  In this work, we introduce a new graph search algorithm, lazy edged based A*
(LEA*), for robot motion planning. By using an edge queue and exploiting the
idea of lazy search, LEA* is optimally vertex efficient similar to A*, and has
improved edge efficiency compared to A*. LEA* is simple and easy to implement
with minimum modification to A*, resulting in a very small overhead compared to
previous lazy search algorithms. We also explore the effect of inflated
heuristics, which results in the weighted LEA* (wLEA*). We show that the edge
efficiency of wLEA* becomes close to LazySP and, thus is near-optimal. We test
LEA* and wLEA* on 2D planning problems and planning of a 7-DOF manipulator. We
perform a thorough comparison with previous algorithms by considering sparse,
medium, and cluttered random worlds and small, medium, and large graph sizes.
Our results show that LEA* and wLEA* are the fastest algorithms to find the
plan compared to previous algorithms.

Robotics;Artificial Intelligence