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Journal ArticleDOI

Manipulation Planning with Probabilistic Roadmaps

TL;DR: A general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches.
Abstract: This paper deals with motion planning for robots manipulating movable objects among obstacles. We propose a general manipulation planning approach capable of addressing continuous sets for modeling both the possible grasps and the stable placements of the movable object, rather than discrete sets generally assumed by the previous approaches. The proposed algorithm relies on a topological property that characterizes the existence of solutions in the subspace of configurations where the robot grasps the object placed at a stable position. It allows us to devise a manipulation planner that captures in a probabilistic roadmap the connectivity of sub-dimensional manifolds of the composite configuration space. Experiments conducted with the planner in simulated environments demonstrate its efficacy to solve complex manipulation problems.

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HAL Id: hal-01987879
https://hal.laas.fr/hal-01987879
Submitted on 21 Jan 2019
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entic research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diusion de documents
scientiques de niveau recherche, publiés ou non,
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publics ou privés.
Manipulation Planning with Probabilistic Roadmaps
Thierry Simeon, Jean-Paul Laumond, Juan Cortés, Anis Sahbani
To cite this version:
Thierry Simeon, Jean-Paul Laumond, Juan Cortés, Anis Sahbani. Manipulation Planning with Prob-
abilistic Roadmaps. The International Journal of Robotics Research, SAGE Publications, 2004, 23
(7-8), pp.729-746. �hal-01987879�

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1

Citations
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Journal ArticleDOI
TL;DR: This work proposes a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects and describes the main features of an implemented planner.
Abstract: We propose a representation and a planning algorithm able to deal with problems integrating task planning as well as motion and manipulation planning knowledge involving several robots and objects. Robot plans often include actions where the robot has to place itself in some position in order to perform some other action or to "modify" the configuration of its environment by displacing objects. Our approach aims at establishing a bridge between task planning and manipulation planning that allows a rigorous treatment of geometric preconditions and effects of robot actions in realistic environments. We show how links can be established between a symbolic description and its geometric counterpart and how they can be used in an integrated planning process that is able to deal with intricate symbolic and geometric constraints. Finally, we describe the main features of an implemented planner and discuss several examples of its use.

276 citations


Cites background or methods from "Manipulation Planning with Probabil..."

  • ...On the other hand, practical motion planning techniques (LaValle 2006), and more precisely manipulation planners (Alami et al. 1990 Siméon et al. 2003), have experienced very important progress....

    [...]

  • ...We follow and extend the definitions presented by Siméon et al. (2003) to the case of several robots and several objects....

    [...]

  • ...A demonstration was given by Siméon et al. (2003)....

    [...]

  • ...To do so, we rely on the definitions used by Ghallab et al. (2004) and on the configuration space definitions (Lozano-Pérez 1983) that have been extended by Alami et al. (1990) and Siméon et al. (2003) to take manipulation problems into account....

    [...]

  • ...The reader can refer to Alami et al. (1995) and Siméon et al. (2003) for a detailed discussion on manipulation problems in the presence of continuous grasps and placements....

    [...]

Proceedings ArticleDOI
18 Apr 2005
TL;DR: This paper develops and implements a simple new planner which shows significant improvement over existing RRT-based planners and proposes a general framework for minimizing their effect.
Abstract: Sampling-based planners have solved difficult problems in many applications of motion planning in recent years. In particular, techniques based on the Rapidly-exploring Random Trees (RRTs) have generated highly successful single-query planners. Even though RRTs work well on many problems, they have weaknesses which cause them to explore slowly when the sampling domain is not well adapted to the problem. In this paper we characterize these issues and propose a general framework for minimizing their effect. We develop and implement a simple new planner which shows significant improvement over existing RRT-based planners. In the worst cases, the performance appears to be only slightly worse in comparison to the original RRT, and for many problems it performs orders of magnitude better.

264 citations


Cites background from "Manipulation Planning with Probabil..."

  • ...Extensions of RRTs for manipulation problems and motions of closed articulated chains are developed in [8], [17], [18], [ 33 ]....

    [...]

