To send this article to your account, please select one or more formats and confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about sending content to .
To send this article to your Kindle, first ensure email@example.com is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about sending to your Kindle.
Find out more about sending to your Kindle.
Note you can select to send to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be sent to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Robot Interaction has always been a challenge in collaborative robotics. In tasks
comprising Inter-Robot Interaction, robot detection is very often needed. We
explore humanoid robots detection because, humanoid robots can be useful in many
scenarios, and everything from helping elderly people live in their own homes to
responding to disasters. Cameras are chosen because they are reach and cheap
sensors, and there are lots of mature two-dimensional (2D) and 3D computer
vision libraries which facilitate Image analysis. To tackle humanoid robot
detection effectively, we collected a data set of various humanoid robots with
different sizes in different environments. Afterward, we tested the well-known
cascade classifier in combination with several image descriptors like Histograms
of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set.
Among the feature sets, Haar-like has the highest accuracy, LBP the highest
recall, and HOG the highest precision. Considering Inter-Robot Interaction, it
is evident that false positives are less troublesome than false negatives, thus
LBP is more useful than the others.
This paper presents our preliminary research into the autonomous control of an
alpine skiing robot. Based on our previous experience with active balancing on
difficult terrain and developing an ice-skating robot, we have implemented a
simple control system that allows the humanoid robot Jennifer to steer around a
simple alpine skiing course, brake, and actively control the pitch and roll of
the skis in order to maintain stability on hills with variable inclination.
The robot steers and brakes by using the edges of the skis to dig into the snow,
by inclining both skis to one side the robot can turn in an arc. By rolling the
skis outward and pointing the toes together the robot creates a snowplough shape
that rapidly reduces its forward velocity.
To keep the skis in constant contact with the hill we use two independent
proportional-integral-derivative (PID) controllers to continually adjust the
robot’s inclination in the frontal and sagittal planes.
Our experiments show that these techniques are sufficient to allow a small
humanoid robot to alpine ski autonomously down hills of different inclination
with variable snow conditions.
Obstacle avoidance is an important issue in robotics. In this paper, the particle
swarm optimization (PSO) algorithm, which is inspired by the collective
behaviors of birds, has been designed for solving the obstacle avoidance
problem. Some animals that travel to the different places at a specific time of
the year are called migrants. The migrants also represent the particles of PSO
for defining the walking paths in this work. Migrants consider not only the
collective behaviors, but also geomagnetic fields during their migration in
nature. Therefore, in order to improve the performance and the convergence speed
of the PSO algorithm, concepts from the migrant navigation method have been
adopted for use in the proposed hybrid particle swarm optimization (H-PSO)
algorithm. Moreover, the potential field navigation method and the designed
fuzzy logic controller have been combined in H-PSO, which provided a good
performance in the simulation and the experimental results. Finally, the
Federation of International Robot-soccer Association (FIRA) HuroCup Obstacle Run
Event has been chosen for validating the feasibility and the practicability of
the proposed method in real time. The designed adult-sized humanoid robot also
performed well in the 2015 FIRA HuroCup Obstacle Run Event through utilizing the
This paper presents a parameterized gait generator based on linear inverted
pendulum model (LIPM) theory, which allows users to generate a natural gait
pattern with desired step sizes. Five types of zero moment point (ZMP)
components are proposed for formulating a natural ZMP reference, where ZMP moves
continuously during single support phases instead of staying at a fixed point in
the sagittal and lateral plane. The corresponding center of mass (CoM)
trajectories for these components are derived by LIPM theory. To generate a
parameterized gait pattern with user-defined parameters, a gait planning
algorithm is proposed, which determines related coefficients and boundary
conditions of the CoM trajectory for each step. The proposed parameterized gait
generator also provides a concept for users to generate gait patterns with
self-defined ZMP references by using different components. Finally, the
feasibility of the proposed method is validated by the experimental results with
a teen-sized humanoid robot, David, which won first place in the sprint event at
the 20th Federation of International Robot-soccer Association (FIRA) RoboWorld
This study presents the algorithm for a humanoid robot to accomplish an obstacle
run in the FIRA HuroCup competition. It includes the integration of image
processing and robot motion. DARwIn-OP (Dynamic Anthropomorphic Robot with
Intelligence–Open Platform) was used as the humanoid robot, and it is
equipped with a webcam as a vision system to obtain an image of what is in front
of the robot. Image processing skills such as erosion, dilation, and
eight-connected component labeling are applied to reduce image noise. Moreover,
we use navigation grids with filters to avoid the obstacles. Fuzzy logic rules
are used to implement the robot’s motion, allowing a humanoid robot to
access any routes using obstacle avoidance to perform the tasks in the
Usually, humanoid walking gaits are only roughly distinguished between stable and
unstable. The evaluation of a stable humanoid walking gait is difficult to
quantify in scales. And, it is extremely hard to adjust humanoid robots in
suitable a walking gait for different movement objectives such as fast walking,
uneven floor walking, and so on. This paper proposes a stability margin
constructed by center of pressure (COP) to evaluate the gait stability of
humanoid walking. The stability margin is modeled by the COP regions that a
humanoid robot needs for stable standing. We derive the mathematical model for
COP position by dividing the walking gait into single and double support phases
in order to measure the stability of the COP regions. An actual measuring system
for the stable COP regions is designed and implemented. The measured COP
trajectory of a walking gait is eventually evaluated with respect to the stable
COP regions for the stability margins. The evaluation focuses on weak stability
areas to be improved for robust walking gaits. To demonstrate the robustness of
the improved walking gait, we replicate the experiment on three different
terrains. The experiments demonstrate that the walking gaits developed based on
stable COP region can be applied for different movement objectives.
This paper describes the motivation for the development of the HuroCup
competition and follows the rule development from its inaugural competition from
2002 to 2015. The history of HuroCup is broken down into its growing phase
(2002–2006), a time of explosive growth (2007–2011), and current
times. This paper describes the main research focus of HuroCup, the multi-event
humanoid robot competition: (a) active balancing, (b) complex motion planning,
and (c) human–robot interaction and shows how the various HuroCup events
relate to those research topics. This paper concludes with some medium- and
long-term goals of the rule development for HuroCup.