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An optimizing path planner for ROS

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.

If you're new to TrajOpt, a great place to start is 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.

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 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

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

The car seat demo requires and external package. Clone the Motoman driver 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

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

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.

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.

TrajOpt packages use ctest because it is ROS agnostic, so to run the test call catkin test --no-deps trajopt trajopt_sco

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

cd gh_pages
sphinx-build . output

Now open gh_pages/output/index.rst and remove output directory before commiting changes.