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Title: GitHub - wjccst/PythonRobotics: Python sample codes for robotics algorithms.

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Description: Python sample codes for robotics algorithms. Contribute to wjccst/PythonRobotics development by creating an account on GitHub.

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https://patch-diff.githubusercontent.com/wjccst/PythonRobotics#what-is-this
[1808.10703] PythonRobotics: a Python code collection of robotics algorithmshttps://arxiv.org/abs/1808.10703
BibTeXhttps://github.com/AtsushiSakai/PythonRoboticsPaper/blob/master/python_robotics.bib
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Introduction to Mobile Robotics: Iterative Closest Point Algorithmhttps://cs.gmu.edu/~kosecka/cs685/cs685-icp.pdf
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Optimal rough terrain trajectory generation for wheeled mobile robotshttp://journals.sagepub.com/doi/pdf/10.1177/0278364906075328
State Space Sampling of Feasible Motions for High-Performance Mobile Robot Navigation in Complex Environmentshttp://www.frc.ri.cmu.edu/~alonzo/pubs/papers/JFR_08_SS_Sampling.pdf
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Incremental Sampling-based Algorithms for Optimal Motion Planninghttps://arxiv.org/abs/1005.0416
Sampling-based Algorithms for Optimal Motion Planninghttp://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.419.5503&rep=rep1&type=pdf
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LQR-RRT*: Optimal Sampling-Based Motion Planning with Automatically Derived Extension Heuristicshttp://lis.csail.mit.edu/pubs/perez-icra12.pdf
MahanFathi/LQR-RRTstar: LQR-RRT* method is used for random motion planning of a simple pendulum in its phase plothttps://github.com/MahanFathi/LQR-RRTstar
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Local Path Planning And Motion Control For Agv In Positioninghttp://ieeexplore.ieee.org/document/637936/
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15.3.2 Reeds-Shepp Curveshttp://planning.cs.uiuc.edu/node822.html
optimal paths for a car that goes both forwards and backwardshttps://pdfs.semanticscholar.org/932e/c495b1d0018fd59dee12a0bf74434fac7af4.pdf
ghliu/pyReedsShepp: Implementation of Reeds Shepp curve.https://github.com/ghliu/pyReedsShepp
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Optimal Trajectory Generation for Dynamic Street Scenarios in a Frenet Framehttps://www.researchgate.net/profile/Moritz_Werling/publication/224156269_Optimal_Trajectory_Generation_for_Dynamic_Street_Scenarios_in_a_Frenet_Frame/links/54f749df0cf210398e9277af.pdf
Optimal trajectory generation for dynamic street scenarios in a Frenet Framehttps://www.youtube.com/watch?v=Cj6tAQe7UCY
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P. I. Corke, "Robotics, Vision and Control" | SpringerLink p102https://link.springer.com/book/10.1007/978-3-642-20144-8
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Stanley: The robot that won the DARPA grand challengehttp://robots.stanford.edu/papers/thrun.stanley05.pdf
Automatic Steering Methods for Autonomous Automobile Path Trackinghttps://www.ri.cmu.edu/pub_files/2009/2/Automatic_Steering_Methods_for_Autonomous_Automobile_Path_Tracking.pdf
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A Survey of Motion Planning and Control Techniques for Self-driving Urban Vehicleshttps://arxiv.org/abs/1604.07446
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Towards fully autonomous driving: Systems and algorithms - IEEE Conference Publicationhttp://ieeexplore.ieee.org/document/5940562/
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Real-time Model Predictive Control (MPC), ACADO, Python | Work-is-Playinghttp://grauonline.de/wordpress/?page_id=3244
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Atsushi Sakaihttps://github.com/AtsushiSakai/
Daniel Ingramhttps://github.com/daniel-s-ingram
Joe Diniushttps://github.com/jwdinius
Karan Chawlahttps://github.com/karanchawla
Antonin RAFFINhttps://github.com/araffin
Alexis Paqueshttps://github.com/AlexisTM
Ryohei Sasakihttps://github.com/rsasaki0109
Göktuğ Karakaşlıhttps://github.com/goktug97
Guillaume Jacquenothttps://github.com/Gjacquenot
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