Assembly sequence planning for motion planning

Wan, Weiwei; Harada, Kensuke; Nagata, Kazuyuki

doi:10.1108/aa-01-2017-009

Cited by 47 publications

(22 citation statements)

References 44 publications

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“…Wang and Hauser [15] formulate pile stability and manipulation feasibility constraints in a packing planner that can handle arbitrary, nonconvex objects. Similar types of constraints have been considered in research that has extended classical assembly planning approaches to physical robot manipulators [16].…”

Section: Related Workmentioning

confidence: 99%

Robot Packing With Known Items and Nondeterministic Arrival Order

Wang

Hauser

2021

IEEE Trans. Automat. Sci. Eng.

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This article formulates two variants of packing problems in which the set of items is known, but the arrival order is unknown. The goal is to certify that the items can be packed in a given container and/or to optimize the size or cost of a container so that that the items are guaranteed to be packable, regardless of arrival order. The nondeterministically ordered packing (NDOP) variant asks to generate a certificate that a packing plan exists for every ordering of items. Quasionline packing (QOP) asks to generate a partially observable packing policy that chooses the location of each item as their arrival order is revealed one-by-one, with all of the remaining items certified to be packable regardless of their future arrival order. Theoretical analysis demonstrates that even the simple subproblem of verifying the feasibility of a packing policy is NPcomplete. Despite this worst case complexity, practical solvers for both NDOP and QOP are developed. Multiple extensions to the basic nondeterministic problem are presented, including packing with a fixed-capacity buffer and packing with equivalent objects. Experiments demonstrate that these algorithms can be applied to packing irregular 3-D shapes with stability and manipulator loading constraints. Note to Practitioners-Automatic packing of objects into containers, such as a shipping box, has applications in warehouse automation for e-commerce applications and less-than-truckload shipping. In many scenarios, a container needs to be preselected for a set of objects before they arrive, and the order of the objects cannot be controlled. This article studies the theoretical foundations of packing problems, in which the set of items is known, but the arrival order is unknown. The algorithms presented in this article can certify whether a container can hold a set of objects in the case where the arrival order is revealed at once, one-by-one, and where a limited set of objects can be put aside in a buffer before packing. These algorithms can be used to choose optimal containers and packing strategies, both for robot and human packers.

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Section: Related Workmentioning

confidence: 99%

Robot Packing With Known Items and Nondeterministic Arrival Order

Wang

Hauser

2021

IEEE Trans. Automat. Sci. Eng.

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“…Previously, many projects were developed using grasping poses planned by the algorithms in this article. These projects use the planned grasps to iteratively select start and goal poses in the robot workspace [43]- [45], build manipulation graphs [46]- [50], or optimize object assembly sequences [51]- [53]. Although the proposed algorithms were heavily used in these projects, they were not carefully introduced or analyzed.…”

Section: Relations To Our Previous Studiesmentioning

confidence: 99%

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

2021

Self Cite

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This article develops model-based grasp planning algorithms. It focuses on industrial end-effectors like grippers and suction cups, and plans grasp configurations considering computer aided design (CAD) models of target objects. The developed algorithms can stably find many high-quality grasps, with satisfying precision and little dependency on the quality of CAD models. The undergoing core technique is superimposed segmentation, which preprocesses a mesh model by peeling it into superimposed facets. The algorithms use the facets to locate contacts and synthesize grasp poses for popular industrial end-effectors. Several tunable parameters are prepared to adapt the algorithms to meet various requirements. The experimental section studies the influence of the tunable parameters and analyzes the cost, precision, and robustness of the proposed algorithms and their planned grasps, with both simulations and real-world systems. Besides, the proposed algorithms are applicable to mesh models reconstructed from point clouds obtained by depth sensors. Some experiments and analysis are also carried out to study and demonstrate the ability. Index Terms-Grasping, grippers and other end-effectors, manipulation planning. I. INTRODUCTIONT HIS article develops algorithms to plan grasping configurations automatically. It focuses on industrial end-effectors, and plans grasp poses for these end-effectors considering computer aided design (CAD) models of target objects.Developing grasp planning algorithms is vital to manufacturing using "teachingless" robotic manipulators. Modern robotic manipulation systems use manually annotated or pretaught grasp configurations to perform specific tasks, which is not only costly Manuscript

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“…Moving the partial assembly between steps is allowed for improving these measures. Wan et al [15] optimize sequences based on a score for the ease of 3D part insertion derived from contact normals (ignoring potential disturbances that can occur along the motion). Anders et al [16] also learn from simulation to plan pushing motions for planar objects.…”

Section: B Robustnessmentioning

confidence: 99%

Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores

Geft

Tamar

Goldberg

et al. 2019

2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)

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To compute robust 2D assembly plans, we present an approach that combines geometric planning with a deep neural network. We train the network using the Box2D physics simulator with added stochastic noise to yield robustness scores-the success probabilities of planned assembly motions. As running a simulation for every assembly motion is impractical, we train a convolutional neural network to map assembly operations, given as an image pair of the subassemblies before and after they are mated, to a robustness score. The neural network prediction is used within a planner to quickly prune out motions that are not robust. We demonstrate this approach on two-handed planar assemblies, where the motions are onestep translations. Results suggest that the neural network can learn robustness to plan robust sequences an order of magnitude faster than physics simulation.

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Assembly sequence planning for motion planning

Cited by 47 publications

References 44 publications

Robot Packing With Known Items and Nondeterministic Arrival Order

Robot Packing With Known Items and Nondeterministic Arrival Order

Planning Grasps With Suction Cups and Parallel Grippers Using Superimposed Segmentation of Object Meshes

Robust 2D Assembly Sequencing via Geometric Planning with Learned Scores

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