Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions (2403.19122v2)

Abstract: Dynamic obstacle avoidance is a challenging topic for optimal control and optimization-based trajectory planning problems. Many existing works use Control Barrier Functions (CBFs) to enforce safety constraints for control systems. CBFs are typically formulated based on the distance to obstacles, or integrated with path planning algorithms as a safety enhancement tool. However, these approaches usually require knowledge of the obstacle boundary equations or have very slow computational efficiency. In this paper, we propose a framework based on model predictive control (MPC) with discrete-time high-order CBFs (DHOCBFs) to generate a collision-free trajectory. The DHOCBFs are first obtained from convex polytopes generated through grid mapping, without the need to know the boundary equations of obstacles. Additionally, a path planning algorithm is incorporated into this framework to ensure the global optimality of the generated trajectory. We demonstrate through numerical examples that our framework allows a unicycle robot to safely and efficiently navigate tight, dynamically changing environments with both convex and nonconvex obstacles. By comparing our method to established CBF-based benchmarks, we demonstrate superior computing efficiency, length optimality, and feasibility in trajectory generation and obstacle avoidance.

• The paper introduces the iMPC-DHOCBF framework that integrates Control Barrier Functions into MPC for collision-free trajectory planning without prior obstacle boundary equations.
• It leverages Discrete-Time Control Barrier Functions within a convex MPC setup to achieve notable computational efficiency in dynamic environments.
• The method demonstrates scalability and flexibility for real-time obstacle avoidance, paving the way for advanced applications in autonomous systems.

Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions

The paper "Safety-Critical Planning and Control for Dynamic Obstacle Avoidance Using Control Barrier Functions" by Shuo Liu and Yihui Mao presents an advanced framework for trajectory planning and control in environments with dynamic obstacles. The research addresses key challenges inherent in obstacle avoidance for optimal control systems by leveraging Control Barrier Functions (CBFs) while integrating them into a Model Predictive Control (MPC) framework. A novel iterative approach named iMPC-DHOCBF is introduced, showcasing enhanced computational efficiency and safety assurance.

Key Contributions

The authors articulate a comprehensive approach to implement collision-free trajectory planning without the prior need for obstacle boundary equations knowledge. The framework utilizes Discrete-Time Control Barrier Functions (DCBFs) in a convex MPC setup, facilitating real-time operations in dynamic environments. This methodology is of particular importance as it deals with both convex and non-convex obstacles, providing a robust solution to the inherent non-linear and non-convex aspects typically encountered in dynamic obstacle avoidance.

Theoretical and Practical Implications

The research extends significant implications both theoretically and practically:

1. Numerical Efficacy: Demonstrated through numerical analyses, the proposed iMPC-DHOCBF framework exhibits remarkable computational efficiency, navigating complex environments with a noteworthy success rate in obstacle avoidance and trajectory planning.
2. Optimization and Feasibility: By utilizing a discrete-time variant of the high-order control barrier functions, the framework presents a feasible solution for implementing safety in planning algorithms, particularly in large horizon lengths scenarios where traditional methods may falter.
3. Scalability and Flexibility: The iterative method showcases scalable attributes by dynamically generating safe trajectories without prior mapping of the obstacle boundaries, thus emphasizing adaptability across different scenarios.
4. Future Applications: The methodology potentially paves the way for future implementations in autonomous systems where real-time dynamic planning is crucial, including autonomous driving and robotic navigation.

Speculation on Future Directions

While the proposed framework addresses many existing challenges, the paper hints at further research areas. Enhanced techniques for real-time adjustment without pre-knowledge of dynamic obstacles present a promising avenue. Furthermore, the integration of learning-based approaches with the presented model could see the optimization of parameters in real-time, adapting to dynamic changes seamlessly.

Overall, this research contributes to the field of robotics and control theory by offering a sophisticated and efficient solution to trajectory planning in dynamic environments. As the field progresses, further studies could focus on integrating this framework with other emerging technologies in artificial intelligence and autonomous systems, amplifying the scalability and efficacy in larger, more complex environments.