  This paper introduces a novel control methodology designed to guide a
collective of robotic-sheep in a cluttered and unknown environment using
robotic-dogs. The dog-agents continuously scan the environment and compute a
safe trajectory to guide the sheep to their final destination. The proposed
optimization-based controller guarantees that the sheep reside within a desired
distance from the reference trajectory through the use of Control Barrier
Functions (CBF). Additional CBF constraints are employed simultaneously to
ensure inter-agent and obstacle collision avoidance. The efficacy of the
proposed approach is rigorously tested in simulation, which demonstrates the
successful herding of the robotic-sheep within complex and cluttered
environments.
