Software tools

There are 12 software tools found.


Adaptive Robot Control using Optimization
ARC-OPT is a collection of tools for optimization-based control of robotic systems. It facilitates intuitive specification, execution and optimization of reactive robot tasks that involve multiple simultaneously running subtasks. ARC-OPT includes different robot models and solvers that allow implementation of hierarchical, weighted or hybrid control schemes on complex robots with many degrees of freedom, such as humanoids.
Optimization-based Control, Whole-Body Control, Humanoid Robotics


Biologically inspired Graph-Based Language
Bagel is a graph-based programming language where nodes define algorithms that transfer input values to output ports and edges define the data flow within the nodes. Additionally, a Bagel graph defines global inputs and outputs that allow to use the whole graph on a higher level as a single Bagel node. Thus, the interface of core algorithms is the same as the interface of a composition of core algorithms and the same graphs are used to define new algorithms and component networks. This and with the possibility to merge multiple edges on one input via several merge functions allow an efficient modeling of hierarchical behavior-based architectures.
Control, Behavior, Robot programming


Behavior Optimization and Learning for Robots
BOLeRo provides tools to learn behaviors for robots. That includes behavior representations as well as reinforcement learning, black-box optimization, and evolutionary algorithms and imitation learning. It provides a C++ and a Python interface to be efficient where this is required and to be flexible and convenient where performance is not an issue. Because the library provides a C++ interface, it is easy to integrate in most robotic frameworks, e.g. the robot operating system (ROS) or the robot construction kit (Rock).
Machine Learning, Evolutionary Computation, Behavior Learning, Optimization


Computer Aided Design To Simulation
The purpose of the program CAD-2-SIM is facilitating the transfer of mechanism specifications from computer aided design programs to simulation software. In particular, the program allows to export the numeric data that describe the kinematic and the dynamic properties of a mechanism without manual intervention from the CAD program to description formats of several simulation environments (Openrave, Mars, RBDL, ROS, ROCK, SimMechanics). By this direct data transformation, a faster and more stable development process is enabled. The program CAD-2-SIM uses the Sheth-Uicker convention and a graph-based enumeration scheme.
Kinematic-Dynamic Modeling, Mechanical Synthesis, Computer Aided Design


Hybrid Robot Dynamics
Hybrid Robot Dynamics (HyRoDyn) is a modular software workbench written in C++ for solving the kinematics and dynamics of highly complex series-parallel hybrid robots which are becoming increasingly popular in recent years. They inherit the advantages and kinematic complexity of both architectures and hence require careful treatment in their analysis and control. Unlike conventional methods that rely on implicit constraint resolution for loop closure, HyRoDyn adopts a holistic approach towards dealing with the complexity of these systems. It re-utilizes the closed form solution to loop closure constraints of parallel mechanisms commonly used in the design of series-parallel hybrid robots. Kinematicians are welcome to contribute to the submechanism libraries in HyRoDyn so that the catalog of supported parallel mechanisms can be enriched. Moreover, for flexibility without compromising efficiency, the software can accommodate any parallel mechanisms through a numerical approach in explicit form. This software tool serves as an efficient and error-free alternative to the already existing generic multi-body kinematics and dynamics software.
series-parallel hybrid robots


Machina Arte Robotum Simulans
MARS is a cross-platform simulation and visualisation tool created for robotics research. It consists of a core framework containing all main simulation components, a GUI (based on Qt), 3D visualization (using OSG) and a physics engine (based on ODE). MARS is designed in a modular manner and can be used very flexibly, e.g. by running the physics simulation without visualization and GUI. It is possible to extend MARS writing your own plugins and many plugins introducing various functionality such as HUDs or custom ground reaction forces already exist - and it's easy to write your own.
Simulation, Visualisierung


Maja Machine Learning Framework
The Maja Machine Learning Framework (MMLF) is a general framework for problems in the domain of Reinforcement Learning (RL) written in python. It provides a set of RL related algorithms and a set of benchmark domains. Furthermore it is easily extensible and allows to automate benchmarking of different agents.
Reinforcement learning, Machine learning, Evolutionary algorithms


Node Level Data Link Communication
Modern robotic systems come with a variety of decentralized sensor- and controlelektronics which frequently need to communicate with one another. The NDLCom protocol allows to exchange small packets of data between microcontrollers, FPGAs and a computer. Each participant is connected to one of its neighbours via a point-to-point connection and has to forward incoming messages according to the receiver address in the packet header. Implementations to receive, forward and decode messages exist in C/C++ and VHDL. The visualization, logging and exporting of received data is accomplished in a graphical user interfaces.
Serial communication, OSI-Layer, Embedded, C, VHDL, Qt, CSV-Export


An add-on for Blender allowing editing and exporting of robots for the MARS simulation
Creating adequate simulation models of a robot is a difficult task that especially in the world of open source and research oftentimes comes down to editing complex custom data files by hand. Phobos is an open-source Add-On for Blender designed to simplify this task, allowing the user to create robot models in a visual, interactive user interface and supporting their export as URDF files as well as SMURF robot descriptions for use with the MARS simulation.
Simulation, robot model, modelling


Signal Processing and Classification Environment written in Python
pySPACE is a modular software for processing segmented time series and feature vector data. It has been specifically designed to enable distributed execution and empirical evaluation of manifold signal processing chains and can be used for benchmarking and online applications. It automatically loads, processes, and stores different datasets. Signal processing algorithms (nodes) and larger transformations of several datasets (operations) can be easily concatenated. The automatic processing can be done in parallel on a multicore system or cluster. pySPACE provides a growing number of algorithms and is actively maintained and developed.
machine learning, signal processing, parallelisation


Reconfigurable Signal Processing and Classification Environment
The framework reSPACE (reconfigurable Signal Processing and Classification Environment) was developed to facilitate the development of application-specific FPGA-based hardware accelerators for embedded and mobile systems. respace uses a model based development process to accelerate the development of the accelerators. The focus lies especially on applications in the areas signal and image processing as well as machine learning.
Machine Learning, Signal Processing, Parallelization, FPGA, Embedded Systems


Robot Construction Kit
Rock is a software framework for the development of robotic systems. The underlying component model is based on the Orocos RTT (Real Time Toolkit). Rock provides all the tools required to set up and run high-performance and reliable robotic systems for wide variety of applications in research and industry. It contains a rich collection of ready to use drivers and modules for use in your own system, and can easily be extended by adding new components.
Robots, Framework, Components, Modular, Drivers, Real-time
last updated 16.11.2023
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