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Zhenchang Liu

Publications and source records attributed to Zhenchang Liu.

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SHIELD: Spherical-Projection Hybrid-Frontier Integration for Efficient LiDAR-based Drone Exploration

This paper introduces SHIELD, a Spherical-Projection Hybrid-Frontier Integration for Efficient LiDAR-based Drone exploration method. Although laser LiDAR offers the advantage of a wide field of view, its application in UAV exploration still faces several challenges. The observation quality of LiDAR point clouds is generally inferior to that of depth cameras. Traditional frontier methods based on known and unknown regions impose a heavy computational burden, especially when handling the wide field of view of LiDAR. In addition, regions without point cloud are also difficult to classify as free space through raycasting. To address these problems, the SHIELD is proposed. It maintains an observation-quality occupancy map and performs ray-casting on this map to address the issue of inconsistent point-cloud quality during exploration. A hybrid frontier method is used to tackle both the computational burden and the limitations of point-cloud quality exploration. In addition, an outward spherical-projection ray-casting strategy is proposed to jointly ensure flight safety and exploration efficiency in open areas. Simulations and flight experiments prove the effectiveness of SHIELD. This work will be open-sourced to contribute to the research community.

cs.RO

Heterogeneous Unmanned Aerial Vehicles Cooperative Search Approach for Complex Environments

This paper studies a heterogeneous Unmanned Aerial Vehicles (UAVs) cooperative search approach suitable for complex environments. In the application, a fixed-wing UAV drops rotor UAVs to deploy the cluster rapidly. Meanwhile, the fixed-wing UAV works as a communication relay node to improve the search performance of the cluster further. The distributed model predictive control and genetic algorithms are adopted to make online intelligent decisions on UAVs search directions. On this basis, a jump grid decision method is proposed to satisfy the maneuverability constraints of UAVs, a parameter dynamic selection method is developed to make search decisions more responsive to task requirements, and a search information transmission method with low bandwidth is designed. This approach can enable UAVs to discover targets quickly, cope with various constraints and unexpected situations, and make adaptive decisions, significantly improving the robustness of search tasks in complex, dynamic, and unknown environments. The proposed approach is tested with several search scenarios, and simulation results show that the cooperative search performance of heterogeneous UAVs is significantly improved compared to homogeneous UAVs.

eess.SY