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

2025-02-28

AI Summary

To address the low exploration efficiency and poor real-time performance of micro aerial vehicles (MAVs) in complex, large-scale 3D environments, this paper proposes a fast autonomous exploration framework integrating frontier-point-driven navigation with deterministic sampling. The method constructs an online 3D voxel map while performing exploration. Key contributions include: (1) the first 3D frontier-point detector that jointly enforces field-of-view constraints and completeness guarantees; (2) an incremental, deterministic sampling-based roadmap construction method leveraging sensor field-of-view geometry and detected frontiers; and (3) a two-stage path planner combining lazy collision evaluation with B-spline smoothing for efficient trajectory generation. Extensive simulation and real-world experiments demonstrate that the proposed framework significantly outperforms random sampling and pure frontier-based methods in exploration speed, volumetric coverage, and computational efficiency, while maintaining robustness and real-time capability.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Multiagent Systems: Multiagent Planning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In this paper, 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-based and sampling-based strategies that can achieve rapid global exploration of the environment. Specifically, a field-of-view-based (FOV) 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 for further improving 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.

Problem

• Fast exploration of complex 3-D environments using MAVs.
• Integration of frontier-based and sampling-based strategies.
• Efficient path planning for autonomous exploration.

Innovation

• Integrates frontier-based and sampling-based exploration strategies
• Uses deterministic sampling for incremental road map building
• Implements two-stage path planner with lazy evaluation