Abstract. This document describes hardware and software of the robots developed by the &quot;FUmanoid&quot; Team for the RoboCup competitions to be held in Graz, Austria 2009. The robot has 22 actuated degrees of freedom based on Dynamixel RX28, and RX64 servos. Central Processing, including Machine vision, Planning and control is performed using a Gumstix Verdex 6LP which is an ARM based 600MHz platform. Planning algorithms are organized in a new structure called Concurrent Scenario based Planning (CSBP). This paper explains the software and hardware used for the robot as well as control and stabilization methods developed by our team

Alexej Tuchscherer

Bennet Fischer

Björn Kahlert

Daniel Seifert

Daniel Steig

Gretta Hohl

Hamid Reza Moballegh

Kai Stoll

Mariusz Kukulski

Raúl Rojas

Sebastian Mielke

Stefan Otte

Steffen Heinrich

Tim Landgraf

Tobias Langner

Torsten Fassbender

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FUmanoid Team Description 2009
Hamid Reza Moballegh, Gretta Hohl, Tim Landgraf, Bennet Fischer, Tobias 
Langner, Torsten Fassbender, Stefan Otte, Kai Stoll, Alexej Tuchscherer, Daniel 
Steig, Steffen Heinrich, Sebastian Mielke, Daniel Seifert, Mariusz Kukulski, Björn 
Kahlert, Raúl Rojas
Institut für Informatik, Arbeitsgruppe Künstliche Intelligenz,
Freie Universität Berlin, Takustr.9, 14195 Berlin, Germany
http://fumanoid.mi.fu-berlin.de
Abstract. This  document  describes  hardware  and  software  of  the  robots 
developed by the “FUmanoid” Team for the RoboCup competitions to be held 
in Graz, Austria 2009. The robot has 22 actuated degrees of freedom based on 
Dynamixel  RX28,  and RX64 servos.  Central  Processing,  including Machine 
vision, Planning and control is performed using a Gumstix Verdex 6LP which 
is an ARM based 600MHz platform. Planning algorithms are organized in a 
new structure called Concurrent Scenario based Planning (CSBP). This paper 
explains the software and hardware used for the robot as well as control and 
stabilization methods developed by our team.
1. Introduction
Humanoid  robots  have  many  potential  applications,  which  make  this  area  very 
attractive for researchers. However many of the yet developed humanoids suffer from 
over-designed and too complicated hardware and software which is still far from the 
human model.
The FUmanoid team was started in 2006 in the Artificial  Intelligence group at 
Freie Universität  Berlin,  which has had a successful  and long history in RoboCup 
with the FU-Fighters team. The team has shown an excellent performance in its first 
year of activity by winning the 3rd place of the world RoboCup humanoid league in 
kid-size class, presenting the lightest and the least expensive football playing robots 
in their class. This is achieved by advancing several solutions in the areas of hardware 
and software, which will be explained briefly in this paper.
The FUmanoid project is a step towards research and development of robots which 
offer more real human-interaction, can perform tasks in our environment and will be 
able to play important roles in our daily life. 
2. Hardware Design 
2.1. Mechanical Structure
The actuators  used in  the FUmanoid robots  is  the Dynamixel  servo  motor family 
produced by Robotis Inc.  Korea. The motion mechanism consists of 22 degrees of 
freedom distributed in 7 per leg, 3 per arm and other two degrees of freedom as a pan-
tilt system holding the head. 
Fig.1 shows one of the constructions used for the motion mechanism of the robots. 
Knee joints are considered to bend in both directions which help faster response of the 
robot in backward walking. Efforts have been made to hold the proportions as much 
as possible human like. Table1 illustrates the physical measurements of the robot. To 
facilitate exchange of the players, all robots use mechanically the same structure.
 
