A regrasp planning component for object reorientation

Wan, Weiwei; Igawa, Hisashi; Harada, Kensuke; Onda, Hiromu; Nagata, Kazuyuki; Yamanobe, Natsuki

doi:10.1007/s10514-018-9781-y

Cited by 29 publications

(16 citation statements)

References 47 publications

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“…This is likely to succeed when arrival poses uncertainty is small, or the items are symmetric. A better, complete solution would be to calculate a regrasping strategy to place the object in the desired orientation so that it can be packed successfully [20]. Another assumption is that the plan is executed perfectly.…”

Section: B Nondeterministic Problems Definition 3 (Ndop)mentioning

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: B Nondeterministic Problems Definition 3 (Ndop)mentioning

confidence: 99%

Robot Packing With Known Items and Nondeterministic Arrival Order

Wang

Hauser

2021

IEEE Trans. Automat. Sci. Eng.

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“…Object reorientation is the task of moving an object between different 3D orientations to make it accessible for further manipulation tasks such as assembly. Traditional methods use pick-and-place motions, where the manipulator grasps the object firmly and rotates it to the desired stable pose [15]. This process may repeat several times, depending on the robot's range of motion and workspace limitations, making it necessary to plan the motion sequencing resulting in a constraint satisfaction problem [16].…”

Section: Related Workmentioning

confidence: 99%

Grasping and Object Reorientation for Autonomous Construction of Stone Structures

Wermelinger

Johns

Gramazio

et al. 2021

IEEE Robot. Autom. Lett.

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Building large and stable structures from highly irregular stones is among the most challenging construction tasks with excavators. In this paper, we present a method for grasp planning and object manipulation that enables the world's first autonomous assembly of a large-scale stone wall with an unmanned hydraulic excavator system. Our method utilizes point clouds and mesh surface reconstruction of stones in order to plan grasp configurations with a 2-jaw gripper mounted on the excavator. Besides considering the geometry of the stone to sample force closure grasps, the grasp planner also takes into account collision constraints during object pick and place, informed by a LiDAR based point cloud map. Furthermore, we show an approach to reorient arbitrarily shaped objects that are not feasible to be directly placed at the desired location without violating collision constraints. Using a physics engine, we find a settled intermediate pose that allows direct placement and is reachable from the initial stone pose. The applicability of the proposed grasp planning method is demonstrated with the construction of a dry stone wall composed of over one hundred boulders using an autonomous excavator. We show a high primary grasp success rate (82.2 %) and illustrate how the system recovers from slippage by relocating the object and replanning the grasp correspondingly.

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“…The object's orientation for a horizontal placement can be obtained by analyzing the object's convex hull and extracting the faces that support a stable placement [7], [8]. Each of these faces gives rise to a base orientation when aligned with the support surface.…”

Section: A Placing Objectsmentioning

confidence: 99%

“…To determine the orientation in which the object should make contact with these regions, we extract contact points from the object's surface. For this, we follow a similar approach as in aforementioned previous works [7], [8] and select faces from the object's convex hull to place the object on. The convex hull of a finite set of points, i.e.…”

Section: A Defining Potential Contactsmentioning

confidence: 99%

Object Placement Planning and optimization for Robot Manipulators

Haustein

Hang

Stork

et al. 2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We address the problem of motion planning for a robotic manipulator with the task to place a grasped object in a cluttered environment. In this task, we need to locate a collisionfree pose for the object that a) facilitates the stable placement of the object, b) is reachable by the robot manipulator and c) optimizes a user-given placement objective. Because of the placement objective, this problem is more challenging than classical motion planning where the target pose is defined from the start. To solve this task, we propose an anytime algorithm that integrates sampling-based motion planning for the robot manipulator with a novel hierarchical search for suitable placement poses. We evaluate our approach on a dualarm robot for two different placement objectives, and observe its effectiveness even in challenging scenarios.

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A regrasp planning component for object reorientation

Cited by 29 publications

References 47 publications

Robot Packing With Known Items and Nondeterministic Arrival Order

Robot Packing With Known Items and Nondeterministic Arrival Order

Grasping and Object Reorientation for Autonomous Construction of Stone Structures

Object Placement Planning and optimization for Robot Manipulators

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