  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.
