  This paper proposes a LiDAR-based goal-seeking and exploration framework,
addressing the efficiency of online obstacle avoidance in unstructured
environments populated with static and moving obstacles. This framework
addresses two significant challenges associated with traditional dynamic
control barrier functions (D-CBFs): their online construction and the
diminished real-time performance caused by utilizing multiple D-CBFs. To tackle
the first challenge, the framework's perception component begins with
clustering point clouds via the DBSCAN algorithm, followed by encapsulating
these clusters with the minimum bounding ellipses (MBEs) algorithm to create
elliptical representations. By comparing the current state of MBEs with those
stored from previous moments, the differentiation between static and dynamic
obstacles is realized, and the Kalman filter is utilized to predict the
movements of the latter. Such analysis facilitates the D-CBF's online
construction for each MBE. To tackle the second challenge, we introduce buffer
zones, generating Type-II D-CBFs online for each identified obstacle. Utilizing
these buffer zones as activation areas substantially reduces the number of
D-CBFs that need to be activated. Upon entering these buffer zones, the system
prioritizes safety, autonomously navigating safe paths, and hence referred to
as the exploration mode. Exiting these buffer zones triggers the system's
transition to goal-seeking mode. We demonstrate that the system's states under
this framework achieve safety and asymptotic stabilization. Experimental
results in simulated and real-world environments have validated our framework's
capability, allowing a LiDAR-equipped mobile robot to efficiently and safely
reach the desired location within dynamic environments containing multiple
obstacles.
