This repository contains Python implementations of the Fruit Fly Optimization Algorithm (FOA) for solving the inverse kinematics of a robot. Additionally, it includes a GUI application for forward and inverse kinematics calculations for various robot models.

• FOA for Inverse Kinematics
• Robot Kinematics Application
• Usage
• Author
• References

The FOA is a nature-inspired optimization algorithm that emulates the foraging behavior of fruit flies. The algorithm is applied to find the best joint angles that will position the robot's end-effector closest to a desired position.

The basic structure of a fruit fly is defined in the FruitFly class. Each fruit fly has a position (joint angles) and a smell (distance from the desired position). The optimization process guides the fruit flies towards the best solution.

The main steps are:

1. Initialize a population of fruit flies.
2. Evaluate their smells based on their positions.
3. Guide them towards the best position found so far.
4. Repeat for a set number of iterations.

A GUI-based application is provided that helps in calculating the forward and inverse kinematics of various robot models. The application allows users to:

• Select the robot model.
• Input the number of links and joints.
• Provide dimensions and types for each link and joint.
• Compute the Degrees of Freedom (DOF) based on the provided parameters.
• Calculate forward and inverse kinematics.

The application is built using the Tkinter library.

1. To run the FOA for inverse kinematics, execute the script containing the FOA implementation.
2. To run the Robot Kinematics Application, execute the script with the Tkinter GUI application.

'python foa_script.py' 'python robot_kinematics_app.py'

Fadi Helal

This project is licensed under the BSD3 License.

• For a deeper understanding of the Fruit Fly Optimization Algorithm:

  • Nature-Inspired Optimization Algorithms (relevant book on the topic)

• Tkinter library for GUI:

  • Tkinter Documentation