  Sampling-based algorithms solve the path planning problem by generating
random samples in the search-space and incrementally growing a connectivity
graph or a tree. Conventionally, the sampling strategy used in these algorithms
is biased towards exploration to acquire information about the search-space. In
contrast, this work proposes an optimization-based procedure that generates new
samples to improve the cost-to-come value of vertices in a neighborhood. The
application of proposed algorithm adds an exploitative-bias to sampling and
results in a faster convergence to the optimal solution compared to other
state-of-the-art sampling techniques. This is demonstrated using benchmarking
experiments performed fora variety of higher dimensional robotic planning
tasks.
