FSMP: A Frontier-Sampling-Mixed Planner for Fast Autonomous Exploration of Complex and Large 3-D Environments

In this article, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier- and sampling-based strategies that can achieve rapid global exploration of the environment. Sp...

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Veröffentlicht in:IEEE transactions on instrumentation and measurement Jg. 74; S. 1 - 14
Hauptverfasser: Zhang, Shiyong, Zhang, Xuebo, Dong, Qianli, Wang, Ziyu, Xi, Haobo, Yuan, Jing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autonomous exploration
Computing time
Detectors
environmental mapping
frontier detection
Lasers
micro aerial vehicle (MAV)
Micro air vehicles (MAV)
Planning
Random sampling
Real-time systems
Roads
Roads & highways
Robot sensing systems
Sampling methods
sampling-based algorithm
Simulation
Space exploration
Three-dimensional displays
Training
Trajectory
ISSN:0018-9456, 1557-9662
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Zusammenfassung:In this article, we propose a systematic framework for fast exploration of complex and large 3-D environments using micro aerial vehicles (MAVs). The key insight is the organic integration of the frontier- and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view (FOV)-based frontier detector with the guarantee of completeness and soundness is devised for identifying 3-D map frontiers. Different from random sampling-based methods, the deterministic sampling technique is employed to build and maintain an incremental road map based on the recorded sensor FOVs and newly detected frontiers. With the resulting road map, we propose a two-stage path planner. First, it quickly computes the global optimal exploration path on the road map using the lazy evaluation strategy. Then, the best exploration path is smoothed to further improve the exploration efficiency. We validate the proposed method both in simulation and real-world experiments. The comparative results demonstrate the promising performance of our planner in terms of exploration efficiency, computational time, and explored volume.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2025.3547488