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Application of Differential Evolution in the optimization of a robotic system with several links using inverse kinematics

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gabrieljablonski/inverse-kinematics-differential-evolution

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Differential Evolution in Robotics - Inverse Kinematics

This implementation makes use of Differential Evolution for the analysis of generic robotic systems in three-dimensional space, with its links described by Denavit-Hartenberg parameters. The optimization (fitness function) takes into account the euclidean distance from the manipulator (extremity of last link) to a target point in space.

The implementation is generic enough to be able to optimize for any features of the system. For demonstration purposes, the only variable parameter is θ.

An example of a robotic system with four links is given below.

robotic system with four links

Link theta_i r_i d_i alpha_i theta_min theta_max
01 theta_1 0.00 0.03 +90° +180°
02 theta_2 0.10 0.00 +90° +180°
03 theta_3 0.10 0.00 +90° -180°
04 theta_4 0.18 0.00 +90° -90° +90°

Example solutions for random targets, using the 4-link system described above, and another one with 2 more links added, for a total of 6 links:

Building and Running

First clone the repository and cd to it with

git clone http://github.com/gabrieljablonski/robotics-differential-evolution 
cd robotics-differential-evolution

The main source file is solveRoboticSystem.go. Before running, you may want to make adjustments to the robotic system (System.AddLinks()), the target point, and the differential evolution parameters.

The program can the be built and run with

go run src/solveRoboticSystem.go [<output file>]

The optional output file parameter can be used to change the name of the output file containing the target and best agent for each generation. Make sure to run it from the same directory as the plot_link_generations.py script to be able to plot the results.

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Application of Differential Evolution in the optimization of a robotic system with several links using inverse kinematics

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