The Walking Robot Equilibrium Recovery Applied on the NAO Robot

Pop, Nicolae; Vlădăreanu, Luige; Wang, H.; Ungureanu, Miorița; Migdalovici, Marcel; Vlădăreanu, Victor; Feng, Yong; Lin, Musong; Mastan, E. P.; Emary, Ibrahiem El

doi:10.1002/9781119509875.ch14

Cited by 1 publication

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“…In many robotic problems, it is desired to achieve a certain state of the robot at a given time. This will lead to a problem of tracking the reference signal when the desired state is specified as a function of time during the entire course (Pop &Vladareanu et al, 2018). If the desired state is discontinuous in time, we need to make a prediction between points, that is, a continuous transfer of the robot, from an initial state to the next state, and the robot movement must meet certain imposed restrictions.…”

Section: Introductionmentioning

confidence: 99%

Trajectory optimization for mobile robots using model predictive control

Pop

Vlădăreanu

Migdalovici

et al. 2019

PEN

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The goal of this article is trajectory generation for biped robots based on Model Predictive Control (MPC) and the receding-horizon principle. Specifically, we want to minimize the error between the desired CoM and ZMP trajectory and the actual one and the cancellation of the shock gradient of the CoM and ZMP movements. Model predictive control (MPC) consist in a finite horizon optimal control scheme which uses a prediction model to predict vehicle response and future states, thus minimizing the current error and optimizing the future trajectory within the prediction horizon. The proposed algorithm will provide a trajectory of control inputs which will optimize the system states utilizing a quadratic form cost function similar to standard linear quadratic tracking. Specific to finite horizon control, the cost is summed over the finite prediction horizon of time length, rather than over an infinite time horizon. Many techniques have been proposed, developed, and applied to solve this constrained optimization problem for the mobile robots. With our aproach we try to investigate how is the MPC framework is applicable to trajectory generation for point-to-point problems with a fixed final time and to find a set of assumptions and methods that allow for realtime solutions. Model predictive control Real time robot control Mobile robot control Constrained optimization problem Linear quadratic optimal control

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Section: Introductionmentioning

confidence: 99%