edwinyi / motion_planning

Robot path planning, mapping and exploration algorithms

Motion Planning

Computational Motion Planning course from Penn. Matlab implementation of the tasks can be found in matlab_src folder. Each of the subfolder includes run.m script for simulation launching and subfunctions. In order to launch the algorithms simply execute:

run.m

in your Matlab command line.

Python code that is aimed to apply path planning algorithms for real mobile robots is located in pthon_src folder. In order to get familiar with the Artificial Potential Filds (APF) algorithm:

jupyter-notebook GradientBasedPlanning.ipynb

• Real time potential fields-based obstacle avoidance method for robots formations with moving or static obstacles.

python python_src/adaptive_formation/gradient_interactive.py

• Road map and path construction with Rapidly exploring Random Tree (RRT) algorithm:

python python_src/rrts/main_rrt2D.py

in 3D environment:

python python_src/rrts/3D/rrt3D.py

• Multi-layered planner for formation of robots navigation based on RRT+APF algorithms. Take a look on the package for more details:

python python_src/layered_planner/main_rrt_gradient.py

• Exploration of the environment with inknown obstacles location. Random walk algorithm implementation for a mobile robot equipped with 4 ranger sensors (front, back, left and right) for obstacles detection.

python python_src/exploration/random_goals_following/main.py

python python_src/exploration/random_walk/main.py

• Coverage path planning for unknown map exploration. Robot's kinematics is taken into account in velocity motion model.

python3 python_src/exploration/coverage_path_planning/main.py

• Mapping of the unknown environment using one or swarm of robots equipped with 4 ranger sensors. Localization data and scans are given in csv-files.