Project Proposal
(Modeling and Simulation of Walking Robots)
Sana'a University, Faculty of Engineering,
Mechatronics Engineering Department, Third year
FROM: {Emad Abd-Al Whid Saleh 201873139 } {Abdullah Sultan Alazazi
201873050 }
{ Hamdan Al-Najri 201873191} { Khaled AL-Maktary 201873108 }
TO: Eng. Ahmed Al-Oqabi
1. Abstract:
➢ Introduction to Dynamic System:
• Benefits:
Legged motion makes it possible to negotiate uneven surfaces, steps, and other areas that would be
difficult for a wheeled robot to reach, as well as causes less damage to environmental terrain as
wheeled robots, which would erode it.
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•
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It used for: inspection, payload delivery and map the terrain and obstacles around the robot
The Domains of the System: Mechanical and Electrical
Component: Battery, Actuators, links, PWM, Gear Box, PID Controllers, Mechanism
Configuration, Rigid Transforms
2. Working principle of the system:
3. Input and output of the system:
4. importance of modeling the system:
•
Safety: Robots will fall. Prototypes will break. You can verify that controls algorithms are
at a good starting point in simulation before moving to hardware. Simulation lets you test
your robot and controller design under multiple scenarios without building prototypes. In
simulation, you also get the benefit of intentionally generating unsafe conditions, as well
as discovering unexpected issues.
•
Efficiency: Physical experiments take time and effort to set up and reset between runs.
With simulation, you get a programmatic environment to automate experiments and walk
away from your desk. If your robot is controlled by an embedded system, simulation lets
you test algorithm changes without having to port and rebuild the code on hardware every
time. This separation of algorithm and implementation can also help you determine
whether new issues are due to algorithm changes or physical limitations.
•
Build from scratch: It may take some initial time to build a model from scratch. However,
if set up correctly, you can easily change properties such as dimensions, cross-sections,
masses, etc. If you are still in the conceptual design phase, this can be useful as you sweep
through different parameters and validate your design.
5. The negative feedback of the system:
An important note is that machine learning and traditional controls can (and should!) be combined. For
example, take the diagram above which
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Uses model-based control techniques to position the feet of the robot, convert that to joint angles,
and control the individual actuators to carry out this action. Safety-critical factors such as stability
would also be taken care of completely independently of what the trained agent may decide.
6. Objective
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•
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model 3D robot mechanics, incorporating physical contact and comparing joint actuator and
controller models. Moreover, building the robot model with multibody. modeling from scratch,
paramterize model using matlab variable and tuning mechanical properties as pair of design.
building the simulation environment.
Reinforcement training and Simulink tools are used for Modeling and Simulation of Walking
Robots