  Motion planning in the presence of multiple dynamic obstacles is an important
research problem from the perspective of autonomous vehicles as well as
space-constrained multi-robot work environment. In this paper, we address the
motion planning problem for multiple dynamic obstacle rich environment and
propose a probabilistically, complete novel motion planning algorithm. Our
claim is that given a fixed path cost i.e. the Euclidean path length, the
proposed algorithm plans a path with the least computational time as compared
to the state-of-the-art techniques. At the same time, given the time duration
for planning, it plans the minimum cost path. Dynamic constraints have been
taken into consideration while designing the planner such that the optimal
planned path is feasible for implementation. The results of extensive
simulation experiments show that the proposed sequential BIT* outperforms the
DOVS both in the planned path length as well as the path generation time.
