ROApr 25, 2014

Similar Part Rearrangement With Pebble Graphs

arXiv:1404.6573v11 citations
Originality Incremental advance
AI Analysis

This addresses the challenge of efficiently rearranging similar objects in cluttered environments for applications like industrial automation or retrieval tasks, representing an incremental improvement over existing methods.

The paper tackles the problem of computing manipulation paths to rearrange similar objects in cluttered spaces, such as factory floors or shelves, by proposing graphical representations that allow reasoning about transitions between object arrangements without explicit enumeration. The method was evaluated in simulation with a Baxter robot and executed in open-loop on the real system, showing it solves complex instances with promising scalability and success ratio.

This work proposes a method for effectively computing manipulation paths to rearrange similar objects in a cluttered space. The solution can be used to place similar products in a factory floor in a desirable arrangement or for retrieving a particular object from a shelf blocked by similarly sized objects. These are challenging problems as they involve combinatorially large, continuous configuration spaces due to the presence of multiple moving bodies and kinematically complex manipulators. This work leverages ideas from algorithmic theory, multi-robot motion planning and manipulation planning to propose appropriate graphical representations for this challenge. These representations allow to quickly reason whether manipulation paths allow the transition between entire sets of objects arrangements without having to explicitly enumerate the path for each pair of arrangements. The proposed method also allows to take advantage of precomputation given a manipulation roadmap for transferring a single object in the same cluttered space. The resulting approach is evaluated in simulation for a realistic model of a Baxter robot and executed in open-loop on the real system, showing that the approach solves complex instances and is promising in terms of scalability and success ratio.

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