This Blog is maintained by the Robot Perception and Learning lab at CSIE, NTU, Taiwan. Our scientific interests are driven by the desire to build intelligent robots and computers, which are capable of servicing people more efficiently than equivalent manned systems in a wide variety of dynamic and unstructured environments.
Tuesday, January 22, 2013
Lab meeting Jan. 23, 2013 (Gene): Fully Distributed Scalable Smoothing and Mapping with Robust Multi-robot Data Association (IEEE 2012)
Title: Fully Distributed Scalable Smoothing and Mapping with Robust Multi-robot Data Association (IEEE 2012)
Authors: Alexander Chunningham, Kai M. Wurm, Wolfarm Burgard, and Frank Dellaert
Abstract:
In this paper we focus on the multi-robot perception problem, and present an experimentally validated end-to-end multi-robot mapping framework, enabling individual robots in a team to see beyond their individual sensor horizons. The inference part of our system is the DDF-SAM algorithm [1], which provides a decentralized communication and inference scheme, but did not address the crucial issue of data association.
One key contribution is a novel, RANSAC-based, approach for performing the between-robot data associations and initialization of relative frames of reference. We demonstrate this system with both data collected from real robot experiments, as well as in a large scale simulated experiment demonstrating the scalability of the proposed approach.
Link
Tuesday, January 08, 2013
Lab meeting Jan 9th 2013 (Bang-Cheng Wang): Kicking a Ball – Modeling Complex Dynamic Motions for Humanoid Robots
Presented by Bang-Cheng Wang
Authors:
Judith Müller, Tim Laue, and Thomas Röfer
Abstract:
Complex motions like kicking a ball into the goal are becoming
more important in RoboCup leagues such as the Standard Platform
League. Thus, there is a need for motion sequences that can be parameterized
and changed dynamically. This paper presents a motion engine
that translates motions into joint angles by using trajectories. These
motions are defined as a set of Bezier curves that can be changed online
to allow adjusting, for example, a kicking motion precisely to the actual
position of the ball. During the execution, motions are stabilized by
the combination of center of mass balancing and a gyro feedback-based
closed-loop PID controller.
From RoboCup 2010: Robot Soccer World Cup XIV, ser. Lecture Notes
in Artificial Intelligence, E. Chown, A. Matsumoto, P. Pl¨oger,
and J. R. del Solar, Eds. Springer, to appear in 2011.
Authors:
Judith Müller, Tim Laue, and Thomas Röfer
Abstract:
Complex motions like kicking a ball into the goal are becoming
more important in RoboCup leagues such as the Standard Platform
League. Thus, there is a need for motion sequences that can be parameterized
and changed dynamically. This paper presents a motion engine
that translates motions into joint angles by using trajectories. These
motions are defined as a set of Bezier curves that can be changed online
to allow adjusting, for example, a kicking motion precisely to the actual
position of the ball. During the execution, motions are stabilized by
the combination of center of mass balancing and a gyro feedback-based
closed-loop PID controller.