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Adaptive Clustering Convexification Mapping-Planning System for UAVs in Unknown Environments
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DOI：10.1109/TASE.2026.3670813.png)
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Abstract
En 中文
In complex environments, available moving space is often non-convex due to the presence of irregular obstacles, which complicates the mapping module’s ability to represent the environment with geometric symbols. This complexity hampers the effectiveness of the planning module, leading to increased computational and storage demands. To address these challenges posed by non-convex spaces, we propose an adaptive clustering convexification mapping-planning system (ACCMPS) for unmanned aerial vehicles (UAVs) navigating in unknown environments. Our approach introduces an adaptive space-searching algorithm that transforms non-convex areas into relatively convex sub-spaces represented by clusters through purposeful sampling. ACCMPS utilizes a sample-friendly 3D grid map for local mapping and constructs a storage-efficient cluster-based weighted graph for global representation, linking convex sub-spaces represented by clusters. This integration streamlines the mapping and path planning processes, enabling direct initial path generation from the weighted graph, rather than relying on traditional algorithms that blindly explore non-convex spaces. To optimize paths, we employ a particle filter on the initial graph-derived path to handle non-convexity, followed by optimization into a smooth B-spline trajectory via cost function minimization within relatively convex regions. A series of real-world experiments conducted in both narrow and expansive environments demonstrates the advantages of ACCMPS in terms of computational efficiency, storage consumption, and path quality compared to existing state-of-the-art methods. Note to Practitioners—This paper proposes an Adaptive Clustering Convexification Mapping-Planning System (ACCMPS) for unmanned aerial vehicles (UAVs) navigating in unknown environments. The system enables UAVs to autonomously reach user-specified target points one by one while avoiding obstacles, even in completely unexplored spaces. By adaptively decomposing complex non-convex environments into relatively convex sub-spaces represented by clusters, ACCMPS significantly reduces computational and storage overhead compared to traditional methods that struggle with irregular obstacle layouts. The key advantage of ACCMPS lies in its efficient representation of the environment using a cluster-based weighted graph, which allows rapid global initial path generation without exhaustive exploration of local optimal regions in non-convex spaces. Path optimization is further refined through a particle filter and gradient-based optimization, ensuring safe and smooth B-spline-based trajectories. Real-world experiments in both confined and expansive environments validate its superior performance in computational speed, memory efficiency, and path quality. Potential applications include post-disaster rescue missions, where UAVs must efficiently explore chaotic, unmapped areas. For inspection tasks in industrial or agricultural settings, ACCMPS eliminates the need for pre-built maps, making it only requires a few rough waypoints as input to enable fully autonomous obstacle avoidance, significantly enhancing operational flexibility in unknown environments.
Keywords:
Unmanned aerial vehicles (UAVs)
robot navigation
mapping
path planning
trajectory planning
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