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Below you can find information regarding software packages that our research group has released or can provide upon request:

Stable Sparse RRT (SST): Efficient Asymptotically Near-Optimal Kinodynamic Motion Planning

We have developed the Stable Sparse-RRT (SST) algorithm, which provides favorable properties for systems where a steering function (BVP solver) may not be available, while being highly efficient. It can be shown that SST is asymptotically near-optimal and with a small modification (SST*) it can also provide asymptotic optimality for kinodynamic problems under some mild assumptions. In addition, SST maintains a sparse data structure, where the number of nodes stored in their trees is significantly lower than RRT providing computational benefits. More details and a link to the codebase can be found under the SST webpage.

Rutgers APC RGB-D Dataset

To better equip the research community in evaluating and improving robotic perception solutions for warehouse picking challenges, the PRACSYS lab at Rutgers University provides a new rich RGB-D data set for warehouse picking and software for utilizing it. The dataset contains 10,368 depth and RGB registered images, complete with hand-annotated 6DOF poses for 24 of the Amazon Picking Challenge (APC) objects (mead_index_cards excluded). Also provided are 3D mesh models of the 25 APC objects, which may be used for training of recognition algorithms.

Multi-agent Path Finding: Push and Swap code

Our efforts in the area of Complete and Tractable Multi-Robot Path Planning started in 2011 with an algorithm called Push and Swap, which has polynomial running time and is complete but suboptimal for the multi-agent path finding problem. The algorithm has attracted attention and we received multiple requests for the source code. Since 2011 Ryan Luna has continued working on optimizing this implementation, in collaboration with Athanasios Krontiris and Kostas Bekris.

Download the latest version of the original Push and Swap algorithm.

The group has also worked on showing the relation between such efficient and complete but suboptimal planners and algorithmic contributions on the pebble motion problem. We will soon release the code that provides implementations for multiple variations of these methods.

The available code for Push and Swap above provides sequential solutions, i.e., where only one agent moves at a time. There is also work on a parallel version of Push and Swap. The implementation is not as optimized yet but we can provide it upon request.

Sparse Representations for Motion Planning: SPARS and SPARS 2 available on OMPL

Asymptotically Near-Optimal Roadmap-Based Planners SPARS and SPARS2 have been released as part of the Open Motion Planning Library maintained by the Physical and Biological Computing Group at Rice University. The benefit of these methods is that they correspond to sparse motion planning data structures that can be queried and communicated efficiently, while at the same time they are also able to provide guarantees regarding the quality of the resulting paths.

Download the latest version of OMPL.

The code is an implementation provided by Andrew Dobson. You can find SPARS and SPARS 2 under the list of supported planners by OMPL.

PRACSYS software: An Extensible Architecture for Composing Motion Controllers, Motion Planners and Task Planners

Our research group is working on a common software package that has been utilized for a variety of research projects over the last few years. Our objective is to provide an extensible software architecture so that the integration of low-level motion controllers with motion planners and higher-level task planers is streamlined.

An earlier version of the code is available through a Sourceforge repository, which corresponds to our paper at SIMPAR 2012.

We have since significantly revised and extended the functionality of our software package. Our current main focus is on the definition of proper interfaces for composing motion and task planners. Feel free to contact us if you are interested in the latest version and we will share a Mercurial repository.

Examples of publications that have utilized this software platform include the following: