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2020 (English)In: Advances on Robotic Item Picking: Applications in Warehousing and E-Commerce Fulfillment, Springer Nature , 2020, p. 1-12Chapter in book (Other academic)
Abstract [en]
In this paper we present the system we developed for the Amazon Picking Challenge 2015, and discuss some of the lessons learned that may prove useful to researchers and future teams developing autonomous robot picking systems. For the competition we used a PR2 robot, which is a dual arm robot research platform equipped with a mobile base and a variety of 2D and 3D sensors. We adopted a behavior tree to model the overall task execution, where we coordinate the different perception, localization, navigation, and manipulation activities of the system in a modular fashion. Our perception system detects and localizes the target objects in the shelf and it consisted of two components: one for detecting textured rigid objects using the SimTrack vision system, and one for detecting non-textured or nonrigid objects using RGBD features. In addition, we designed a set of grasping strategies to enable the robot to reach and grasp objects inside the confined volume of shelf bins. The competition was a unique opportunity to integrate the work of various researchers at the Robotics, Perception and Learning laboratory (formerly the Computer Vision and Active Perception Laboratory, CVAP) of KTH, and it tested the performance of our robotic system and defined the future direction of our research.
Place, publisher, year, edition, pages
Springer Nature, 2020
Keywords
Autonomous picking system, Behavior trees, Dual arm robot, Mobile picking, MoveIt, Parallel gripper, PR2 robot, SIFT, Texture-based tracking, Volumetric reasoning
National Category
Robotics and automation
Identifiers
urn:nbn:se:kth:diva-331965 (URN)10.1007/978-3-030-35679-8_1 (DOI)2-s2.0-85149591750 (Scopus ID)
Note
Part of ISBN 9783030356798 9783030356781
QC 20230714
2023-07-172023-07-172025-02-09Bibliographically approved