Autonomous Exploration Method for Fast Unknown Environment Mapping by Using UAV Equipped With Limited FOV Sensor

Zhao, Yinghao; Li, Yan; Xie, Hong Quan; Dai, Jiawei; Wei, Pengcheng

doi:10.1109/tie.2023.3285921

Cited by 7 publications

(9 citation statements)

References 28 publications

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“…In this section, we conduct benchmark comparisons using simulation experiments to verify the effectiveness and exploration efficiency of the proposed method. Robot exploration in maze scenes is the most effective method to verify the efficiency of autonomous exploration [13]. Thus, we manually constructed two large-scale mazes, Maze-1 (48 × 63 × 2 m 3 ) and Maze-2 (66 × 62 × 2 m 3 ), in Gazebo simulation platform.…”

Section: Benchmark Comparisonsmentioning

confidence: 99%

“…We employ the optimal exploration strategy provided by the CPP solver to guide robot exploration and compare it with FUEL and FAEP [12,13]. They are state-of-the-art frontierbased methods which have been proposed in recent years, and which exhibit high exploration efficiency and have open-sourced their code to serve community.…”

Section: Benchmark Comparisonsmentioning

confidence: 99%

“…However, most autonomous exploration methods tend to greedily guide the robot to exploration scenes with immediate rewards and neglect some targets with long-term rewards, resulting in lower efficiency in global exploration [12]. Although some methods plan paths from the global exploration standpoint, the robot inevitably overlooks some scenarios during exploration because of the limited perception range of sensors and the unpredictability of unknown environments [12,13]. To thoroughly explore a given region, the robot must revisit areas containing those missed scenarios, resulting in a waste of resources.…”

Section: Introductionmentioning

confidence: 99%

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Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles

Cao,

Du,

Yang

2024

Drones

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Robot autonomous exploration is a challenging and valuable research field that has attracted widespread research interest in recent years. However, existing methods often encounter problems such as incomplete exploration, repeated exploration paths, and low exploration efficiency when facing large-scale scenes. Considering that many indoor and outdoor scenes usually have a prior topological map, such as road navigation maps, satellite road network maps, indoor computer-aided design (CAD) maps, etc., this paper incorporated this information into the autonomous exploration framework and proposed an innovative topological map-based autonomous exploration method for large-scale scenes. The key idea of the proposed method is to plan exploration paths with long-term benefits by tightly merging the information between robot-collected and prior topological maps. The exploration path follows a global exploration strategy but prioritizes exploring scenes outside the prior information, thereby preventing the robot from revisiting explored areas and avoiding the duplication of any effort. Furthermore, to improve the stability of exploration efficiency, the exploration path is further refined by assessing the cost and reward of each candidate viewpoint through a fast method. Simulation experimental results demonstrated that the proposed method outperforms state-of-the-art autonomous exploration methods in efficiency and stability and is more suitable for exploration in large-scale scenes. Real-world experimentation has also proven the effectiveness of our proposed method.

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Section: Benchmark Comparisonsmentioning

confidence: 99%

Section: Benchmark Comparisonsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles

Cao,

Du,

Yang

2024

Drones

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“…To alleviate this problem, the best candidate target is chosen by either satisfying the dynamic constraints [6] or solving a traveling salesman problem (TSP) [7]. To further improve the global nature of the exploration algorithms, a small subset of the algorithms introduce a priori information about the environment while selecting the next target, e.g., detecting small frontier regions based on known maps to preferentially cover them [8], and predicting environmental maps in real time using deep learning to more accurately calculate information gain [9]. Meanwhile, the algorithms based on exploratory maps are naturally incapable of properly evaluating small frontier clusters as they lack a global view of environmental contour information, which is essential to characterize the importance of small frontier clusters.…”

Section: Introductionmentioning

confidence: 99%

“…We benchmarked the proposed algorithm against two state-of-the-art algorithms [7,8] in different simulation environments, and superior performance is exhibited by the proposed algorithm. An experiment was conducted on a robotic UAV platform, and the results confirm the feasibility of using our novel approach in real-world scenarios.…”

Section: Introductionmentioning

confidence: 99%

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

Hou,

Pan,

et al. 2024

Drones

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In UAV autonomous exploration, large frontier clusters are commonly associated with high information gain and are visited first. In contrast, small and isolated frontier clusters with fewer frontiers are associated with smaller information gain and are thus explored with low priority. However, these small and isolated frontier clusters are often in close proximity to UAVs and surrounded by explored areas, which could result in back-and-forth flights that lower exploration efficiency. This paper proposes LAEA, a LiDAR-assisted and depth camera-dominated UAV exploration algorithm that aims to improve UAV autonomous exploration efficiency. A hybrid map is obtained that characterizes rich environmental profile information in real time, enabling us to detect small and isolated frontier clusters that can lead to repeated visits to explored areas. An environmental information gain optimization strategy is incorporated such that frontier clusters with larger unexplored areas behind them, as well as small and isolated frontier clusters close to the UAV, are assigned higher weights to prioritize their visit order. An optimized flight trajectory is generated to cover unexplored frontier clusters in the immediate vicinity of the UAV while flying to the next target. A comprehensive comparison between the proposed algorithm and state-of-the-art algorithms was conducted via a simulation study, which showed that our algorithm exhibits superior exploration efficiency in various environments. Experiments were also carried out to verify the feasibility of the proposed approach in real-world scenarios.

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A Multi-UAVs Cooperative Spectrum Sensing Method Based on Improved IDW Algorithm

Shi,

Chong,

Huang

et al. 2024

Communications in Computer and Information Science

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Autonomous Exploration Method for Fast Unknown Environment Mapping by Using UAV Equipped With Limited FOV Sensor

Cited by 7 publications

References 28 publications

Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles

Topological Map-Based Autonomous Exploration in Large-Scale Scenes for Unmanned Vehicles

LAEA: A 2D LiDAR-Assisted UAV Exploration Algorithm for Unknown Environments

A Multi-UAVs Cooperative Spectrum Sensing Method Based on Improved IDW Algorithm

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