Fast Sampling-based Next-Best-View Exploration Algorithm for a MAV

Respall, Victor Massagué; Devitt, Dmitry; Fedorenko, Roman; Klimchik, Alexandr

doi:10.1109/icra48506.2021.9562107

Cited by 19 publications

(15 citation statements)

References 17 publications

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“…An incremental sampling and probabilistic roadmap are used in [14] to improve the efficiency of planning. The method [15] uses a combination of sampling and frontier-based method to reduce the impact of finding unexplored areas in large scenarios. There are also some two-stage methods [16], [17] to cover the entire environment efficiently by different planning strategies in the global and local map.…”

Section: A Sampling-based Exploration Methodsmentioning

confidence: 99%

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

Zhao¹,

Li²,

Xie³

et al. 2023

Preprint

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Autonomous exploration is one of the important parts to achieve the fast autonomous mapping and target search. However, most of the existing methods are facing low-efficiency problems caused by low-quality trajectory or back-and-forth maneuvers. To improve the exploration efficiency in unknown environments, a fast autonomous exploration planner (FAEP) is proposed in this paper. Different from existing methods, we firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors in the asymmetric traveling salesman problem (ATSP). Then, according to the exploration sequence and the distribution of frontiers, an adaptive yaw planning method is proposed to cover more frontiers by yaw change during an exploration journey. In addition, to increase the speed and fluency of flight, a dynamic replanning strategy is also adopted. We present sufficient comparison and evaluation experiments in simulation environments. Experimental results show the proposed exploration planner has better performance in terms of flight time and flight distance compared to typical and state-of-the-art methods. Moreover, the effectiveness of the proposed method is further evaluated in real-world environments.

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Section: A Sampling-based Exploration Methodsmentioning

confidence: 99%

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

Zhao¹,

Li²,

Xie³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To validate the proposed approach, we conducted several simulations and experiments in different scenarios using a mobile platform in the physics engine-based simulator Gazebo and an experimental mobile robot. We evaluated the exploration performance using the exploration completeness and exploration efficiency by comparing against two state-of-the-art approaches: Autonomous Exploration Planner (AEP) (Selin et al, 2019) and Fast Next-Best-View Exploration (F-NBVE) (Respall et al, 2021). After that, we conducted ablation experiments to verify the effectiveness of the localization uncertainty-awareness and the terrain-aware tracking controller in robust exploration.…”

Section: Simulations and Experimentsmentioning

confidence: 99%

Whole‐body motion planning and tracking of a mobile robot with a gimbal RGB‐D camera for outdoor 3D exploration

Wang,

Chen,

2024

Journal of Field Robotics

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Autonomous exploration is a fundamental problem for certain applications of unmanned ground vehicles (UGVs). Depth cameras are widely used in exploring indoor and specific outdoor environments. However, existing methods suffer from inefficient and unstable performance of exploration in open outdoor environments. This study develops a framework to achieve fast and robust three‐dimensional exploration by UGVs with a gimbal camera in challenging outdoor environments. A hierarchical reactive planning approach based on a multilayered map is proposed to improve exploration efficiency. Additionally, an adaptive semantic mapping algorithm is proposed to more accurately and stably represent the environmental localization uncertainty. Localization uncertainty is integrated into the planning approach to decrease odometry drift and prevent localization failure, further enhancing the exploration robustness. A viewpoint tracking controller based on terrain‐aware model predictive control is proposed to guarantee the accuracy of the planned viewpoint tracking and to further improve the robustness of localization and exploration on rough terrains. Finally, several physical‐engine simulations and experiments in outdoor environments are performed. The comparisons to existing state‐of‐the‐art approaches have verified the effectiveness of the proposed approach. The code of our system will be available at https://github.com/HITSZ-NRSL/Open3DExplorer.git.

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“…The problem of autonomous exploration has been studied by many scholars in recent years, and lots of methods from multiple angles have been proposed, which are mainly divided into the following three categories: samplingbased exploration [11]- [19], frontier-based exploration [20]- [27] and algorithms based on machine learning which has emerged recently [28]. This paper only discusses the previous two algorithms which have been widely used in various exploration tasks.…”

Section: Related Workmentioning

confidence: 99%

“…An incremental sampling and probabilistic roadmap are used in [18] to improve the efficiency of planning. The method [19] uses a combination of sampling and frontierbased method to reduce the impact of finding unexplored areas in large scenarios. There are also some two-stage methods [12], [13] to cover the entire environment efficiently by different planning strategies in the global and local map.…”

Section: Related Workmentioning

confidence: 99%

FAEP: Fast Autonomous Exploration Planner for UAV Equipped with Limited FOV Sensor

Zhao¹,

Li²,

Chen³

et al. 2022

Preprint

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Autonomous exploration is one of the important parts to achieve the autonomous operation of Unmanned Aerial Vehicles (UAVs). To improve the efficiency of the exploration process, a fast and autonomous exploration planner (FAEP) is proposed in this paper. We firstly design a novel frontiers exploration sequence generation method to obtain a more reasonable exploration path, which considers not only the flight-level but frontier-level factors into TSP. According to the exploration sequence and the distribution of frontiers, a two-stage heading planning strategy is proposed to cover more frontiers by heading change during an exploration journey. To improve the stability of path searching, a guided kinodynamic path searching based on a guiding path is devised. In addition, a dynamic start point selection method for replanning is also adopted to increase the fluency of flight. We present sufficient benchmark and real-world experiments. Experimental results show the superiority of the proposed exploration planner compared with typical and state-of-the-art methods.

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Fast Sampling-based Next-Best-View Exploration Algorithm for a MAV

Cited by 19 publications

References 17 publications

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

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

Whole‐body motion planning and tracking of a mobile robot with a gimbal RGB‐D camera for outdoor 3D exploration

FAEP: Fast Autonomous Exploration Planner for UAV Equipped with Limited FOV Sensor

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