  For a nonlinear stochastic path planning problem, sampling-based algorithms
generate thousands of random sample trajectories to find the optimal path while
guaranteeing safety by Lagrangian penalty methods. However, the sampling-based
algorithm can perform poorly in obstacle-rich environments because most samples
might violate safety constraints, invalidating the corresponding samples. To
improve the sample efficiency of sampling-based algorithms in cluttered
environments, we propose an algorithm based on model predictive path integral
control and control barrier functions. The proposed algorithm needs fewer
samples and time-steps and has a better performance in cluttered environments
compared to the original model predictive path integral control algorithm.
