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minimize snap trajectory generation impl via close-form solution and OSQP iteration solution

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Minimizer Snap Trajectory Generation 😋

We have implemented two methods for solving the Minimizer Snap Trajectory Generation problem, including a close-form solution and an optimized solution based on OSQP. The result is as follows

Build And Run Example 🏃

the following package is need to install

then clone and build the project

git clone https://github.com/weihaoysgs/minimize-snap-traj-optimize.git
cd minimize-snap-traj-optimize
mkdir build
cmake ..
make -j12

run the example

./minimizer_snap_traj_generation

you will get the following result, which is a matrix with k rows and 3*poly_paramter_num cols, the k is the trajectory segment number and the 3 meaning that the trajectory is in 3D space, poly_parameter_num is polynomial variable number. And the trajectory will also visualization via matplotlib.

 0          2.78854e-15 -9.90396e-16   3.3374e-16    0.0357055......
 1.20292    0.00672824    -0.279682   -0.0240655     0.016702......
-0.692698    -0.821328     0.128629    0.0548062  -0.00938771......
-0.979247     0.413536   0.00903509    -0.017289   0.00635208......
 0.366166     0.562193    0.0797064   0.00654287  -0.00444768......
 3.47916     0.411673    -0.133411 -0.000115826   0.00501725......

you can easy update the waypoint to generate deifferent smooth trajectory in the .cpp file

Eigen::Matrix<double, 7, 3> waypoints;
// clang-format off
waypoints <<         0,         0,      0,
                1.20292,   3.05196,   1.32,
                -0.692698,   2.74029,   0.88,
                -0.979247, -0.258448,   1.46,
                0.366166,  -1.73207,      2,
                3.47916,  -1.59115,   3.62,
                3.78978,  0.978379,   0.56;
// clang-format on

Acknowledgments 😻

Our implementation is based on professor Fei Gao’s code and theoretical explanation. Thanks to Teacher Gao and Fast-Lab for their open source contributions in motion planning field.

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