Practical Pushing Planning for Rearrangement Tasks

Ohad Ben-Shahar and Ehud Rivlin.
Practical pushing planning for rearrangement tasks.
IEEE Transactions on Robotics and Automation, 14(4):549--565, 1998

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Abstract

We address the problem of practical manipulation planning for rearrangement tasks of many movable objects. We study a special case of the rearrangement task, where the only allowed manipulation is pushing. We search for algorithms that can provide practical planning time for most common scenarios. We present a hierarchical classification of manipulation problems into several classes, each characterized by properties of the plans that can solve it. Such a classification allows one to consider each class individually, to analyze and exploit properties of each class, and to suggest individual planning methods accordingly. Following this classification, we suggest algorithms for two of the defined classes. Both items have been tested in a simulated environment, with up to 32 movable objects and 66 combined DOF. We present the simulations results as well as some experimental results using a real platform

Co-authors

Bibtex Entry

@article{Ben-ShaharR98a-p,
  title = {Practical pushing planning for rearrangement tasks},
  author = {Ohad Ben-Shahar and Ehud Rivlin},
  year = {1998},
  journal = {IEEE Transactions on Robotics and Automation},
  volume = {14},
  number = {4},
  pages = {549--565},
  abstract = {We address the problem of practical manipulation planning for rearrangement tasks of many movable objects. We study a special case of the rearrangement task, where the only allowed manipulation is pushing. We search for algorithms that can provide practical planning time for most common scenarios. We present a hierarchical classification of manipulation problems into several classes, each characterized by properties of the plans that can solve it. Such a classification allows one to consider each class individually, to analyze and exploit properties of each class, and to suggest individual planning methods accordingly. Following this classification, we suggest algorithms for two of the defined classes. Both items have been tested in a simulated environment, with up to 32 movable objects and 66 combined DOF. We present the simulations results as well as some experimental results using a real platform}
}