msc_project_template_md_version.md

title	authors	affiliations	end_date	description	cover_image	links
Sampling Based MPC for Motion Planning of Autonomous Vehicles	name url superscript John Doe https://andreumatoses.github.io/ *1 name url superscript Luzia Knödler https://andreumatoses.github.io/ 1 name url superscript Ludwig van Beethoven https://andreumatoses.github.io/ †	name superscript Equal contribution * name superscript url TU Delft 1 https://tudelft.nl name superscript Wien Opera House †	2022-01-01	This project explores the use of Model Predictive Path Integral (MPPI) control algorithm to enable autonomous robots to navigate complex environments. MPPI is a sampling-based control method that combines path planning and control into a single optimization problem. By iteratively sampling and optimizing trajectories, the algorithm generates control inputs that minimize a cost function while satisfying system dynamics and constraints. The project aims to implement and evaluate the performance of MPPI on a robot platform, showcasing its effectiveness in real-world scenarios.	/assets/images/msc_projects/msc_project_template/jackal.jpg	name icon url Paper bi-file-earmark-pdf / name url arXiv / name icon url Code bi-github / name icon url Video bi-youtube /

All the content in the front matter will be rendered automatically. In this local version, it just shows the content of the front matter as text so ignore it, just make sure you fill the front matter with all the info correctly. You can see an example here.

Summary of the Work

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Showcase 1

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Some code that explains something important. Use the highlight tag to highlight code. You can use inline code. NOTE: code blocks will not render correctly in the local template.

import torch
import torch.nn as nn

class VAE(nn.Module):
def __init__(self, input_dim, hidden_dim, latent_dim):
super(VAE, self).__init__()

self.encoder = nn.Sequential(
nn.Linear(input_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, latent_dim * 2) # Output mean and log variance
)

return reconstructed_x, mu, log_var

Some cool math equations here. You can just use math like in latex e.g $x^2$ or $\alpha = \int_{\beta}^\gamma 1 ; dt$. For inline, use $ ... $, for block, use \[ ... \].

[ \begin{aligned} \dot{x} &= \sigma(y-x) \ \dot{y} &= \rho x - y - xz \ \dot{z} &= -\beta z + xy \end{aligned} ]

Showcase 2

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Conclusions

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