  Among the most prevalent motion planning techniques, sampling and trajectory
optimization have emerged successful due to their ability to handle tight
constraints and high-dimensional systems, respectively. However, limitations in
sampling in higher dimensions and local minima issues in optimization have
hindered their ability to excel beyond static scenes in offline settings. Here
we consider highly dynamic environments with long horizons that necessitate a
fast online solution. We present a unified approach that leverages the
complementary strengths of sampling and optimization, and interleaves them both
in a manner that is well suited to this challenging problem. With benchmarks in
multiple synthetic and realistic simulated environments, we show that our
approach performs significantly better on various metrics against baselines
that employ either only sampling or only optimization. Project page:
https://sites.google.com/view/jistplanner
