Results 1 - 10
of
27
BiSpace Planning: Concurrent Multi-Space Exploration
"... Abstract — We present a planning algorithm called BiSpace that produces fast plans to complex high-dimensional problems by simultaneously exploring multiple spaces. We specifically focus on finding robust solutions to manipulation and grasp planning problems by using BiSpace’s special characteristic ..."
Abstract
-
Cited by 31 (2 self)
- Add to MetaCart
(Show Context)
Abstract — We present a planning algorithm called BiSpace that produces fast plans to complex high-dimensional problems by simultaneously exploring multiple spaces. We specifically focus on finding robust solutions to manipulation and grasp planning problems by using BiSpace’s special characteristics to explore the work and configuration spaces of the environment and robot. Furthermore, we present a number of techniques for constructing informed heuristics to intelligently search through these highdimensional spaces. In general, the BiSpace planner is applicable to any problem involving multiple search spaces. I.
Trajectory Prediction: Learning to Map Situations to Robot Trajectories
"... Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal trajectories from scratch in a new situation. In effect, extensive data is accumulated containing situations together with the respective optimized trajec ..."
Abstract
-
Cited by 19 (1 self)
- Add to MetaCart
(Show Context)
Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal trajectories from scratch in a new situation. In effect, extensive data is accumulated containing situations together with the respective optimized trajectories – but this data is in practice hardly exploited. The aim of this paper is to learn from this data. Given a new situation we want to predict a suitable trajectory which only needs minor refinement by a conventional optimizer. Our approach has two essential ingredients. First, to generalize from previous situations to new ones we need an appropriate situation descriptor – we propose a sparse feature selection approach to find such wellgeneralizing features of situations. Second, the transfer of previously optimized trajectories to a new situation should not be made in joint angle space – we propose a more efficient task space transfer of old trajectories to new situations. Experiments on a simulated humanoid reaching problem show that we can predict reasonable motion prototypes in new situations for which the refinement is much faster than an optimization from scratch. 1.
Constraint Propagation on Interval Bounds for Dealing with Geometric Backtracking
"... Abstract — The combination of task and motion planning presents us with a new problem that we call geometric backtracking. This problem arises from the fact that a single symbolic state or action may be geometrically instantiated in infinitely many ways. When a symbolic action cannot be geometricall ..."
Abstract
-
Cited by 7 (1 self)
- Add to MetaCart
(Show Context)
Abstract — The combination of task and motion planning presents us with a new problem that we call geometric backtracking. This problem arises from the fact that a single symbolic state or action may be geometrically instantiated in infinitely many ways. When a symbolic action cannot be geometrically validated, we may need to backtrack in the space of geometric configurations, which greatly increases the complexity of the whole planning process. In this paper, we address this problem using intervals to represent geometric configurations, and constraint propagation techniques to shrink these intervals according to the geometric constraints of the problem. After propagation, either (i) the intervals are shrunk, thus reducing the search space in which geometric backtracking may occur, or (ii) the constraints are inconsistent, indicating the non-feasibility of the sequence of actions without further effort. We illustrate our approach on scenarios in which a two-arm robot manipulates a set of objects, and report experiments that show how the search space is reduced. I.
Positioning Mobile Manipulators to Perform Constrained Linear Trajectories
- In Proc. of the IEEE/RSJ International Conf. on Intelligent Robots and Systems, 2008. Preprint submitted to 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems. Received February 28
, 2009
"... Abstract — For mobile manipulators envisioned in home en-vironments a kitchen scenario provides a challenging testbed for numerous skills. Diverse manipulation actions are required, e.g. simple pick and place for moving objects and constrained motions for opening doors and drawers. The robot kinemat ..."
Abstract
-
Cited by 7 (1 self)
- Add to MetaCart
(Show Context)
Abstract — For mobile manipulators envisioned in home en-vironments a kitchen scenario provides a challenging testbed for numerous skills. Diverse manipulation actions are required, e.g. simple pick and place for moving objects and constrained motions for opening doors and drawers. The robot kinematics and link limits however are restrictive. Therefore especially a constrained trajectory will not be executable from arbitrary placements of the mobile manipulator. A two stage approach is presented to position a mobile manipulator to execute constrained linear trajectories as needed for opening drawers. In a first stage, a representation of a robot arm’s reachable workspace is computed. Pattern recog-nition techniques are used to find regions in the workspace representation where these trajectories are possible. A set of trajectories results. In the second stage mobile manipulator placements are computed and the corresponding trajectories are checked for collisions. Compared to a brute force search through the workspace, the success rate of finding a mobile manipulator placement can be increased from 2 % to 70%. I.
Learning and reasoning with action-related places for robust mobile manipulation
- Journal of Artificial Intelligence Research
"... We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probabili ..."
