wwtao1111

wwtao1111's Stars

• rookie7777/Path-planning

  Lane-changing trajectory planning of the autonomous vehicle based on the quintic polynomial model

  Language:MATLAB413

• libai2020/Single_Vehicle_Parking_Trajectory_Planning

  第二章. 混合A*做路径决策，S-T图搜索做速度决策，数值求解最优控制问题做轨迹优化

  Language:MATLAB3314

• Logan-Shi/UAV-motion-control

  MATLAB implementation of UAV (unmanned aerial vehicle) control simulation, with RRT (rapidly exploring random tree) for path planning, B-Spline for trajectory generation and LP (linear programming) for trajectory optimization.

  Language:MATLAB1029

• libai1943/AGV_Motion_Planning_with_Moving_Obstacles

  Real-Time Trajectory Planning for AGV in the Presence of Moving Obstacles: A First-Search-Then-Optimization Approach

  Language:MATLAB7216

• icsl-Jeon/traj_gen-matlab

  Optimal trajectory generation

  Language:MATLAB5517

• yrlu/quadrotor

  Quadrotor control, path planning and trajectory optimization

  Language:MATLAB848277

• zhm-real/MotionPlanning

  Motion planning algorithms commonly used on autonomous vehicles. (path planning + path tracking)

  Language:Python2.1k565

• Anand-Patel-95/Self-Driving-Car-RRT-path-planning

  Rapidly-exploring random tree algorithm for path planning an autonomous car, with vehicle dynamics, around static obstacles.

  Language:MATLAB11

• alessioverdone/Safe-and-Minimum-Energy-Trajectory-Planning-using-Flatness

  Matlab implementation of a safe and minimum energy trajectory planning for quadrotor unmanned aerial vehicles, testing a single UAV in environment with the presence of obstacles

  Language:MATLAB114

• SHITIANYU-hue/Efficient-motion-planning

  To guarantee safe and efficient driving for automated vehicles in complicated traffic conditions, the motion planning module of automated vehicles are expected to generate collision-free driving policies as soon as possible in varying traffic environment. However, there always exist a tradeoff between efficiency and accuracy for the motion planning algorithms. Besides, most motion planning methods cannot find the desired trajectory under extreme scenarios (e.g., lane change in crowded traffic scenarios). This study proposed an efficient motion planning strategy for automated lane change based on Mixed-Integer Quadratic Optimization (MIQP) and Neural Networks. We modeled the lane change task as a mixed-integer quadratic optimization problem with logical constraints, which allows the planning module to generate feasible, safe and comfortable driving actions for lane changing process. Then, a hierarchical machine learning structure that consists of SVM-based classification layer and NN-based action learning layer is established to generate desired driving policies that can make online, fast and generalized motion planning. Our model is validated in crowded lane change scenarios through numerical simulations and results indicate that our model can provide optimal and efficient motion planning for automated vehicles

  Language:MATLAB6412

• ai-winter/matlab_motion_planning

  Motion planning and Navigation of AGV/AMR：matlab implementation of Dijkstra, A*, Theta*, JPS, D*, LPA*, D* Lite, RRT, RRT*, RRT-Connect, Informed RRT*, ACO, Voronoi, PID, LQR, MPC, APF, RPP, DWA, DDPG, Bezier, B-spline, Dubins, Reeds-Shepp etc.

  Language:MATLAB28451

• Kayne0401/Robust-Decision-Making-Framework

  11

• MohamadSayegh/MPC_Quadrotor

  Model Predictive Control for a quadrotor in static and dynamic environments

  Language:Python5613

• DayuanTan/OpenSourceAcademicResearch

  Our open-source academic research files, codes, data, and so on.

  31