  In this paper, we address the problem of safe trajectory planning for
autonomous search and exploration in constrained, cluttered environments.
Guaranteeing safe (collision-free) trajectories is a challenging problem that
has garnered significant due to its importance in the successful utilization of
robots in search and exploration tasks. This work contributes a method that
generates guaranteed safety-critical search trajectories in a cluttered
environment. Our approach integrates safety-critical constraints using discrete
control barrier functions (DCBFs) with ergodic trajectory optimization to
enable safe exploration. Ergodic trajectory optimization plans continuous
exploratory trajectories that guarantee complete coverage of a space. We
demonstrate through simulated and experimental results on a drone that our
approach is able to generate trajectories that enable safe and effective
exploration. Furthermore, we show the efficacy of our approach for safe
exploration using real-world single- and multi- drone platforms.
