Incremental algorithms for Safe and Reachable Frontier Detection for robot exploration

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.

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“…Senarathne et al present an algorithm called OBB based Frontier Detector (ODF) ( Senarathne et al, (2013) ; Senarathne and Wang. (2015) ).…”

Section: Introductionmentioning

confidence: 99%

Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

Quin

Nguyen

et al. 2021

Front. Robot. AI

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“…Many researchers have proposed various autonomous exploration methods in recent years, which can be divided into frontier-based methods [11][12][13][14], information-based methods [15,16], and hybrid methods [17]. Different types of maps are applied to autonomous exploration, such as methods [11,[18][19][20] based on occupancy grid map, methods [21,22] based on topological maps, and methods [23] based on feature maps. The frontier-based method is intuitive and efficient, and an occupancy grid map can be used for efficient path planning.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Safe Robotic Autonomous Environment Exploration Using Integrated Frontier Detection and Multiple Path Evaluation

Sun

Zhang

2021

Remote Sensing

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Autonomous exploration and remote sensing using robots have gained increasing attention in recent years and aims to maximize information collection regarding the external world without human intervention. However, incomplete frontier detection, an inability to eliminate inefficient frontiers, and incomplete evaluation limit further improvements in autonomous exploration efficiency. This article provides a systematic solution for ground mobile robot exploration with high efficiency. Firstly, an integrated frontier detection and maintenance method is proposed, which incrementally discovers potential frontiers and achieves incremental maintenance of the safe and informative frontiers by updating the distance map locally. Secondly, we propose a novel multiple paths planning method to generate multiple paths from the robot position to the unexplored frontiers. Then, we use the proposed utility function to select the optimal path and improve its smoothness using an iterative optimization strategy. Ultimately, the model predictive control (MPC) method is applied to track the smooth path. Simulation experiments on typical environments demonstrate that compared with the benchmark methods, the proposed method reduce the path length by 27.07% and the exploration time by 27.09% on average. The real-world experimental results also reveal that our proposed method can achieve complete mapping with fewer repetitive paths.

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“…The primary goal is to make robots to acquire the most complete and accurate map of an environment in a finite time without human intervention. Many existing map exploration strategies are based on frontier [3]- [7], [9], which is the boundary between unexplored space and known space. The idea of frontier-based exploration strategy is to direct robots to unknown regions to complete exploration missions, thus the autonomous exploration task can be divided into three general steps: generation of frontier points, selection of the frontier point with the highest evaluation value, path planning to the selected frontier point.…”

Section: Introductionmentioning

confidence: 99%

“…and region extraction of digital image processing technology [3], [4]. In order to extract frontier edges, the entire map has to be processed, and as the map expands, processing it will consume more and more computational resources.…”

Section: Introductionmentioning

confidence: 99%

Autonomous robotic exploration based on frontier point optimization and multistep path planning

2019

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Autonomous robotic exploration of an unknown environment is a key technology for robot intelligence. In order to improve the efficiency of it, we propose a strategy based on frontier point optimization and multistep path planning in this paper. In the frontier points' optimization section, we present a random frontier points' optimization (RFPO) algorithm to select the frontier point with the highest evaluation value as the target frontier point. The evaluation function of frontier points is defined by considering information gain, navigation cost, and the precision of the localization of the robots. In the path planning section, we propose a multistep exploration strategy. Instead of planning the global path from the current position of the robot to the target frontier point directly, we set a local exploration path step size. When the robot's movement distance reaches the local exploration path step size, we reselect the current optimal frontier point for path planning to reduce the possibility that the robot may take some repetitive paths. Finally, the relevant experiments are carried out to verify the effectiveness of this strategy.INDEX TERMS Autonomous exploration, random frontier points optimization algorithm, frontier point evaluation function, multistep path planning.

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Incremental algorithms for Safe and Reachable Frontier Detection for robot exploration

Cited by 24 publications

References 33 publications

Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

Efficient and Safe Robotic Autonomous Environment Exploration Using Integrated Frontier Detection and Multiple Path Evaluation

Autonomous robotic exploration based on frontier point optimization and multistep path planning

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