  In this paper, we propose an efficient frontier detector method based on
adaptive Rapidly-exploring Random Tree (RRT) for autonomous robot exploration.
Robots can achieve real-time incremental frontier detection when they are
exploring unknown environments. First, our detector adaptively adjusts the
sampling space of RRT by sensing the surrounding environment structure. The
adaptive sampling space can greatly improve the successful sampling rate of RRT
(the ratio of the number of samples successfully added to the RRT tree to the
number of sampling attempts) according to the environment structure and control
the expansion bias of the RRT. Second, by generating non-uniform distributed
samples, our method also solves the over-sampling problem of RRT in the sliding
windows, where uniform random sampling causes over-sampling in the overlap area
between two adjacent sliding windows. In this way, our detector is more
inclined to sample in the latest explored area, which improves the efficiency
of frontier detection and achieves incremental detection. We validated our
method in three simulated benchmark scenarios. The experimental comparison
shows that we reduce the frontier detection runtime by about 40% compared with
the SOTA method, DSV Planner.
