Safe navigation and path planning for multiagent systems with control barrier functions
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Finding safe trajectories for multiagent autonomous systems can be difficult, especially as multiple robots and obstacles are added to the system. Control barrier functions (CBFs) have been effective in addressing this problem. Although the use of CBFs for guaranteeing safe operation is well established, there is no standard software implementation to simplify the integration of these techniques into robotic systems. We present a CBF Toolbox to fill this void. Although the CBF Toolbox can be used to ensure safety based on local control decisions, it may not be sufficient to guide a robots to their goals in certain environments. In these cases, path planning algorithms are required. We present one such algorithm, which is the multiagent extension of the CBF guided rapidly-exploring random trees (CBF-RRT) to demonstrate how the CBF Toolbox can be applied. This work addresses the theory behind the CBF Toolbox, as well as presenting examples of how it is applied to multiagent systems. Examples are shown for its use in both simulation and hardware experiments. Details are provided on CBF guided rapidly-exploring random trees (CBF-RRT), and its application to multiagent systems with multiagent CBF-RRT (MA-CBF-RRT) that streamlines safe path planning for teams of robots.