利用MATLAB打造無人機模擬飛行控制、路徑規劃和軌跡優化 並錄製動態軌跡圖GIF檔 是模擬飛行玩家紙上作業必備好物~

## Quadrotor Control, Path Planning and Trajectory Optimization <a href="https://youtu.be/lA2B1YDLJaY"> <img src="imgs/hover.jpg" alt="step" width="600"> </a> (Click above image for real quadrotor demos) Following [MEAM 620 Advanced Robotics](https://alliance.seas.upenn.edu/~meam620/wiki/) course at University of Pennsylvania. (For Penn students: *DO NOT* spoil the fun by looking at this repo and not working on your assignments! and most importantly, *DO NOT* violate the honor code!) This repo includes matlab code for: - Quadrotor PD controller - Path planning algorithms (Dijkstra, A*) - Trajectory optimizations (Minimum Snap/Acceleration Trajectory) Please cite this work using the following bibtex if you use the software in your publications ``` @software{Lu_yrlu_quadrotor_Quadrotor_Control_2022, author = {Lu, Yiren}, doi = {10.5281/zenodo.6796215}, month = {7}, title = {{yrlu/quadrotor: Quadrotor Control, Path Planning and Trajectory Optimization}}, url = {https://github.com/yrlu/quadrotor}, version = {1.0.0}, year = {2017} } ``` ## PD Controller - Run code: change trajectories in file `control/runsim.m` and run. - See [quadrotor_dynamics.pdf](quadrotor_dynamics.pdf) for dynamic modeling of the quadrotor. - See `control/controller.m` for implementation of the PD controller. - Visualization below. Desired (blue) vs Actual (red) #### Trajectory 1: Step <img src="gifs/p1p1_step.gif" alt="step" width="270"> <img src="imgs/p1p1_step_p.jpg" alt="step" width="270"> <img src="imgs/p1p1_step_v.jpg" alt="step" width="270"> #### Trajectory 2: Circle <img src="gifs/p1p1_circle.gif" alt="step" width="270"> <img src="imgs/p1p1_circle_p.jpg" alt="step" width="270"> <img src="imgs/p1p1_circle_v.jpg" alt="step" width="270"> #### Trajectory 2: Diamond <img src="gifs/p1p1_diamond.gif" alt="step" width="270"> <img src="imgs/p1p1_diamond_p.jpg" alt="step" width="270"> <img src="imgs/p1p1_diamond_v.jpg" alt="step" width="270"> ## Path Planning and Trajectory Optimization - Run code: `traj_planning/runsim.m` and run path 1 or path 3. - See [project_report.pdf](project_report.pdf) for more details about trajectory generation - See `traj_planning/path_planning/dijkstra.m` for implementation of path finding algorithms (dijstra, A*). - See `traj_planning/traj_opt7.m` for implementations of minimium snap trajectory. - See `traj_planning/traj_opt5.m` for implementations of minimium acceleration trajectory. - Visualization below. #### Minimum Acceleration Trajectory <img src="gifs/p1p3_map1_acc.gif" alt="step" width="270"> <img src="imgs/p1p3_map1_acc_p.jpg" alt="step" width="270"> <img src="imgs/p1p3_map1_acc_v.jpg" alt="step" width="270"> <img src="gifs/p1p3_map3_mini_acc.gif" alt="step" width="270"> <img src="imgs/p1p3_map3_mini_acc_p.jpg" alt="step" width="270"> <img src="imgs/p1p3_map3_mini_acc_v.jpg" alt="step" width="270"> #### Minimum Snap Trajectory <img src="gifs/p1p3_map1_snap.gif" alt="step" width="270"> <img src="imgs/p1p3_map1_snap_v.jpg" alt="step" width="270"> <img src="imgs/p1p3_map1_snap_v.jpg" alt="step" width="270"> (with way points constraints) <img src="gifs/p1p3_map3_snap.gif" alt="step" width="270"> <img src="imgs/p1p3_map3_snap_p.jpg" alt="step" width="270"> <img src="imgs/p1p3_map3_snap_v.jpg" alt="step" width="270">