Posts by Tags

ADMM

Informative Path Planning

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Control systems

UAV control

less than 1 minute read

Published:

Differentiable Optimization

Gaussian Process

Informative Path Planning

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Implicit Function Theorem

Information Gain

Informative Path Planning

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

KKT

MPCC

Model predictive contouring control

2 minute read

Published:

Let us consider kinematics of the racing car

$$\begin{align} x_{t+1} = f_p(x_t, u_t;\zeta_p) = \begin{bmatrix} X_t + \Delta_T v_t \cos \phi_t, \\ Y_t + \Delta_T v_t \sin \phi_t, \\ \phi_t + \Delta_T \omega_t, \\ v_t + \Delta_T \zeta_p a_t, \end{bmatrix} \end{align}$$

where \(\zeta_p \in (0,1]\), \(\Delta_T = 0.05[s]\) is the slipping coefficient which depends position of the racing car in the track (or road). Denote by \(x_t = [X_t, Y_t, \phi_t, v_t]^\top\), \(u_t = [\omega_t, a_t]^\top\) the state of the racing car at current time step \(t\).

Optimal Control

Model predictive contouring control

2 minute read

Published:

Let us consider kinematics of the racing car

$$\begin{align} x_{t+1} = f_p(x_t, u_t;\zeta_p) = \begin{bmatrix} X_t + \Delta_T v_t \cos \phi_t, \\ Y_t + \Delta_T v_t \sin \phi_t, \\ \phi_t + \Delta_T \omega_t, \\ v_t + \Delta_T \zeta_p a_t, \end{bmatrix} \end{align}$$

where \(\zeta_p \in (0,1]\), \(\Delta_T = 0.05[s]\) is the slipping coefficient which depends position of the racing car in the track (or road). Denote by \(x_t = [X_t, Y_t, \phi_t, v_t]^\top\), \(u_t = [\omega_t, a_t]^\top\) the state of the racing car at current time step \(t\).

Optimization

Model predictive contouring control

2 minute read

Published:

Let us consider kinematics of the racing car

$$\begin{align} x_{t+1} = f_p(x_t, u_t;\zeta_p) = \begin{bmatrix} X_t + \Delta_T v_t \cos \phi_t, \\ Y_t + \Delta_T v_t \sin \phi_t, \\ \phi_t + \Delta_T \omega_t, \\ v_t + \Delta_T \zeta_p a_t, \end{bmatrix} \end{align}$$

where \(\zeta_p \in (0,1]\), \(\Delta_T = 0.05[s]\) is the slipping coefficient which depends position of the racing car in the track (or road). Denote by \(x_t = [X_t, Y_t, \phi_t, v_t]^\top\), \(u_t = [\omega_t, a_t]^\top\) the state of the racing car at current time step \(t\).

Robotics

UAV

UAV control

less than 1 minute read

Published:

VICON

UAV control

less than 1 minute read

Published: