MPCC | Model Predictive Contouring Controller

by alexliniger C++ Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | MPCC Summary

MPCC is a C++ library typically used in Simulation applications. MPCC has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

Simulation environments in C++ and Matlab of the Model Predictive Contouring Controller (MPCC) for Autonomous Racing developed by the Automatic Control Lab (IfA) at ETH Zurich.

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Quality

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Support

MPCC has a medium active ecosystem.

It has 940 star(s) with 317 fork(s). There are 32 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 58 have been closed. On average issues are closed in 44 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of MPCC is current.

Quality

MPCC has no bugs reported.

Security

MPCC has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

MPCC is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

MPCC releases are not available. You will need to build from source code and install.

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MPCC Key Features

No Key Features are available at this moment for MPCC.

MPCC Examples and Code Snippets

No Code Snippets are available at this moment for MPCC.

Community Discussions

Trending Discussions on MPCC

Error when installing mpi4py on Ubuntu instance

MySQL Optimization: Multiple Modes (Most Common Value) in a Single Query

improve XSLT 3.0 performance to avoid expensive preceding selection

Excel output in Panda - TypeError: unhashable type: 'list'

pyMPI build fails: relocation R_X86_64_32 error - recompile with -fPIC

QUESTION

Error when installing mpi4py on Ubuntu instance

Asked 2019-Oct-12 at 15:06

I'm trying to install mpi4py on my Ubuntu 18.04 instance. I have python3.6.5 installed.

First I try sudo apt-get build-dep python-mpi4py and I get:

Reading package lists... Done E: You must put some 'source' URIs in your sources.list

I then try to install simply with pip install mpi4py. I get a long error message and I include some of the most important parts below:

...

ANSWER

Answered 2019-Oct-12 at 15:06

Simply run:

conda install -c anaconda mpi4py as shown here.

Also, a helpful comment by Lidandro Dalcin:

I guess you are missing the mpich-mpicc package, this one is a metapackage that depends in the right compiler. Why are you installing mpi4py with pip? Maybe all you need is to conda install mpi4py (available in the conda-forge channel) ?

Source https://stackoverflow.com/questions/58355271

QUESTION

MySQL Optimization: Multiple Modes (Most Common Value) in a Single Query

Asked 2019-Apr-09 at 19:34

A feature of my real estate website allows users to subscribe to a particular market and receive regular updates (called "market analyses") by email. The analyses require a few values to be calculated as a mode (most common value). Upon doing some research, I learned that MySQL does not have a MODE() function particularly because there might be multiple modes and there might be no mode whatsoever, but also because you cannot even get a single mode without having at least two values in the column.

Which leads me to this query

...

ANSWER

Answered 2019-Apr-09 at 19:34

One improvement is to cut out half the subqueries:

Source https://stackoverflow.com/questions/55246507

QUESTION

improve XSLT 3.0 performance to avoid expensive preceding selection

Asked 2018-Nov-27 at 15:36

I have a input structure like below,

...

ANSWER

Answered 2018-Nov-26 at 18:21

Try whether an accumulator improves performance:

Source https://stackoverflow.com/questions/53484319

QUESTION

Excel output in Panda - TypeError: unhashable type: 'list'

Asked 2018-Apr-10 at 11:04

I am trying to troubleshoot this error message in the code I am in the middle of creating. When I run it and print(df) it gives me the output that I should have, however when I try to export to .xlsx it's giving me this error:

Traceback (most recent call last): File "C:/Attr_Editor_Excel/Extract_xlsx.py", line 38, in

df.to_excel(writer, sheet_name= sheet_name, startrow=0, startcol=0, index=False, header=False)

File "C:\Python36\lib\site-packages\pandas\core\frame.py", line 1424, in to_excel engine=engine)

File "C:\Python36\lib\site-packages\pandas\io\formats\excel.py", line 628, in write freeze_panes=freeze_panes)

File "C:\Python36\lib\site-packages\pandas\io\excel.py", line 1558, in write_cells if sheet_name in self.sheets:

TypeError: unhashable type: 'list'

I've tried for over an hour to try to troubleshoot this error, but haven't. So I'm doing my last resort at asking you guys.

...

ANSWER

Answered 2018-Apr-10 at 11:04

sheet_name = ([soup.find('leafname').text])

The fact that sheet_name is a list causes if sheet_name in self.sheets: to fail since self.sheets is a dictionary, meaning sheet_name in self.sheets is going to attempt hashing sheet_name hence the error TypeError: unhashable type: 'list'

Change sheet_name to a string:

Source https://stackoverflow.com/questions/49751944

QUESTION

pyMPI build fails: relocation R_X86_64_32 error - recompile with -fPIC

Asked 2017-Mar-11 at 08:31

I'm trying to install pyMPI for the purpose of using it with WARP package for some low resolution simulations that I can run on a regular desktop. The documentation for WARP suggests that I use a specific variant of pyMPI:

Warp has traditionally been used with pyMPI, but will also work with mpi4py. (Warp with python3 only works with mpi4py.) pyMPI can be obtained from git at NERSC. (The sourceforge version will not work with Warp.)
...

ANSWER

Answered 2017-Mar-11 at 08:31

The issue was resolved by passing the following arguments while configuring:

Source https://stackoverflow.com/questions/42243904

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install MPCC

You can download it from GitHub.

Support

Liniger, A., Zhang, X., Aeschbach, P., Georghiou, A. and Lygeros, J. Racing miniature cars: Enhancing performance using stochastic MPC and disturbance feedback. 2017 American Control Conference (ACC). Similar to the previous paper, the MPCC is augmented with probabilistic track constraints, and we show that it is beneficial to optimize over the feedback policies used in stochastic MPC, using the disturbance feedback approach. We also show that by using only 3 samples, the approach can be implemented in real-time and that even when using so few samples, it helps on the experimental platform.

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