TY  - BOOK
AU  - Jameel, Saad 
AU  - Supervisor: Dr. Khawaja Fahad Iqbal
TI  - Impact Dynamics for Humanoid Robot
U1  - 629.8 
PY  - 2024///
CY  - Islamabad : 
PB  - SMME- NUST; 
KW  - MS Robotics and Intelligent Machine Engineering
N1  - Research on biped robots focuses on replicating human behavior such as walking, jumping, and kicking. The kicking motion, in particular, poses significant challenges due to
the need for precise balance and coordination of joint movements and the optimization
of joint variables critical for effective kicking. Existing kicking techniques generally
rely on kinematic models and predictive model assumptions without incorporating the
full dynamics of the robot. Most models use keyframe-based and Inverse Kinematics
(IK)-based techniques for joint trajectories and apply feedback control methods such
as Dynamic Movement Primitives (DMP), Zero Moment Point (ZMP) control, and
reinforcement learning-based control for stability and walking motion. These methods
can produce a kicking motion but do not account for the kicking dynamics. Moreover, these techniques are limited to fully actuated robots. This thesis introduces a
dynamically inspired, underactuated biped robot operating in a sagittal plane capable
of walking and kicking. The model’s dynamics are derived using the Euler-Lagrange
method and controlled through a Hybrid Zero Dynamics (HZD)-based Input-Output
Linearization (IOL) strategy to achieve precise trajectory tracking. These trajectories
are parameterized by the underactuated joint and optimized via Sequential Quadratic
Programming (SQP), ensuring that torque remains within permissible limits. This
approach incorporates impact dynamics to maintain stability during the walking and
kicking phases. The model’s effectiveness is validated using the NAO robot platform in
a 3D physics simulator. Our results demonstrate that the robot executes kicks faster,
with an average kicking time of 0.75 seconds, and achieves long-range kicks, with an
average kicking distance of approximately 6.1 meters. These capabilities surpass the
performance of the current state-of-the-art Q-learning-based kicking engines
UR  - http://10.250.8.41:8080/xmlui/handle/123456789/48106
ER  -