  Implementing obstacle avoidance in dynamic environments is a challenging
problem for robots. Model predictive control (MPC) is a popular strategy for
dealing with this type of problem, and recent work mainly uses control barrier
function (CBF) as hard constraints to ensure that the system state remains in
the safe set. However, in crowded scenarios, effective solutions may not be
obtained due to infeasibility problems, resulting in degraded controller
performance. We propose a new MPC framework that integrates CBF to tackle the
issue of obstacle avoidance in dynamic environments, in which the infeasibility
problem induced by hard constraints operating over the whole prediction horizon
is solved by softening the constraints and introducing exact penalty, prompting
the robot to actively seek out new paths. At the same time, generalized CBF is
extended as a single-step safety constraint of the controller to enhance the
safety of the robot during navigation. The efficacy of the proposed method is
first shown through simulation experiments, in which a double-integrator system
and a unicycle system are employed, and the proposed method outperforms other
controllers in terms of safety, feasibility, and navigation efficiency.
Furthermore, real-world experiment on an MR1000 robot is implemented to
demonstrate the effectiveness of the proposed method.
