Comparison of Control Barrier Functions (CBF) and Artificial Potential Fields (APF) for controlling a drone (MATLAB).

This code implements the path planning method of DOI: 10.1109/IROS51168.2021.9636670 by Singletary et al., using control barrier functions (CBF).

Singletary, Andrew, Karl Klingebiel, Joseph Bourne, Andrew Browning, Phil Tokumaru, and Aaron Ames. "Comparative analysis of control barrier functions and artificial potential fields for obstacle avoidance." In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8129-8136. IEEE, 2021.

Authors: Francesco Bernardini, Ryan Lewis, Aaron T. Becker Last update: May 29, 2024

The main functions are:

1. ObstacleAvoidanceUsingAPF.m

2. ObstacleAvoidanceUsingCBF.m

these implement obstacle avoidance with artificial potential fields (APF) and control barrier functions (CBF), for an arbitrary number of parameters <math-renderer class="js-inline-math" style="display: inline" data-static-url="https://github.githubassets.com/static" data-run-id="ea986b8d1dfe6f5875bc83214cee91bd">$\rho_0$</math-renderer> (for APF) and <math-renderer class="js-inline-math" style="display: inline" data-static-url="https://github.githubassets.com/static" data-run-id="ea986b8d1dfe6f5875bc83214cee91bd">$\alpha$</math-renderer> (for CBF).

The functions call other two functions which compute the individual trajectories:

3. computeTrajectoryAPF.m

4. computeTrajectoryCBF.m

The auxiliary functions:

5. drawcircles.m

6. findAxisLimits.m

7. plotTrajectories.m

take care of the graphical aspects and plot the results.