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.
[1]
Ian Millington,et al.
Artificial Intelligence for Games
,
2006,
The Morgan Kaufmann series in interactive 3D technology.
[2]
Danica Kragic,et al.
A database for reproducible manipulation research: CapriDB – Capture, Print, Innovate
,
2017,
Data in brief.
[3]
Danica Kragic,et al.
SimTrack: A simulation-based framework for scalable real-time object pose detection and tracking
,
2015,
2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[4]
Petter Ögren,et al.
Behavior Trees in Robotics and AI: An Introduction
,
2017,
ArXiv.
[5]
Danica Kragic,et al.
Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
,
2016,
IEEE Transactions on Robotics.
[6]
David G. Lowe,et al.
Distinctive Image Features from Scale-Invariant Keypoints
,
2004,
International Journal of Computer Vision.