Abstract
Many robot exploration algorithms that are used to explore office, home, or outdoor environments, rely on the concept of frontier cells. Frontier cells define the border between known and unknown space. Frontier-based exploration is the process of repeatedly detecting frontiers and moving towards them, until there are no more frontiers and therefore no more unknown regions. The faster frontier cells can be detected, the more efficient exploration becomes. This paper proposes several algorithms for detecting frontiers. The first is called Naïve Active Area (NaïveAA) frontier detection and achieves frontier detection in constant time by only evaluating the cells in the active area defined by scans taken. The second algorithm is called Expanding-Wavefront Frontier Detection (EWFD) and uses frontiers from the previous timestep as a starting point for searching for frontiers in newly discovered space. The third approach is called Frontier-Tracing Frontier Detection (FTFD) and also uses the frontiers from the previous timestep as well as the endpoints of the scan, to determine the frontiers at the current timestep. Algorithms are compared to state-of-the-art algorithms such as Naïve, WFD, and WFD-INC. NaïveAA is shown to operate in constant time and therefore is suitable as a basic benchmark for frontier detection algorithms. EWFD and FTFD are found to be significantly faster than other algorithms.
Highlights
The concept of frontiers was first proposed by Yamauchi. (1997)
This paper presents the details of two frontier detection algorithms called Naïve Active Area frontier detection (NaïveAA) and Expanding-Wavefront Frontier Detection (EWFD), which were first introduced by the authors in a conference paper (Quin et al, (2014))
Experiment three was conducted in a 60 × 60 m real-world office environment at the University of Technology Sydney (UTS), where a MP-700 mobile robot equipped with a SICK-S300 laser scanner was manually controlled to construct a map
Summary
The concept of frontiers was first proposed by Yamauchi. (1997). Frontiers have since been used in many robot exploration strategies, whether by single robots (Digor et al (2010); Quin et al (2013); Shade and Newman. (2011); Paul et al, (2015); Dornhege and Kleiner. (2011); Paul et al, (2016)) or teams of multiple robots (Faigl and Kulich. (2013); Reid et al, (2013); Hassan et al, (2018)). Allowing faster frontier detection may improve the quality of the decisions made by the exploration algorithms since faster decisions can be made with more recent data. The speed of new sensors means that frontier detection is more likely to be the bottleneck to fast robot exploration and needs to be made more efficient. In the rest of this paper “known freespace” and “known occupied” will be referred to as freespace and occupied, respectively. This state is usually represented as a value from 0 to 1 representing that cell’s likelihood of containing an obstacle. Frontiers are cells of an occupancy grid that are freespace
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