Abstract
-
Cited by 6 (0 self)
- Add to MetaCart
(Show Context)
We propose the concept of Action-Related Place (ARPlace) as a powerful and flexible representation of task-related place in the context of mobile manipulation. ARPlace represents robot base locations not as a single position, but rather as a collection of positions, each with an associated probability that the manipulation action will succeed when located there. ARPlaces are generated using a predictive model that is acquired through experience-based learning, and take into account the uncertainty the robot has about its own location and the location of the object to be manipulated. When executing the task, rather than choosing one specific goal position based only on the initial knowledge about the task context, the robot instantiates an ARPlace, and bases its decisions on this ARPlace, which is updated as new information about the task becomes available. To show the advantages of this least-commitment approach, we present a transformational planner that reasons about ARPlaces in order to optimize symbolic plans. Our empirical evaluation demonstrates that using ARPlaces leads to more robust and efficient mobile manipulation in the face of state estimation uncertainty on our simulated robot. 1.
Optimization of fluent approach and grasp motions
"... Abstract — Generating a fluent motion of approaching, grasping and lifting an object comprises a number of problems which are typically tackled separately. Some existing research specializes on the optimization of the final grasp posture based on force closure criteria neglecting the motion necessar ..."
Abstract
-
Cited by 5 (3 self)
- Add to MetaCart
(Show Context)
Abstract — Generating a fluent motion of approaching, grasping and lifting an object comprises a number of problems which are typically tackled separately. Some existing research specializes on the optimization of the final grasp posture based on force closure criteria neglecting the motion necessary to approach this grasp. Other research specializes on motion optimization including collision avoidance criteria, but typically not considering the subsequent grasp as part of the optimization problem. In this paper we aim to combine existing techniques for grasp optimization, trajectory optimization, and attractor-based movement representation, into a comprehensive framework that allows us to efficiently compute a fluent approach and grasping motion. The feasibility of the proposed approach is shown in simulation studies and experiments with a humanoid robot. I.
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72546 Visual Odometry for Tracked Vehicles
"... N.B.: When citing this work, cite the original article. ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to ..."
Abstract
-
Cited by 5 (1 self)
- Add to MetaCart
N.B.: When citing this work, cite the original article. ©2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Trajectory prediction in Cluttered Voxel Environments
- IEEE International Conference on Robotics and Automation - ICRA2010
, 2010
"... Abstract — Trajectory planning and optimization is a fundamental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. There are many methods that can ..."
Abstract
-
Cited by 5 (0 self)
- Add to MetaCart
(Show Context)
Abstract — Trajectory planning and optimization is a fundamental problem in articulated robotics. It is often viewed as a two phase problem of initial feasible path planning around obstacles and subsequent optimization of a trajectory satisfying dynamical constraints. There are many methods that can generate good movements when given enough time, but planning for high-dimensional robot configuration spaces in realistic environments with many objects in real time remains challenging. This work presents a novel way for faster movement planning in such environments by predicting good path initializations. We build on our previous work on trajectory prediction by adapting it to environments modeled with voxel grids and defining a frame invariant prototype trajectory space. The constructed representations can generalize to a wide range of situations, allowing to predict good movement trajectories and speed up convergence of robot motion planning. An empirical comparison of the effect on planning movements with a combination of different trajectory initializations and local planners is presented and tested on a Schunk arm manipulation platform with laser sensors in simulation and hardware. I.
Using a Model of the Reachable Workspace to Position Mobile Manipulators for 3-d Trajectories
"... Abstract — Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening d ..."
Abstract
-
Cited by 4 (2 self)
- Add to MetaCart
(Show Context)
Abstract — Humanoid robots are envisioned in general household tasks. To be able to fulfill a given task the robot needs to be equipped with knowledge concerning the manipulation and interaction in the environment and with knowledge about its own capabilities. When performing actions, e.g. opening doors or imitating human reach to grasp movements special 3-d trajectories are followed with the robot’s end-effector. These trajectories can not be executed in every part of the robot’s arm workspace. Therefore a task planner has to determine if and how additional degrees of freedom such as the robot’s upper body or the robot’s base can be moved in order to execute the task-specific trajectory. An approach is presented that computes placements for a mobile manipulator online given a task-related 3-d trajectory. A discrete representation of the robot arm’s reachable workspace is used. Task-specific trajectories are interpreted as patterns and searched in the reachability model using multi-dimensional correlation. The relevance of the presented approach is demonstrated in simulated positioning tasks. I.
Voxel-Based Motion Bounding and Workspace Estimation for Robotic Manipulators
"... Abstract — Identification of regions in space that a robotic manipulator can reach in a given amount of time is important for many applications, such as safety monitoring of industrial manipulators and trajectory and task planning. However, due to the high-dimensional configuration space of many rob ..."
Abstract
-
Cited by 4 (2 self)
- Add to MetaCart
(Show Context)
Abstract — Identification of regions in space that a robotic manipulator can reach in a given amount of time is important for many applications, such as safety monitoring of industrial manipulators and trajectory and task planning. However, due to the high-dimensional configuration space of many robots, reasoning about possible physical motion is often intractable. In this paper, we propose a novel method for creating a reachability grid, a voxel-based representation that estimates the minimum time needed for a manipulator to reach any physical location within its workspace. We use up to second-degree constraints on joint motion to model motion limits for each joint independently, followed by successive voxel approximations to map these limits on to the robot’s physical workspace. Results using a simulated manipulator indicate that our method can produce accurate reachability grids in real-time, even for robots with many degrees of freedom. Furthermore, errors are almost exclusively biased towards producing more optimistic reachability estimates, which is a desirable characteristic for many applications. I.
