robot has picked it. At the same time, the destination container is monitored online to obtain the actual status of the packaging. To this end, we present a novel heuristic algorithm that, based on the point cloud of the scene, estimates the empty volume inside the container as empty maximal spaces (EMS). Finally, we present the development of the dynamic IK-PAL mosaic planner that allows us to dynamically estimate the optimal packing pose considering both the status of the picked part and the estimated EMSs. The developed method has been successfully integrated in a real robotic picking and packing system and validated with 7 tests of increasing complexity. In these tests, we demonstrate the flexibility of the presented system in handling a wide range of objects in a real dynamic packaging environment. To our knowledge, this is the first time that a complete online picking and packing system is deployed in a real robotic scenario allowing to create mosaics with arbitrary objects and to consider the dynamics of a real robotic packing system.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic mosaic planning for a robotic bin-packing system based on picked part and target box monitoring

Iriondo

Lazkano

Ansuategi

et al. 2023

Int J Adv Manuf Technol

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“…Similar strategies have also been adapted to Online BPP works like [17,20,46,50]. Different from the offline setting, the size information of coming items is unknown for the agent to optimize the packing and the packing order can not be adjusted as well.…”

Section: Related Workmentioning

confidence: 99%

Learning Practically Feasible Policies for Online 3D Bin Packing

Zhao¹,

Zhu²,

Xu³

et al. 2021

Preprint

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We tackle the Online 3D Bin Packing Problem, a challenging yet practically useful variant of the classical Bin Packing Problem. In this problem, the items are delivered to the agent without informing the full sequence information. Agent must directly pack these items into the target bin stably without changing their arrival order, and no further adjustment is permitted. Online 3D-BPP can be naturally formulated as Markov Decision Process (MDP). We adopt deep reinforcement learning, in particular, the on-policy actorcritic framework, to solve this MDP with constrained action space. To learn a practically feasible packing policy, we propose three critical designs. First, we propose an online analysis of packing stability based on a novel stacking tree. It attains a high analysis accuracy while reducing the computational complexity from O(N 2 ) to O(N log N ), making it especially suited for RL training. Second, we propose a decoupled packing policy learning for different dimensions of placement which enables high-resolution spatial discretization and hence high packing precision. Third, we introduce a reward function that dictates the robot to place items in a far-to-near order and therefore simplifies the collision avoidance in movement planning of the robotic arm. Furthermore, we provide a comprehensive discussion on several key implemental issues. The extensive evaluation demonstrates that our learned policy outperforms the state-of-the-art methods significantly and is practically usable for real-world applications.

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