Optimizing 3D Irregular Object Packing from 3D Scans Using Metaheuristics

Zhao, Yinghui; Rausch, Chris; Haas, Carl T.

doi:10.1016/j.aei.2020.101234

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…Nevertheless, irregular objects instead of cuboids are usually packed in realistic applications, and yielding optimal object sequence and placement for irregular objects according to the visual appearance has aroused extensive interest. Meta-heuristic methods including Tabu Search (TS) [16] and Guided local search (GLS) [17] begin with randomly placed objects and iteratively minimize the objective function by moving objects with collision prevention [4]. Constructive positioning heuristic methods containing Empty Maximal Space (EMS) [18], Maximum Contact Area (MCA) [19] and Heightmap Minimization (HM) [1] pack objects into empty container in the order determined by heuristic rules.…”

Section: A Packing Planningmentioning

confidence: 99%

“…Packing planning is an NP-hard combinatorial optimization problem with high complexity. In order to efficiently generate the optimal sequence and placement of objects, heuristic methods [1], [3], [4] with the greedy objective minimize the object stack heights in the packing boxes. Since the greedy search results in sub-optimal solution and high computational cost for object arrangement, data-driven methods [2], [5], [6] employ the reinforcement learning framework for bin packing.…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%

Planning Irregular Object Packing via Hierarchical Reinforcement Learning

Huang

Wang

Zhou

et al. 2023

IEEE Robot. Autom. Lett.

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“…When the shape of the items is irregular, it is not feasible to find an optimal solution to the problem as the search space is infinite, and only can be solved with approximate solutions. For instance, Zhao et al used a metaheuristic algorithm to estimate a nearoptimal packing mosaic for irregular objects [13]. In this work, the items were represented as meshes and the empty space as a bounding box of the bin.…”

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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“…The key to optimizing the BPP lies in generating the object packing order and determining the object placement strategy. To maximize the space utilization and minimize the usage number of boxes, conventional packing methods [1,4,5] take advantage of the meta-heuristics to generate the optimal object packing order, such as the genetic algorithm (GA) [6], generally resulting in highly computational cost and relatively low accuracy. Learning-based methods [7,8,9] employing DRL to resolve the BPP have gradually come up in academia, being advantageous in saving computational cost as well as increasing packing accuracy.…”

Section: Introductionmentioning

confidence: 99%

Learning to Solve 3-D Bin Packing Problem via Deep Reinforcement Learning and Constraint Programming

Jiang

Cao

Zhang

2023

IEEE Trans. Cybern.

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The Bin Packing Problem (BPP) has attracted enthusiastic research interest recently, owing to widespread applications in logistics and warehousing environments. It is truly essential to optimize the bin packing to enable more objects to be packed into boxes. Object packing order and placement strategy are the two crucial optimization objectives of the BPP. However, existing optimization methods for BPP, such as the genetic algorithm (GA), emerge as the main issues in highly computational cost and relatively low accuracy, making it difficult to implement in realistic scenarios. To well relieve the research gaps, we present a novel optimization methodology of two-dimensional (2D)-BPP and three-dimensional (3D)-BPP for objects with regular shapes via deep reinforcement learning (DRL), maximizing the space utilization and minimizing the usage number of boxes. First, an end-to-end DRL neural network constructed by a modified Pointer Network consisting of an encoder, a decoder and an attention module is proposed to achieve the optimal object packing order. Second, conforming to the top-down operation mode, the placement strategy based on a height map is used to arrange the ordered objects in the boxes, preventing the objects from colliding with boxes and other objects in boxes. Third, the reward and loss functions are defined as the indicators of the compactness, pyramid, and usage number of boxes to conduct the training of the DRL neural network based on an on-policy actor-critic framework.Finally, a series of experiments are implemented to compare our method with conventional packing methods, from which we conclude that our method outperforms these packing methods in both packing accuracy and efficiency.

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Optimizing 3D Irregular Object Packing from 3D Scans Using Metaheuristics

Cited by 20 publications

References 30 publications

Planning Irregular Object Packing via Hierarchical Reinforcement Learning

Planning Irregular Object Packing via Hierarchical Reinforcement Learning

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

Learning to Solve 3-D Bin Packing Problem via Deep Reinforcement Learning and Constraint Programming

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