Integrated grasp and motion planning using independent contact regions

Fontanals, Joan; Dang-Vu, Bao-Anh; Porges, Oliver; Rosell, Jan; Roa, Máximo A.

doi:10.1109/humanoids.2014.7041469

Cited by 29 publications

(19 citation statements)

References 27 publications

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“…Ensuring that a collision-free approach motion to a placement exists (challenge 2) requires us to closely integrate the placement search with a motion planning algorithm. This relates our problem to integrated grasp and motion planning [12]- [16]. These works present algorithms that simultaneously compute grasps with corresponding approach motions, and demonstrate that in cluttered environments separate planning of grasps and approach motions is inefficient.…”

Section: B Integrated Grasp and Motion Planningmentioning

confidence: 92%

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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Section: B Integrated Grasp and Motion Planningmentioning

confidence: 92%

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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“…A different set of methods use the fitting of object models to the point clouds to generate smaller or simpler sets of grasp points. In [26], the calculation of grasp regions is combined with path planning. Curvature-based grasping using antipodal points for differentiable curves, in both convex and concave segments, was studied in [27], while in [28], a grasping energy function is used to calculate antipodal grasping points using local modelling of the surface.…”

Section: Related Workmentioning

confidence: 99%

Grasping Unknown Objects in Clutter by Superquadric Representation

Makhal

Thomas

Pérez-Gracia

2018

2018 Second IEEE International Conference on Robotic Computing (IRC)

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In this paper, a quick and efficient method is presented for grasping unknown objects in clutter. The grasping method relies on real-time superquadric (SQ) representation of partial view objects and incomplete object modelling, well suited for unknown symmetric objects in cluttered scenarios which is followed by optimized antipodal grasping. The incomplete object models are processed through a mirroring algorithm that assumes symmetry to first create an approximate complete model and then fit for SQ representation.The grasping algorithm is designed for maximum force balance and stability, taking advantage of the quick retrieval of dimension and surface curvature information from the SQ parameters. The pose of the SQs with respect to the direction of gravity is calculated and used together with the parameters of the SQs and specification of the gripper, to select the best direction of approach and contact points. The SQ fitting method has been tested on custom datasets containing objects in isolation as well as in clutter. The grasping algorithm is evaluated on a PR2 and real time results are presented. Initial results indicate that though the method is based on simplistic shape information, it outperforms other learning based grasping algorithms that also work in clutter in terms of time-efficiency and accuracy.

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“…Some approaches deal with integrated grasp and motion planning [25] [26]. This work focuses on motion planning aspects and uses the output of grasping planners [27][28] [29], i.e., grasps to set the goals of the corresponding picking challenges, which are frequently defined as end effector poses relative to a target object.…”

Section: Background and Relative Contributionmentioning

confidence: 99%

Fast, Anytime Motion Planning for Prehensile Manipulation in Clutter

Kimmel

Shome

Littlefield

et al. 2018

2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids)

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Many methods have been developed for planning the motion of robotic arms for picking and placing, ranging from local optimization to global search techniques, which are effective for sparsely placed objects. Dense clutter, however, still adversely affects the success rate, computation times, and quality of solutions in many real-world setups. The current work integrates tools from existing methodologies and proposes a framework that achieves high success ratio in clutter with anytime performance. The idea is to first explore the lower dimensional end effector's task space efficiently by ignoring the arm, and build a discrete approximation of a navigation function, which guides the end effector towards the set of available grasps or object placements. This is performed online, without prior knowledge of the scene. Then, an informed sampling-based planner for the entire arm uses Jacobianbased steering to reach promising end effector poses given the task space guidance. While informed, the method is also comprehensive and allows the exploration of alternative paths over time if the task space guidance does not lead to a solution. This paper evaluates the proposed method against alternatives in picking or placing tasks among varying amounts of clutter for a variety of robotic manipulators with different end-effectors. The results suggest that the method reliably provides higher quality solution paths quicker, with a higher success rate relative to alternatives.

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Integrated grasp and motion planning using independent contact regions

Cited by 29 publications

References 27 publications

Object Placement Planning and optimization for Robot Manipulators

Object Placement Planning and optimization for Robot Manipulators

Grasping Unknown Objects in Clutter by Superquadric Representation

Fast, Anytime Motion Planning for Prehensile Manipulation in Clutter

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