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.

翻译：本文提出了一种基于激光雷达的目标搜索与探索框架，解决了在包含静态与动态障碍物的非结构化环境中在线避障的效率问题。该框架针对传统动态控制障碍函数（D-CBFs）的两个重大挑战：其在线构建以及因使用多个D-CBFs导致的实时性下降。为应对第一个挑战，框架的感知模块首先通过DBSCAN算法对点云进行聚类，随后利用最小包围椭圆（MBEs）算法封装这些聚类以生成椭圆表示。通过对比当前时刻MBEs的状态与历史存储状态，实现静态与动态障碍物的区分，并采用卡尔曼滤波器预测后者的运动。该分析促进了每个MBE对应的D-CBF在线构建。为应对第二个挑战，我们引入缓冲区，为每个识别出的障碍物在线生成II型D-CBF。利用这些缓冲区作为激活区域，可显著减少需要激活的D-CBF数量。当系统进入这些缓冲区时，优先考虑安全性，自主导航安全路径，故称为探索模式；退出缓冲区则触发系统切换至目标搜索模式。我们证明了在该框架下系统状态可实现安全性与渐近稳定性。仿真与真实环境实验已验证了本框架的能力，使配备激光雷达的移动机器人在包含多障碍物的动态环境中高效、安全地到达目标位置。