On-line three-dimensional packing problems: A review of off-line and on-line solution approaches

Ali, Sara; Ramos, António Galrão; Carravilla, Maria Antónia; Oliveira, José Fernando

doi:10.1016/j.cie.2022.108122

Cited by 42 publications

(19 citation statements)

References 168 publications

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“…Packing planning is a classic NP-hard combinatorial optimization problem [7]. Most research on packing planning focuses on packing 2D or 3D rectangle objects, and existing methods can be divided into two categories: heuristic [8]- [10] and data-driven approaches [11]- [13]. Heuristic methods generate the sequence and placement of objects by greedy objective regarding height, surface area and volume.…”

Section: A Packing Planningmentioning

confidence: 99%

“…El et al [15] modified squirrel search algorithm for improved best fit heuristic to solve large-scale instances within a reasonable time. To address the problem of sub-optimal solution and high computational cost in heuristic methods, data-driven methods [11] explored the large search space by efficiently interacting with the environment. Zhang et al [12] encoded the input with self-attention and decoded the sequence and placement with reinforcement learning for bin packing problem.…”

Section: A Packing Planningmentioning

confidence: 99%

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Planning Irregular Object Packing via Hierarchical Reinforcement Learning

Huang

Wang

Zhou

et al. 2023

IEEE Robot. Autom. Lett.

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Object packing by autonomous robots is an important challenge in warehouses and logistics industry. Most conventional data-driven packing planning approaches focus on regular cuboid packing, which are usually heuristic and limit the practical use in realistic applications with everyday objects. In this paper, we propose a deep hierarchical reinforcement learning approach to simultaneously plan packing sequence and placement for irregular object packing. Specifically, the top manager network infers packing sequence from six principal view heightmaps of all objects, and then the bottom worker network receives heightmaps of the next object to predict the placement position and orientation. The two networks are trained hierarchically in a self-supervised Q-Learning framework, where the rewards are provided by the packing results based on the top height , object volume and placement stability in the box. The framework repeats sequence and placement planning iteratively until all objects have been packed into the box or no space is remained for unpacked items. We compare our approach with existing robotic packing methods for irregular objects in a physics simulator. Experiments show that our approach can pack more objects with less time cost than the state-of-the-art packing methods of irregular objects. We also implement our packing plan with a robotic manipulator to show the generalization ability in the real world.

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Section: A Packing Planningmentioning

confidence: 99%

Section: A Packing Planningmentioning

confidence: 99%

Planning Irregular Object Packing via Hierarchical Reinforcement Learning

Huang

Wang

Zhou

et al. 2023

IEEE Robot. Autom. Lett.

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“…The use of metaheuristic-based algorithms led to good approximate solutions for the orthogonal 3D binpacking problem [8]. In [9], Crainic et al efficiently solved the orthogonal 3D bin-packing problem applying a two-level tabu-search method.…”

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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“…It has a high frame rate for tracking the high-speed object because high-speed visual feedback provides accurate automated pantilt control. The goal of the tracking unit is to make the centroid of the object image coincide with the center of the captured image 22 . These two units are coaxially constructed by a beam splitter to share the shooting screen, as shown in Figure 1.…”

Section: Structure and Mechanismmentioning

confidence: 99%