  This paper presents an integrated perception and control approach to
accomplish safe autonomous navigation in unknown environments. This is achieved
by numerical optimization with constraint learning for instantaneous local
control barrier functions (IL-CBFs) and goal-driven control Lyapunov functions
(GD-CLFs). In particular, the constraints reflecting safety and task
requirements are first online learned from perceptual signals, wherein IL-CBFs
are learned to characterize potential collisions, and GD-CLFs are constructed
to reflect incrementally discovered subgoals. Then, the learned IL-CBFs are
united with GD-CLFs in the context of a quadratic programming optimization,
whose feasibility is improved by enlarging the shared control space. Numerical
simulations are conducted to reveal the effectiveness of our proposed safe
feedback control strategy that could drive the mobile robot to safely reach the
destination incrementally in an uncertain environment.
