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https://github.com/ros-industrial-consortium/trajopt_ros
Trajectory Optimization Motion Planner for ROS
https://github.com/ros-industrial-consortium/trajopt_ros
Last synced: 2 months ago
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Trajectory Optimization Motion Planner for ROS
- Host: GitHub
- URL: https://github.com/ros-industrial-consortium/trajopt_ros
- Owner: tesseract-robotics
- Created: 2018-06-06T13:18:04.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2024-04-30T13:07:54.000Z (6 months ago)
- Last Synced: 2024-05-18T12:05:30.514Z (6 months ago)
- Language: C++
- Size: 94.4 MB
- Stars: 356
- Watchers: 22
- Forks: 100
- Open Issues: 37
-
Metadata Files:
- Readme: README.md
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README
# trajopt_ros
Platform | CI Status
---------------------|:---------
Linux (Focal) | [](https://github.com/ros-industrial-consortium/trajopt_ros/actions)
Linux (Bionic) | [](https://github.com/ros-industrial-consortium/trajopt_ros/actions)
Windows | [](https://github.com/ros-industrial-consortium/trajopt_ros/actions)
Lint (Clang-Format) | [](https://github.com/ros-industrial-consortium/trajopt_ros/actions)
[](http://github.com/ros-industrial-consortium/trajopt_ros/issues)
[](https://opensource.org/licenses/Apache-2.0)
[](https://opensource.org/licenses/BSD-2-Clause)
[](http://rosindustrial.org/news/2016/10/7/better-supporting-a-growing-ros-industrial-software-platform)
An optimizing path planner for ROS
## Solvers support
`trajopt_ros` implements sequential convex optimization to solve the motion planning problem.
It implements a penalty method to optimize for joint velocities while satisfying a set of constraints.
Internally, it makes use of convex solvers that are able to solve linearly constrained quadratic problems.
At the moment, the following solvers are supported:
- `BPMPD` (interior point method, free for non-commercial use only)
- `Gurobi` (simplex and interior point/parallel barrier, license required)
- `OSQP` (ADMM, BSD2 license)
- `qpOASES` (active set, LGPL 2.1 license)
While the `BPMPD` library is bundled in the distribution, `Gurobi`, `OSQP` and `qpOASES` need to be installed in the system.
To compile with `Gurobi` support, a `GUROBI_HOME` variable needs to be defined.
Once `trajopt_ros` is compiled with support for a specific solver, you can select it by properly setting the `TRAJOPT_CONVEX_SOLVER` environment variable. Possible values are `GUROBI`, `BPMPD`, `OSQP`, `QPOASES`, `AUTO_SOLVER`.
The selection to `AUTO_SOLVER` is the default and automatically picks the best between the available solvers.
## TrajOpt Examples
If you're new to TrajOpt, a great place to start is [tesseract_ros_examples](https://github.com/ros-industrial-consortium/tesseract_ros/tree/master/tesseract_ros_examples). This contains a number of examples to get you started.
This package is no longer part of this repository, but is maintained over at `ros-industrial-consortium/tesseract_ros`.
Additionally, there is an industrial training module that covers TrajOpt for a pick and place application. That module can be found [HERE](https://industrial-training-master.readthedocs.io/en/melodic/_source/demo3/index.html).
#### Pick and Place
The pick and place example is great place to start because it shows a complete end to end process using TrajOpt. While the code itself is quite long, this is because it is showing setting up and solving 2 problems (the pick and the place) as well as attaching and detaching objects in Tesseract. It makes use of the Tesseract TrajOpt Planner which simplifies some of the problem setup.
```roslaunch tesseract_ros_examples pick_and_place_example.launch```
### Basic Cartesian
Basic Cartesian shows how to use TrajOpt directly. It also shows doing collision checking against an octomap generated from a point cloud
```roslaunch tesseract_ros_examples basic_cartesian_example.launch```
### Glass Upright
This example shows a robot avoiding a collision while keeping its end effector orientation upright. It demonstrates how competing costs and constraints can be combined to achieve the desired results
```roslaunch tesseract_ros_examples glass_upright_example.launch```
### Car Seat Demo
The car seat demo requires and external package. Clone the [Motoman driver](https://github.com/ros-industrial/motoman) into your workspace to use it. While it is quite complex, it shows the power of TrajOpt to plan using external axes and redundancy to solve complex manipulation tasks.
```roslaunch tesseract_ros_examples car_seat_example.launch```
### Puzzle Piece Demos
The puzzle piece examples show a small collaborative robot manipulaing a puzzle piece to debur the edges with either a fixed grinder or one with extra axes.
```roslaunch tesseract_ros_examples puzzle_piece_example.launch```
```roslaunch tesseract_ros_examples puzzle_piece_auxillary_axes_example.launch```
 
## Building with Clang-Tidy Enabled
Must pass the -DTRAJOPT_ENABLE_CLANG_TIDY=ON to cmake when building. This is automatically enabled if cmake argument -DTRAJOPT_ENABLE_CLANG_TIDY=ON is passed.
## Building TrajOpt Tests
Must pass the -DTRAJOPT_ENABLE_TESTING=ON to cmake when wanting to build tests. This is automatically enabled if cmake argument -DTRAJOPT_ENABLE_TESTING_ALL=ON is passed.
.. NOTE: If you are building using catkin tools, use `catkin build --force-cmake -DTRAJOPT_ENABLE_TESTING=ON`.
## Running TrajOpt Tests
TrajOpt packages use ctest because it is ROS agnostic, so to run the test call `catkin test --no-deps trajopt trajopt_sco`
## TrajOpt Benchmarks
TrajOpt's Google benchmarks can be built by building with the flag `DTRAJOPT_ENABLE_BENCHMARKING=ON`.
To run the benchmarks at compile time and save the results to a json file in the build directory, add the flag `-DTRAJOPT_ENABLE_RUN_BENCHMARKING=ON`
## Build Branch Sphinx Documentation
```
cd gh_pages
sphinx-build . output
```
Now open gh_pages/output/index.rst and remove *output* directory before commiting changes.