  Navigation tasks often cannot be defined in terms of a target, either because
global position information is unavailable or unreliable or because target
location is not explicitly known a priori. This task is then often defined
indirectly as a source seeking problem in which the autonomous agent navigates
so as to minimize the convex potential induced by a source while avoiding
obstacles. This work addresses this problem when only scalar measurements of
the potential are available, i.e., without gradient information. To do so, it
construct an artificial potential over which an exact gradient dynamics would
generate a collision-free trajectory to the target in a world with convex
obstacles. Then, leveraging extremum seeking control loops, it minimizes this
artificial potential to navigate smoothly to the source location. We prove that
the proposed solution not only finds the source, but does so while avoiding any
obstacle. Numerical results with velocity-actuated particles, simulations with
an omni-directional robot in ROS+Gazebo, and a robot-in-the-loop experiment are
used to illustrate the performance of this approach.
