  With the increasing need for multi-robot for exploring the unknown region in
a challenging environment, efficient collaborative exploration strategies are
needed for achieving such feat. A frontier-based Rapidly-Exploring Random Tree
(RRT) exploration can be deployed to explore an unknown environment. However,
its' greedy behavior causes multiple robots to explore the region with the
highest revenue, which leads to massive overlapping in exploration process. To
address this issue, we present a temporal memory-based RRT (TM-RRT) exploration
strategy for multi-robot to perform robust exploration in an unknown
environment. It computes adaptive duration for each frontier assigned and
calculates the frontier's revenue based on the relative position of each robot.
In addition, each robot is equipped with a memory consisting of frontier
assigned and share among fleets to prevent repeating assignment of same
frontier. Through both simulation and actual deployment, we have shown the
robustness of TM-RRT exploration strategy by completing the exploration in a
25.0m x 54.0m (1350.0m2) area, while the conventional RRT exploration strategy
falls short.
