1,650 research outputs found
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar
Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction
Navigating in search and rescue environments is challenging, since a variety
of terrains has to be considered. Hybrid driving-stepping locomotion, as
provided by our robot Momaro, is a promising approach. Similar to other
locomotion methods, it incorporates many degrees of freedom---offering high
flexibility but making planning computationally expensive for larger
environments.
We propose a navigation planning method, which unifies different levels of
representation in a single planner. In the vicinity of the robot, it provides
plans with a fine resolution and a high robot state dimensionality. With
increasing distance from the robot, plans become coarser and the robot state
dimensionality decreases. We compensate this loss of information by enriching
coarser representations with additional semantics. Experiments show that the
proposed planner provides plans for large, challenging scenarios in feasible
time.Comment: In Proceedings of IEEE International Conference on Robotics and
Automation (ICRA), Brisbane, Australia, May 201
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