Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

Quin, Phillip; Nguyen, Dac Dang Khoa; Vu, Thanh Long; Alempijevic, Alen; Paul, Gavin

doi:10.3389/frobt.2021.616470

Cited by 14 publications

(7 citation statements)

References 23 publications

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“…Frontiers have been effectively identified for topological maps as nodes with no neighbors in certain directions [76]. For 2D OG maps, a plethora of geometric frontier-detection methods have been developed to circumvent the computational cost of searching the entire space [108]. Keidar and Kaminka [109] propose the wavefront frontier detector (WFD) and fast frontier detector (FFD).…”

Section: B Detecting Goal Locationsmentioning

confidence: 99%

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed

Strader

Carrillo³

et al. 2023

IEEE Trans. Robot.

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Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-ofthe-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including beliefspace planning and deep reinforcement learning techniques, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

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Section: B Detecting Goal Locationsmentioning

confidence: 99%

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed

Strader

Carrillo³

et al. 2023

IEEE Trans. Robot.

View full text Add to dashboard Cite

show abstract

“…Frontiers have been effectively identified for topological maps as nodes with no neighbors in certain directions [69]. For 2D OG maps, a plethora of geometric frontier-detection methods have been developed to circumvent the computational cost of searching the entire space [102]. Keidar and Kaminka [103] propose the wavefront frontier detector (WFD) and fast frontier detector (FFD).…”

Section: B Detecting Goal Locationsmentioning

confidence: 99%

“…Following this idea, the same authors present the incremental WFD [104], that restricts the search to recently scanned areas. Quin et al [102] improve the performance of the previous algorithms by only evaluating a subset of the observed free space. Refer to [102], [105] for further discussion.…”

Section: B Detecting Goal Locationsmentioning

confidence: 99%

“…Quin et al [102] improve the performance of the previous algorithms by only evaluating a subset of the observed free space. Refer to [102], [105] for further discussion. Umari and Mukhopadhyay [50] first present a frontier search method over a 2D OG based on rapidly-exploring random trees (RRTs) that grow both globally and locally to sample recently scanned regions.…”

Section: B Detecting Goal Locationsmentioning

confidence: 99%

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A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Placed¹,

Strader²,

Carrillo³

et al. 2022

Preprint

View full text Add to dashboard Cite

Active Simultaneous Localization and Mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this work, we survey the state-of-the-art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the classical solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and modern techniques based on deep reinforcement learning, and review related work on multi-robot coordination. The manuscript concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics.

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“…Thus, the representation of polygons is also adopted in this study to express most workspace that needs to be explored. Such a method simplifies the complex environments and solves the covering irregularity for vehicles ( Quin et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

et al. 2022

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With the introduction of autonomy into the precision agriculture process, environmental exploration, disaster response, and other fields, one of the global demands is to navigate autonomous vehicles to completely cover entire unknown environments. In the previous complete coverage path planning (CCPP) research, however, autonomous vehicles need to consider mapping, obstacle avoidance, and route planning simultaneously during operating in the workspace, which results in an extremely complicated and computationally expensive navigation system. In this study, a new framework is developed in light of a hierarchical manner with the obtained environmental information and gradually solving navigation problems layer by layer, consisting of environmental mapping, path generation, CCPP, and dynamic obstacle avoidance. The first layer based on satellite images utilizes a deep learning method to generate the CCPP trajectory through the position of the autonomous vehicle. In the second layer, an obstacle fusion paradigm in the map is developed based on the unmanned aerial vehicle (UAV) onboard sensors. A nature-inspired algorithm is adopted for obstacle avoidance and CCPP re-joint. Equipped with the onboard LIDAR equipment, autonomous vehicles, in the third layer, dynamically avoid moving obstacles. Simulated experiments validate the effectiveness and robustness of the proposed framework.

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Approaches for Efficiently Detecting Frontier Cells in Robotics Exploration

Cited by 14 publications

References 23 publications

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers

Deep Learning-Based Complete Coverage Path Planning With Re-Joint and Obstacle Fusion Paradigm

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