Difference between revisions of "Stability and motion control of a balancing robot"

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TiltOne is a robot with only two wheels that can stand in vertical position following an unstable equilibrium point. The control is applied by commanding an amount of torque to the wheels, allowing the robot to mantain it's gravity center vertical aligned to the wheel axis.
 
TiltOne is a robot with only two wheels that can stand in vertical position following an unstable equilibrium point. The control is applied by commanding an amount of torque to the wheels, allowing the robot to mantain it's gravity center vertical aligned to the wheel axis.
  
The aim of the project is to implement and compare different control solutions, based on classical approach (as PID and LQR control) and Machine Learning approach (as Reinforcement Learning control policies).
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The aim of the project proposal is to implement and compare different control solutions, based on classical approach (as PID and LQR control) and Machine Learning approach (as Reinforcement Learning control policies), that allow the robot to move following a given trajectory at a given speed.
 
|tutor=AndreaBonarini; MartinoMigliavacca
 
|tutor=AndreaBonarini; MartinoMigliavacca
 
|start=2010/03/01
 
|start=2010/03/01
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|restopic=Robot development;
 
|restopic=Robot development;
 
|level=Ms
 
|level=Ms
|type=Thesis
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|type=Course; Thesis
|status=Active
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|status=Closed
 
}}
 
}}
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Home page of [[TiltOne]]

Latest revision as of 17:58, 28 April 2011

Title: Stability and motion control of a balancing robot
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Image:Proposta tiltone.png

Description: This project is focused on the control of both stability and motion of TiltOne, a balancing robot.

TiltOne is a robot with only two wheels that can stand in vertical position following an unstable equilibrium point. The control is applied by commanding an amount of torque to the wheels, allowing the robot to mantain it's gravity center vertical aligned to the wheel axis.

The aim of the project proposal is to implement and compare different control solutions, based on classical approach (as PID and LQR control) and Machine Learning approach (as Reinforcement Learning control policies), that allow the robot to move following a given trajectory at a given speed.

Tutor: AndreaBonarini (andrea.bonarini@polimi.it), MartinoMigliavacca (migliavacca@elet.polimi.it)
Start: 2010/03/01
Students: 1 - 2
CFU: 5 - 20
Research Area: Robotics
Research Topic: Robot development
Level: Ms
Type: Course, Thesis
Status: Closed

Home page of TiltOne