  Motion planning is a key tool that allows robots to navigate through an
environment without collisions. The problem of robot motion planning has been
studied in great detail over the last several decades, with researchers
initially focusing on systems such as planar mobile robots and low
degree-of-freedom (DOF) robotic arms. The increased use of high DOF robots that
must perform tasks in real time in complex dynamic environments spurs the need
for fast motion planning algorithms. In this overview, we discuss several types
of strategies for motion planning in high dimensional spaces and dissect some
of them, namely grid search based, sampling based and trajectory optimization
based approaches. We compare them and outline their advantages and
disadvantages, and finally, provide an insight into future research
opportunities.
