Environment manipulation planner for humanoid robots using task graph that generates action sequence

Okada, Kunihiko; Haneda, A.; Nakai, Hirokazu; Inaba, Masayuki; Inoue, H.

doi:10.1109/iros.2004.1389555

Cited by 40 publications

(39 citation statements)

References 14 publications

(10 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An example of a work considering a fully known environment can be found in [26], where the problem is solved as follows. The initial configuration, the goal configuration and the configurations describing the position of the obstacles are considered as nodes of a graph, a collision free path is searched between every pair of these configurations and when it is found the corresponding nodes are connected in the graph, and finally, the task solution path is found searching the graph for a branch from the initial to the final configuration; every intermediate node in this branch represents an obstacle that must be removed to execute the task.…”

Section: Related Workmentioning

confidence: 99%

Planning manipulation movements of a dual-arm system considering obstacle removing

Rodríguez

Montano

Suárez

2014

Robotics and Autonomous Systems

View full text Add to dashboard Cite

The paper deals with the problem of planning movements of two hand-arm robotic systems, considering the possibility of using the robot hands to remove potential obstacles in order to obtain a free access to grasp a desired object. The approach is based on a variation of a Probabilistic Road Map that does not rule out the samples implying collisions with removable objects but instead classifies them according to the collided obstacle(s), and allows the search of free paths with the indication of which objects must be removed from the work-space to make the path actually valid, we call it Probabilistic Road Map with Obstacles (PRMwO). The proposed system includes a task assignment system that distributes the task among the robots, using for that purpose a precedence graph built from the results of the PRMwO. The approach has been implemented for a real dualarm robotic system, and some simulated and real running examples are presented in the paper.

show abstract

Section: Related Workmentioning

confidence: 99%

Planning manipulation movements of a dual-arm system considering obstacle removing

Rodríguez

Montano

Suárez

2014

Robotics and Autonomous Systems

View full text Add to dashboard Cite

show abstract

“…Much of this work assumes only rigid grasping [1,14,17,18,19] or that each object moves only once [14,17,18,19]. van den Berg et al [1] relax this second assumption, but their approach relies on describing connected components of a robot's configuration space, which is intractable for high-dimensional configuration spaces.…”

Section: Related Workmentioning

confidence: 99%

Manipulation with Multiple Action Types

Barry

Hsiao

Kaelbling

et al. 2013

Experimental Robotics

View full text Add to dashboard Cite

We present DARRT, a sampling-based algorithm for planning with multiple types of manipulation. Given a robot, a set of movable objects, and a set of actions for manipulating the objects, DARRT returns a sequence of manipulation actions that move the robot and objects from an initial configuration to a final configuration. The manipulation actions may be non-prehensile, meaning that the object is not rigidly attached to the robot, such as push, tilt, or pull. We describe a simple extension to the RRT algorithm to search the combined space of robot and objects and present an implementation of DARRT on the Willow Garage PR2 robot.

show abstract

“…In humanoid robot manipulation tasks, grasp planners are seldom used online. Instead often fixed manipulation points on the objects [83], [100] are defined to be able to move objects around and avoid grasp planning. Further, predefined grasps [52] or simple rules for grasp generation are used [9].…”

Section: Grasp Planningmentioning

confidence: 99%

Knowledge Representations for Planning Manipulation Tasks

Zacharias

2012

Cognitive Systems Monographs

View full text Add to dashboard Cite

A complex manipulation task for a robot has to be planned on different levels of abstraction before it can be executed on a real system. At a high-level of abstraction, a task planner generates a sequence of subtasks from a given abstract command, its pre-and postconditions and a current world model. To restrict the already huge search space of the task planner, aspects like geometric information are not contained in the world model at this level. The task planner then assigns the subtasks to planners on a lower level of abstraction like path planners or grasp planners. These planners rely on geometric information to compute their solutions. However, without considering geometric information the task planner cannot determine good parameters for these low-level planners. Therefore, the derived plans may not be valid for a given situation and may require backtracking. Knowledge representations can bridge the gap between these planning levels and thereby enable scene reasoning. The kinematic capabilities of a robot are one aspect the task planner has to be aware of. Therefore this thesis introduces a novel general representation of the kinematic capabilities of a robot arm. The versatile workspace is defined to describe in which orientations the end effector attached to a robot arm can reach a position. The capability map is a calculable representation of the versatile workspace that allows to determine how well regions of the workspace are reachable. It accurately represents the versatile workspace of arbitrary arm kinematics and enables autonomous taskspecific reasoning. Furthermore, its visualization scheme is intuitively comprehensible for the human. The versatile applicability of the capability map is shown by examples from several distinct application domains. In human-robot interaction, the capability map is used to objectively evaluate a bi-manual interface for tele-operation. The results can also supplement the design process for humanoid robot design. In low-level geometric planning, the capability map is used to reduce the search space and to parameterize path planners and grasp planners. Thus, in a manipulation task for a humanoid robot more human-like motion is planned while simultaneously the computation time is reduced. In high-level task reasoning, the capability map is used to evaluate how well a robot is suited for a task. In an example a humanoid robot has to perform a task involving 3D trajectories. Regions are extracted from the capability map that permit the task execution and the suitability of the robot is inferred.

show abstract

Environment manipulation planner for humanoid robots using task graph that generates action sequence

Cited by 40 publications

References 14 publications

Planning manipulation movements of a dual-arm system considering obstacle removing

Planning manipulation movements of a dual-arm system considering obstacle removing

Manipulation with Multiple Action Types

Knowledge Representations for Planning Manipulation Tasks

Contact Info

Product

Resources

About