Proceedings ArticleDOI
27 Jun 2011
TL;DR: This work introduces a framework for planning in clutter that uses a library of actions inspired by human strategies that succeeds where traditional grasp planners fail, and works under high uncertainty by utilizing the funneling effect of pushing.
Abstract: Humans use a remarkable set of strategies to manipulate objects in clutter. We pick up, push, slide, and sweep with our hands and arms to rearrange clutter surrounding our primary task. But our robots treat the world like the Tower of Hanoi — moving with pick-and-place actions and fearful to interact with it with anything but rigid grasps. This produces inefficient plans and is often inapplicable with heavy, large, or otherwise ungraspable objects. We introduce a framework for planning in clutter that uses a library of actions inspired by human strategies. The action library is derived analytically from the mechanics of pushing and is provably conservative. The framework reduces the problem to one of combinatorial search, and demonstrates planning times on the order of seconds. With the extra functionality, our planner succeeds where traditional grasp planners fail, and works under high uncertainty by utilizing the funneling effect of pushing. We demonstrate our results with experiments in simulation and on HERB, a robotic platform developed at the Personal Robotics Lab at Carnegie Mellon University.

247 citations


Cites background from "Manipulation Planning with Probabil..."

  • ...We overload Volume to accept trajectories of regions too; e.g. Volume(o, ν) gives the volume of space swept by the uncertainty of the object during its manipulation....

    [...]

Proceedings Article
25 Jul 2015
TL;DR: This work proposes to formulate the problem of sequential robot manipulation holistically as a 1st- order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints.
Abstract: We consider problems of sequential robot manipulation (aka. combined task and motion planning) where the objective is primarily given in terms of a cost function over the final geometric state, rather than a symbolic goal description. In this case we should leverage optimization methods to inform search over potential action sequences. We propose to formulate the problem holistically as a 1st- order logic extension of a mathematical program: a non-linear constrained program over the full world trajectory where the symbolic state-action sequence defines the (in-)equality constraints. We tackle the challenge of solving such programs by proposing three levels of approximation: The coarsest level introduces the concept of the effective end state kinematics, parametrically describing all possible end state configurations conditional to a given symbolic action sequence. Optimization on this level is fast and can inform symbolic search. The other two levels optimize over interaction keyframes and eventually over the full world trajectory across interactions. We demonstrate the approach on a problem of maximizing the height of a physically stable construction from an assortment of boards, cylinders and blocks.

238 citations

Journal ArticleDOI
TL;DR: A global overview of deliberation functions in robotics is presented and the main characteristics, design choices and constraints of these functions are discussed.

229 citations


Cites background from "Manipulation Planning with Probabil..."

  • ...Manipulation planning requires finding feasible sequences of grasping positions, each of which is a partial constraint on the robot configuration that changes its kinematics [198]....

    [...]

References
More filters
Book
01 Jan 1990
TL;DR: This chapter discusses the configuration space of a Rigid Object, the challenges of dealing with uncertainty, and potential field methods for solving these problems.
Abstract: 1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.

6,186 citations


"Manipulation Planning with Probabil..." refers methods in this paper

  • ...Manipulation Planning with Probabilistic Roadmaps Thierry Simeon, Jean-Paul Laumond, Juan Cortés, Anis Sahbani...

    [...]

  • ...HAL Id: hal-01987879 https://hal.laas.fr/hal-01987879...

    [...]

Proceedings ArticleDOI
24 Apr 2000
TL;DR: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces by incrementally building two rapidly-exploring random trees rooted at the start and the goal configurations.
Abstract: A simple and efficient randomized algorithm is presented for solving single-query path planning problems in high-dimensional configuration spaces. The method works by incrementally building two rapidly-exploring random trees (RRTs) rooted at the start and the goal configurations. The trees each explore space around them and also advance towards each other through, the use of a simple greedy heuristic. Although originally designed to plan motions for a human arm (modeled as a 7-DOF kinematic chain) for the automatic graphic animation of collision-free grasping and manipulation tasks, the algorithm has been successfully applied to a variety of path planning problems. Computed examples include generating collision-free motions for rigid objects in 2D and 3D, and collision-free manipulation motions for a 6-DOF PUMA arm in a 3D workspace. Some basic theoretical analysis is also presented.

