Ada-Detector: Adaptive Frontier Detector for Rapid Exploration

TL;DR

This paper introduces Ada-Detector, an adaptive frontier detection method based on RRT that improves real-time exploration efficiency by adaptively adjusting sampling and reducing over-sampling, validated in simulated benchmarks.

Contribution

The paper presents an adaptive RRT-based frontier detector that enhances sampling efficiency and incremental detection in unknown environments.

Findings

Reduces frontier detection runtime by about 40% compared to SOTA.

Improves sampling success rate through adaptive environment sensing.

Effectively solves over-sampling in sliding window areas.

Abstract

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.