Motion Planning - Bug Algorithms and Kinematics
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Implementation of Bug Algorithms and Basic Forwards / Inverse Kinematics Equations. Results of the implentation include:
- plots of the path performance of the different bug planning algorithms on multiple environments
- plots of the results of running forwards / inverse kinematics on a user specified 2-link manipulator
How to Run the Code
This repository uses python 3, which is managed with an anaconda environment.
Dependencies
In order to obtain all dependencies, you simply need to install anaconda for your OS, and then run from any shell with the conda command on its path:
$(REPO_DIR_LOCATION) $ conda env create -f conda/environment.yml
Running the Source Code
To run the code:
-
Activate the
condaenvironment you just created in the "Dependencies" section:$(REPO_DIR_LOCATION) $ conda activate motionPlanning1 -
Run the main python module to run both the bug algorithms and kinematics codes:
$(REPO_DIR_LOCATION) $ python main.py
Configuring the Code
Bug Algorithm Configuration
To change the scenarios for the bug algorithm code, simply modify one of the $(REPO_DIR_LOCATION)/config/bug_scenarioX.yaml files to create a new set of obstacles, or to change the location of the start / goal points.
Forwards / inverse Kinematics on a 2-link manipulator
To change the scenarios for the manipulator code, simply modify one of the $(REPO_DIR_LOCATION)/config/manipulator_scenarioX.yaml files to change the arm link lengths, the joint offset, the forward kinematics desired angle set, or the inverse kinematic desired end effector location.
Results
Bug1 Algorithm, Environment 1
Bug2 Algorithm, Environment 1
Bug1 Algorithm, Environment 2
Bug2 Algorithm, Environment 2
Forward Kinematics, Desired Angles = [pi/4, pi/2, -pi/6]
Inverse Kinematics, Desired End Effector Location = [0, 4]