3,102 citations

Journal ArticleDOI
TL;DR: A new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space is proposed and a planner based on this approach has been implemented.
Abstract: We propose a new approach to robot path planning that consists of building and searching a graph connecting the local minima of a potential function defined over the robot's configuration space. A planner based on this approach has been implemented. This planner is consider ably faster than previous path planners and solves prob lems for robots with many more degrees of freedom (DOFs). The power of the planner derives both from the "good" properties of the potential function and from the efficiency of the techniques used to escape the local min ima of this function. The most powerful of these tech niques is a Monte Carlo technique that escapes local min ima by executing Brownian motions. The overall approach is made possible by the systematic use of distributed rep resentations (bitmaps) for the robot's work space and configuration space. We have experimented with the plan ner using several computer-simulated robots, including rigid objects with 3 DOFs (in 2D work space) and 6 DOFs (in 3D work space) and ...

1,097 citations


"Manipulation Planning with Probabil..." refers methods in this paper

  • ...Manipulation Planning with Probabilistic Roadmaps Thierry Simeon, Jean-Paul Laumond, Juan Cortés, Anis Sahbani...

    [...]

Journal ArticleDOI
TL;DR: A planner for finding stable pushing paths among obstacles is described, and the planner is demon strated on several manipulation tasks.
Abstract: We would like to give robots the ability to position and orient parts in the plane by pushing, particularly when the parts are too large or heavy to be grasped and lifted. Unfortunately, the motion of a pushed object is generally unpredictable due to unknown support friction forces. With multiple pushing contact points, however, it is possible to find pushing directions that cause the object to remain fixed to the manipulator. These are called stable pushing directions. In this article we consider the problem of planning pushing paths using stable pushes. Pushing imposes a set of nonholonomic velocity constraints on the motion of the object, and we study the issues of local and global controllability during pushing with point contact or stable line contact. We describe a planner for finding stable pushing paths among obstacles, and the planner is demon strated on several manipulation tasks.

513 citations


"Manipulation Planning with Probabil..." refers background in this paper

  • ...In Lynch and Mason (1994), grasping is replaced by pushing and the space of stable pushing directions imposes...

    [...]

Proceedings ArticleDOI
08 May 1994
TL;DR: This paper presents a new approach to path planning for robots with many degrees of freedom (DOF) operating in known static environments that is particularly attractive for many-DOF robots which have to perform many successive point-to-point motions in the same environment.
Abstract: This paper presents a new approach to path planning for robots with many degrees of freedom (DOF) operating in known static environments. The approach consists of a preprocessing and a planning stage. Preprocessing, which is done only once for a given environment, generates a network of randomly, but properly selected, collision-free configurations (nodes). Planning then connects any given initial and final configurations of the robot to two nodes of the network and computes a path through the network between these two nodes. Experiments show that after paying the preprocessing cost (on the order of hundreds of seconds), planning is extremely fast (on the order of a fraction of a second for many difficult examples involving a 10-DOF robot). The approach is particularly attractive for many-DOF robots which have to perform many successive point-to-point motions in the same environment. >

441 citations


"Manipulation Planning with Probabil..." refers background or methods in this paper

  • ...In particular, applying a purely randomized PRM framework ( Kavraki and Latombe 1994; Overmars and Svestka 1994) to closed-chain mechanisms is prohibited by the fact that the probability to choose a configuration at random on a given sub-dimensional manifold is null (LaValle, Yakey, and Kavraki 1999)....

    [...]

  • ...Finally, Nielsen and Kavraki (2000) propose a practical manipulation planner based on the extension of the PRM framework ( Kavraki and Latombe 1994; Overmars and Svestka 1994)....

    [...]

  • ...The manipulation graph is computed as in Nielsen and Kavraki (2000) using a probabilistic technique ( Kavraki and Latombe 1994; Overmars and Svestka 1994), but our construction of the manipulation roadmap integrates a specific step allowing us to directly capture the connectivity of the submanifold CG ∩ CP inside the roadmap....

    [...]

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