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GRASSMARLIN provides IP network situational awareness of industrial control systems (ICS) and Supervisory Control and Data Acquisition (SCADA) networks to support network security. Passively map, and visually display, an ICS/SCADA network topology while safely conducting device discovery, accounting, and reporting on these critical cyber-physical systems. A presentation on GRASSMARLIN is also available.
Sample Hiera data for the PSICK control-repo is available via the psick-hieradata module. PSICK is a Puppet control-repo itself, you can use this repository directly in a Puppet environment, and basically have a full PSICK setup, or run the psick command to generate a new Puppet control-repo based on the components you need.
AirSim is a simulator for drones (and soon other vehicles) built on Unreal Engine. It is open-source, cross platform and supports hardware-in-loop with popular flight controllers such as PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped in to any Unreal environment you want.
A homework solution for the Autonomous Mobile Systems class at Beuth Hochschule für Technik, Berlin with Prof. Dr. Volker Sommer. Runs a simulation of a VolksBot with (error-free) laser rangers. in the project directory to bootstrap a virtual machine that is preconfigured with Player/Stage. The VM will be downloaded if it doesn't already exist your local machine (which is likely if you run the command the first time).
This package provides a core interface for working with Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs). For examples, please see the Gallery. There are multiple interfaces for expressing and interacting with (PO)MDPs: When the explicit interface is used, the transition and observation probabilities are explicitly defined using api functions or tables; when the generative interface is used, only a single step simulator (e.g. (s', o, r) = G(s,a)) needs to be defined.
Package Industrial Control Systems is a library that is developed with the Modelica language and provides a set of continuous and discrete control systems. This library can be used to set up or replicate the majority of industrial controllers. The first version of the library has been presented at the 9th Modelica conference, held in Munich 3-5 September 2012. The library won the 2nd prize at the Modelica library competition award.
The DriverStation library allows you to connect and manage a robot easily by providing an abstraction layer between an application and the network comununications between the robot and the host computer. The library is written in C, allowing it to be used in many platforms and/or programming languages (using wrappers).
The QDriverStation is a cross-platform and open-source alternative to the FRC Driver Station. It allows you to operate FRC robots with the major operating systems (Windows, Mac OSX and GNU/Linux). The QDriverStation is able to operate both 2009-2014 robots and 2015-2017 robots. The actual code that operates a FRC robot is found in a separate repository, which is written in C and can be used for your own projects or change it to support more communication protocols (such as ROS).
This is my turn-in code for one of the project in partial fulfillment of the requirements for Udacity's self-driving car Nanodegree program. In this project, I have implemented a software pipeline using the model predictive control (MPC) method to drive a car around a track in a simulator. There is a 100 millisecond latency between actuation commands on top of the connection latency. Model predictive controllers rely on dynamic models of the process. The main advantage of MPC is the fact that it allows the current timeslot to be optimized, while keeping future timeslots in account. This is achieved by optimizing a finite time-horizon, but only implementing the current timeslot. MPC has the ability to anticipate future events and can take control actions accordingly.
This is an implementation of the Iterative Linear Quadratic Regulator (iLQR) for non-linear trajectory optimization based on Yuval Tassa's paper. It is compatible with both Python 2 and 3 and has built-in support for auto-differentiating both the dynamics model and the cost function using Theano.
This repository accompanies the paper Learning convex optimization control policies. It contains the source code for the examples therein as IPython notebooks. Our examples make use the Python package cvxpylayers to differentiate through convex optimization problems.
Model Predictive Control is a feedback control method to get a appropriate control input by solving optimization problem. For autonomous vehicle, control input means steering wheel and throttle(and break pedal). Assuming we know reference trajectory, we predicte next N steps' waypoints according to kinematic model(for simplicity) over some pattern, and calculate cost function for each to find most appropriate predicted trajectory. Then we use the first control input of the predicted trajectory and throw away the other trajectory. That's it. We only need to repeat this.
Serial2pcap converts serial IP data, typically collected from Industrial Control System devices, to the more commonly used Packet Capture (PCAP) format. It is designed to support multiple serial protocols and plug-ins can be developed by independent users.