  In this work, we propose a collision-free source-seeking control framework
for unicycle robots traversing an unknown cluttered environment. In this
framework, obstacle avoidance is guided by the control barrier functions (CBF)
embedded in quadratic programming and the source seeking control relies solely
on the use of on-board sensors that measure the signal strength of the source.
To tackle the mixed relative degree of the CBF, we proposed three different
CBFs, namely the zeroing control barrier functions (ZCBF), exponential control
barrier functions (ECBF), and reciprocal control barrier functions (RCBF) that
can directly be integrated with our recent gradient-ascent source-seeking
control law. We provide rigorous analysis of the three different methods and
show the efficacy of the approaches in simulations using Matlab, as well as,
using a realistic dynamic environment with moving obstacles in Gazebo/ROS.