Fig. 1.Mechanical construction of the Robots
Quantity Value Unit
Overall Height 60 cm
COM Height 45 cm
Weight 4320 G
Leg Length 33 cm
Foot Area 120 cm2
Arm Length 25 cm
Head Length 10 cm
Table 1.Physical measurements of the robot
2.2. Actuators
The actuators  used in FUmanoid robots are “Dynamixel  AX-28” and “Dynamixel 
RX-64”  servomotors,  produced  by  Robotis  Inc.  Each  actuator  has  its  own 
microcontroller  which  implements  adjustable  position  control  using  potentiometer 
position feedback. It also calculates many other parameters such as rotation speed and 
motor  load  which  can  be  accessed  through  a  single-bus,  high-speed  serial 
communication protocol. This facilitates the construction of an extendable network of 
motors which can be individually accessed and controlled by a single microprocessor. 
The parameters of the actuators used in FUmanoid robots are summarized in table 2. 
Weight 
g
Gear Ratio Max Torque 
kgf.cm
Speed 
sec/60º
Resolution 
degrees
Dynamixel AX-64 125 1 : 200 64.4(@15V) 0.188 0.35
Dynamixel RX-28 72 1 : 193 28.3(@12V) 0.167 0.35
Table 2.Characteristics of the servomotors used in FUmanoid Robots
2.3 Sensors
Due to the single bus structure, almost every type of sensors can be easily integrated 
with the hardware. The robot is equipped with following sensors: 
- Compass
To accelerate self localization, a compass module is developed which is connected to 
the system bus and obtains the direction of the robot. However the measurements are 
subject to error und non-linearization due to magnetic fields present on the robots. 
These can be back calibrated to some extent, however the results should be improved 
using other sensors, i.e. vision based self localization.
- Pressure sensors
Measurements  form  pressure  sensors  are  used  as  a  feedback  to  synchronize  the 
walking with the mechanical  properties of the robot and also to stabilize dynamic 
motions such as kick and standup.
- Accelerometer/Tilt sensor
A 3 axis accelerometer  is  connected to the bus.  It  is  usually used to measure the 
direction of the gravitation vector. This measurement can be then used either to detect 
a fall down or to stabilize the robot in motions such as walking or kick.
- Actuator feedback
The feedback  of  the  actuators  includes  the  current  joint  angle,  the  current  motor 
speed, and the load. Because all of this values are derived from the only feedback 
sensor of  the actuators  (the position potentiometer),  the latter  two values  are  less 
reliable.  There  are  also  other  measured  values  which  can  be  accessed  through 
Dynamixel serial interface, such as supplied voltage and temperature, which can be 
used for safety purposes.  Joint position measurement is very helpful in stable gait 
generation for the robots.
- Visual feedback
The robot  is  equipped with 3 cameras  which can  cover  a  full  range  of  180 x 90 
degrees. Two cameras are multiplexed and connected directly to the Quick Capture 
interface  of  the  CPU.  These  cameras  have  a  horizontal  overlapping  region  of  60 
degrees this feature is additionally used by a stereo vision algorithm for measurement 
of camera tilt and also distance of partially observed obstacles. 
The  third  camera  is connected  through  USB and  is  used  for  recognition  of  near 
objects. 
2.4 Processors and communications
Each Robot has a Gumstix verdex pro XL6P on board. The Processor is a Marvell® 
PXA270 with XScale™ running at 600MHz. The gumstix motherboard has several 
interesting features which make it ideal as a brain for humanoid robots. These include 
low weight and power consumption, direct camera connection and easy extension.
All hardware units including motors and sensors are connected to the main processor 
via an RS485 bus. Each unit has a unique ID for packet identification. A broadcasting 
ID can be used to send the same data packet to all existing units on the bus. 
 3. Software Design 
Fig.3  shows  the  block  diagram  of  the  software  which  runs  in  the  robot’s  main 
processor. The program consists of 4 main blocks:
• Hardware Interface:  Contains all low level routines to access hardware of the 
robot including sensors and actuators.
• Vision: Contains image processing algorithms such as recognition of landmarks 
and other object.  Self localization is done using particle filtering. Particles are 
scored by comparing a simulated image from each particle with the current frame 
captured by camera. Using “Sampling-Importance Resampling” method, a new 
distribution of the particles is created after each step. Particles are also updated 
using a motion model. Final  distribution of the particles converges to the real 
pose of the robot. 
• Planning: Planning system of  the robot is  based  on a multi  layer,  and multi 
thread  structure.  The  layers  are  named Strategy,  Role,  Behavior  and  Motion. 
Each layer contains a Scenario which runs in parallel with the scenarios in the 
other layers. A scenario in a higher level can terminate and change the scenario 
running in the lower level; however it is usually done in synchronization with the 
lower level  scenario to avoid conflicts and instabilities.  (such as stopping the 
walking motion while one of the feet is still in the air). 
• Network: Mainly responsible for the wireless communication of the robot with 
the other robots or the referee box. This is done via WLAN.
Fig. 2. Structure of the control software
4. Stabilization and Control
Stabilizing  humanoid  robots  is  a  challenging  subject  which  has  attracted  many 
researchers who have developed widely varying techniques. These techniques range 
from simple and static COG methods to poorly dynamic nonlinear control methods 
using multi-DOF underactuated inverted pendulum models.  A novel approach was 
developed for the biped walking stability problem by McGeer,  who pioneered the 
idea of passive dynamic walking [2]. This approach which is both simple and direct 
has been followed by Collins, Wisse and Ruina and has been improved with different 
techniques to obtain 3D walking stability [3].
Both 2D and 3D passive dynamic walkers receive their energy from changes in 
height of their COM as they walk down a shallow heel. Therefore the original passive 
walking is  not  suited  to  applications  such  as  football  playing  in  which  the  robot 
should not only walk on a level surface but also change its velocity and direction very 
often. To solve this problem, further researches have presented several  methods of 
pumping energy into a passive walker such as torso control  [4], active toes  [5] and 
virtual gravity [6].
The aim of our biped walking research is to develop a walking technique which 
uses few sensory data (i.e. only joint angle data) and provides stability over a wide 
range of velocities. To examine the present solutions and be also able to study new 
ideas, a simplified model of the robot has been simulated with ODE and its walking 
stability has been tested in simulations. Using this simulator, some new techniques 
have been introduced to improve walking stability and to control the walker. 
However implementing the simulated control ideas in a real robot is as difficult as 
re-doing the whole work despite of the simulated results. This is because of the vast 
Hardware InterfaceNetworkVision Planning 
Robot Recognition
Self localization
Role 1
Role 2
…
Behavior Engine
Role Engine
Ball Recognition
Goal Recognition
Motion Engine
Motion 1
Motion 2
…
Beh. 2
Beh. 1
…
Odometry Data
Strategy Engine
difference between the simulated and the real platform. Actuators used in most of the 
humanoid platforms are servomotors, which have normally a high grade of damping 
regarding the gear reduction ratio and have also strong limits in their maximum speed 
and/or applied torque. This is very disadvantageous as the energy of centre of mass is 
of  great  importance  in  passive  dynamic  walking.  A  direct  torque  control  is  also 
provided by almost none of the commercially available servomotors. 
As a test, the friction of the ankle actuators of each foot was reduced by removing 
a gear from each, so that they could only function as low friction joints with position 
sensors, the data derived from this sensors are then used in the control program which 
finds the stance foot at each step and controls all active actuators regarding to the 
stance angle. The robot has been able to walk several steps on a slope.
To have the passive walking controlled and supplied from own energy of the robot, 
one should be able either to switch the actuators to act as passive free running and 
active in different walking phases or  to decrease the stiffness of the servos and use 
them in a mixed way both as sensors and actuators. Using the later technique, stable 
walking at velocities up to 40cm/s has been achieved. 
Because several other necessary behaviors of the robot are fully active and mostly 
semi-static,  a  key-frame  interpolator  is  developed  and  used  to  generate  stable 
trajectories for different behaviors. This unit together with the walk controller forms 
the so called “Motion Engine”.
References
1. H.  Moballegh,  M.  Mohajer,  R.  Rojas:  Increasing  foot  clearance  in  biped  walking: 
Independence of body vibration amplitude from foot clearance. In: L. Iocchi, H. Matsubara, 
A. Weitzenfeld, C. Zhou, editors, RoboCup 2008: Robot Soccer World Cup XII,  LNCS. 
Springer, 2008.
2.  McGeer,  T.  Passive  dynamic  walking.  In:  International  Journal  of  Robotics  Research 
9(2):62–82. 1990.
3. S. H., Wisse, M., Ruina, A. A Three-Dimensional Passive-Dynamic Walking Robot with 
Two Legs and Knees, Collins, In: International Journal of Robotics Research, Vol. 20, No. 
2, Pages 607-615, 2001
4. Haruna, M., Ogino, M., Hosoda, K. and Minour, A., Yet another humanoid walking - Passive 
dynamic walking with torso under a simple control In: Int. Conference on intelligent robots 
and system, 2001
5. Sven Behnke, Jürgen Müller, Michael Schreiber: Toni: A Soccer Playing Humanoid Robot. 
In: RoboCup 2005: 59-704. 
6. Asano F. and Yamakita M. Virtual Gravity and Coupling Control for Robotic Gait Synthesis. 
In: IEEE Trans. on Systems, Man and Cybernetics, Part A: Systems and Humans, Vol. 31, 
No. 6, pp. 737-745, November 2001. 
7. Erik V. Cuevas, Daniel Zaldívar, and Raúl Rojas: Incremental fuzzy control for a biped robot 
balance.  In:  International  Conference  on Robotics  and Applications  2005,  October  31 - 
November 2, 2005 Cambridge, USA
8. Eric V. Cuevas, Daniel Zaldívar, and Raúl Rojas: Dynamic Control Algorithm for a Biped 
Robot. In: 7th International Conference on Control and Applications, Cancún, Mexico, May 
18-20, 2005.
8. RoboCup Humanoid Team of the Freie Universität Berlin, http://fumanoid.mi.fu-berlin.de/ 


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