Online Efficient Safety-Critical Control for Mobile Robots in Unknown Dynamic Multi-Obstacle Environments

TL;DR

Experimental results in simulated and real-world environments have validated the LiDAR-based goal-seeking and exploration framework’s capability, allowing a LiDAR-equipped mobile robot to efficiently and safely reach the desired location within dynamic environments containing multiple obstacles.

Abstract

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.

Figures (9)

• Figure 1: Visualization of the algorithm for clustering and classification. (a) The DBSCAN clustering algorithm is applied to point cloud data $\mathcal{P}_{t}$, subsequently resulting in $\mathcal{E}_{t,k}$ through the MBE algorithm. (b) Clustering outcomes obtained by the Kuhn–Munkres algorithm.
• Figure 2: A detailed explanation of the parameters for the mobile robot and MBEs at time $t$. $l_{t,k}$ is measured along the line that extends from the center $(x_{t,k}^{ob},y_{t,k}^{ob})$ of $\mathcal{E}_{t,k}$ to the center $(x_{t},y_{t})$ of the circular space $\mathcal{C}(\boldsymbol{z})$ occupied by the mobile robot. $R_{safe}$ is the minimum safe distance.
• Figure 3: The goal-seeking and exploration framework, with and without the backup safety controller for specific scenarios: (a) Without the backup safety controller, the mobile robot oscillates at the boundaries of two buffer zones, ensuring safety but unable to reach the goal fast. (b) With the backup safety controller, the mobile robot can alter its path through multi-step prediction, facilitating goal attainment.
• Figure 4: Simulation setups and perception results for moving obstacle scenarios.
• Figure 5: Comparison of trajectories utilizing various methods under different numbers of static obstacles. The starting point is at the (0, 0) position, with p1, p2, p3, and p4 representing different goals in sequence.

Theorems & Definitions (13)

• Definition 1
• Definition 2
• Proposition 1
• Definition 3
• Theorem 1
• Theorem 2
• proof
• Theorem 3
• proof
• Theorem 4