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Probabilistic approach to physical object disentangling

Research output: Contribution to journalArticleScientificpeer-review

Details

Original languageEnglish
Pages (from-to)5510-5517
Number of pages8
JournalIEEE Robotics and Automation Letters
Volume5
Issue number4
DOIs
Publication statusPublished - 2020
Publication typeA1 Journal article-refereed

Abstract

Physically disentangling entangled objects from each other is a problem encountered in waste segregation or in any task that requires disassembly of structures. Often there are no object models, and especially with cluttered irregularly shaped objects, the robot cannot create a model of the scene due to occlusion. One of our key insights is that based on previous sensory input we are only interested in moving an object out of the disentanglement around obstacles. That is, we only need to know where the robot can successfully move in order to plan the disentangling. Due to the uncertainty we integrate information about blocked movements into a probability map. The map defines the probability of the robot successfully moving to a specific configuration. Using as cost the failure probability of a sequence of movements we can then plan and execute disentangling iteratively. Since our approach circumvents only previously encountered obstacles, new movements will yield information about unknown obstacles that block movement until the robot has learned to circumvent all obstacles and disentangling succeeds. In the experiments, we use a special probabilistic version of the Rapidly exploring Random Tree (RRT) algorithm for planning and demonstrate successful disentanglement of objects both in 2-D and 3-D simulation, and, on a KUKA LBR 7-DOF robot. Moreover, our approach outperforms baseline methods.

Keywords

  • Autonomous systems, collision avoidance, intelligent robots, path planning, probabilistic computing, waste recovery

Publication forum classification

Field of science, Statistics Finland