Sampling-based Motion Planning via Control Barrier Functions
This work addresses motion planning for robots in applications like self-driving cars and surveillance, offering an incremental improvement by integrating control barrier functions into RRT for better efficiency in dynamic settings.
The paper tackles the challenge of generating control signals for nonlinear systems in dynamic environments by proposing CBF-RRT, a sampling-based motion planning algorithm that efficiently produces obstacle-free paths and handles dynamic obstacles without nearest neighbor or collision checks, reducing run-time overhead compared to standard RRT variants.
Robot motion planning is central to real-world autonomous applications, such as self-driving cars, persistence surveillance, and robotic arm manipulation. One challenge in motion planning is generating control signals for nonlinear systems that result in obstacle free paths through dynamic environments. In this paper, we propose Control Barrier Function guided Rapidly-exploring Random Trees (CBF-RRT), a sampling-based motion planning algorithm for continuous-time nonlinear systems in dynamic environments. The algorithm focuses on two objectives: efficiently generating feasible controls that steer the system toward a goal region, and handling environments with dynamical obstacles in continuous time. We formulate the control synthesis problem as a Quadratic Program (QP) that enforces Control Barrier Function (CBF) constraints to achieve obstacle avoidance. Additionally, CBF-RRT does not require nearest neighbor or collision checks when sampling, which greatly reduce the run-time overhead when compared to standard RRT variants.