Among the available solutions for drone swarm simulations, we identified a
gap in simulation frameworks that allow easy algorithms prototyping, tuning,
debugging and performance analysis, and do not require the user to interface
with multiple programming languages. We present SwarmLab, a software entirely
written in Matlab, that aims at the creation of standardized processes and
metrics to quantify the performance and robustness of swarm algorithms, and in
particular, it focuses on drones. We showcase the functionalities of SwarmLab
by comparing two state-of-the-art algorithms for the navigation of aerial
swarms in cluttered environments, Olfati-Saber's and Vasarhelyi's. We analyze
the variability of the inter-agent distances and agents' speeds during flight.
We also study some of the performance metrics presented, i.e. order, inter and
extra-agent safety, union, and connectivity. While Olfati-Saber's approach
results in a faster crossing of the obstacle field, Vasarhelyi's approach
allows the agents to fly smoother trajectories, without oscillations. We
believe that SwarmLab is relevant for both the biological and robotics research
communities, and for education, since it allows fast algorithm development, the
automatic collection of simulated data, the systematic analysis of swarming
behaviors with performance metrics inherited from the state of the art.Comment: Accepted to the 2020 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS