2,391 research outputs found
Fall Prediction and Controlled Fall for Humanoid Robots
Humanoids which resemble humans in their body structure and degrees of freedom are anticipated to work like them within infrastructures and environments constructed for
humans. In such scenarios, even humans who have exceptional manipulation, balancing, and locomotion skills are vulnerable to fall, humanoids being their approximate imitators
are no exception to this. Furthermore, their high center of gravity position in relation to their small support polygon makes them more prone to fall, unlike other robots such
as quadrupeds. The consequences of these falls are so devastating that it can instantly annihilate both the robot and its surroundings. This has become one of the major stumbling
blocks which humanoids have to overcome to operate in real environments. As a result, in this thesis, we have strived to address the imminent fall over of humanoids by developing
different control techniques. The fall over problem as such can be divided into three subissues: fall prediction, controlled fall, and its recovery. In the presented work, the first two
issues have been addressed, and they are presented in three parts.
First, we define what is fall over for humanoids, different sources for it to happen, the effect fall over has both on the robot and to its surroundings, and how to deal with them.
Following which, we give a brief introduction to the overall system which includes both the hardware and software components which have been used throughout the work for varied
purposes.
Second, the first sub-issue is addressed by proposing a generic method to predict the falling over of humanoid robots in a reliable, robust, and agile manner across various
terrains, and also amidst arbitrary disturbances. The aforementioned characteristics are strived to attain by proposing a prediction principle inspired by the human balance sensory
systems. Accordingly, the fusion of multiple sensors such as inertial measurement unit and gyroscope (IMU), foot pressure sensor (FPS), joint encoders, and stereo vision sensor,
which are equivalent to the human\u2019s vestibular, proprioception, and vision systems are considered. We first define a set of feature-based fall indicator variables (FIVs) from the
different sensors, and the thresholds for those FIVs are extracted analytically for four major disturbance scenarios. Further, an online threshold interpolation technique and an
impulse adaptive counter limit are proposed to manage more generic disturbances. For the generalized prediction process, both the instantaneous and cumulative sum of each FIVs
are normalized, and a suitable value is set as the critical limit to predict the fall over.
To determine the best combination and the usefulness of multiple sensors, the prediction performance is evaluated on four different types of terrains, in three unique combinations:
first, each feature individually with their respective FIVs; second, an intuitive performance based (PF); and finally, Kalman filter based (KF) techniques, which involve the usage
of multiple features. For PF and KF techniques, prediction performance evaluations are carried out with and without adding noise. Overall, it is reported that KF performs better
than PF and individual sensor features under different conditions. Also, the method\u2019s ability to predict fall overs during the robot\u2019s simple dynamic motion is also tested and
verified through simulations. Experimental verification of the proposed prediction method on flat and uneven terrains was carried out with the WALK-MAN humanoid robot.
Finally, in reference to the second sub-issue, i.e., the controlled fall, we propose two novel fall control techniques based on energy concepts, which can be applied online to mitigate
the impact forces incurred during the falling over of humanoids. Both the techniques are inspired by the break-fall motions, in particular, Ukemi motion practiced by martial arts
people. The first technique reduces the total energy using a nonlinear control tool, called energy shaping (ES) and further distributes the reduced energy over multiple contacts by
means of energy distribution polygons (EDP). We also include an effective orientation control to safeguard the end-effectors in the event of ground impacts. The performance of
the proposed method is numerically evaluated by dynamic simulations under the sudden falling over scenario of the humanoid robot for both lateral and sagittal falls. The effectiveness of the proposed ES and EDP concepts are verified by diverse comparative simulations regarding total energy, distribution, and impact forces.
Following the first technique, we proposed another controller to generate an online rolling over motion based on the hypothesis that multi-contact motions can reduce the impact
forces even further. To generate efficient rolling motion, critical parameters are defined by the insights drawn from a study on rolling, which are contact positions and attack
angles. In addition, energy-injection velocity is proposed as an auxiliary rolling parameter to ensure sequential multiple contacts in rolling. An online rolling controller is synthesized
to compute the optimal values of the rolling parameters. The first two parameters are to construct a polyhedron, by selecting suitable contacts around the humanoid\u2019s body. This
polyhedron distributes the energy gradually across multiple contacts, thus called energy distribution polyhedron. The last parameter is to inject some additional energy into the
system during the fall, to overcome energy drought and tip over successive contacts. The proposed controller, incorporated with energy injection, minimization, and distribution
techniques result in a rolling like motion and significantly reduces the impact forces, and it is verified in numerical experiments with a segmented planar robot and a full humanoid
model
A Benchmarking of DCM Based Architectures for Position and Velocity Controlled Walking of Humanoid Robots
This paper contributes towards the development and comparison of
Divergent-Component-of-Motion (DCM) based control architectures for humanoid
robot locomotion. More precisely, we present and compare several DCM based
implementations of a three layer control architecture. From top to bottom,
these three layers are here called: trajectory optimization, simplified model
control, and whole-body QP control. All layers use the DCM concept to generate
references for the layer below. For the simplified model control layer, we
present and compare both instantaneous and Receding Horizon Control
controllers. For the whole-body QP control layer, we present and compare
controllers for position and velocity control robots. Experimental results are
carried out on the one-meter tall iCub humanoid robot. We show which
implementation of the above control architecture allows the robot to achieve a
walking velocity of 0.41 meters per second.Comment: Submitted to Humanoids201
Independent Motion Detection with Event-driven Cameras
Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for robots. Visual tracking, for example, is easily achieved
even for very fast stimuli, as only moving objects cause brightness changes.
However, cameras mounted on a moving robot are typically non-stationary and the
same tracking problem becomes confounded by background clutter events due to
the robot ego-motion. In this paper, we propose a method for segmenting the
motion of an independently moving object for event-driven cameras. Our method
detects and tracks corners in the event stream and learns the statistics of
their motion as a function of the robot's joint velocities when no
independently moving objects are present. During robot operation, independently
moving objects are identified by discrepancies between the predicted corner
velocities from ego-motion and the measured corner velocities. We validate the
algorithm on data collected from the neuromorphic iCub robot. We achieve a
precision of ~ 90 % and show that the method is robust to changes in speed of
both the head and the target.Comment: 7 pages, 6 figure
Tahap penguasaan, sikap dan minat pelajar Kolej Kemahiran Tinggi MARA terhadap mata pelajaran Bahasa Inggeris
Kajian ini dilakukan untuk mengenal pasti tahap penguasaan, sikap dan minat pelajar
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dijalankan ini berbentuk deskriptif atau lebih dikenali sebagai kaedah tinjauan. Seramai
325 orang pelajar Diploma in Construction Technology dari Kolej Kemahiran Tinggi
Mara di daerah Batu Pahat telah dipilih sebagai sampel dalam kajian ini. Data yang
diperoleh melalui instrument soal selidik telah dianalisis untuk mendapatkan
pengukuran min, sisihan piawai, dan Pekali Korelasi Pearson untuk melihat hubungan
hasil dapatan data. Manakala, frekuensi dan peratusan digunakan bagi mengukur
penguasaan pelajar. Hasil dapatan kajian menunjukkan bahawa tahap penguasaan
Bahasa Inggeris pelajar adalah berada pada tahap sederhana manakala faktor utama yang
mempengaruhi penguasaan Bahasa Inggeris tersebut adalah minat diikuti oleh sikap.
Hasil dapatan menggunakan pekali Korelasi Pearson juga menunjukkan bahawa terdapat
hubungan yang signifikan antara sikap dengan penguasaan Bahasa Inggeris dan antara
minat dengan penguasaan Bahasa Inggeris. Kajian menunjukkan bahawa semakin positif
sikap dan minat pelajar terhadap pengajaran dan pembelajaran Bahasa Inggeris semakin
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diri serta meningkatkan minat mereka terhadap Bahasa Inggeris dengan lebih baik. Oleh
itu, diharap kajian ini dapat memberi panduan kepada pihak-pihak yang terlibat dalam
membuat kajian yang akan datang
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