{"ID":2871853,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.10349","arxiv_id":"2509.10349","title":"Acetrans: An Autonomous Corridor-Based and Efficient UAV Suspended Transport System","abstract":"Unmanned aerial vehicles (UAVs) with suspended payloads offer significant advantages for aerial transportation in complex and cluttered environments. However, existing systems face critical limitations, including unreliable perception of the cable-payload dynamics, inefficient planning in large-scale environments, and the inability to guarantee whole-body safety under cable bending and external disturbances. This paper presents Acetrans, an Autonomous, Corridor-based, and Efficient UAV suspended transport system that addresses these challenges through a unified perception, planning, and control framework. A LiDAR-IMU fusion module is proposed to jointly estimate both payload pose and cable shape under taut and bent modes, enabling robust whole-body state estimation and real-time filtering of cable point clouds. To enhance planning scalability, we introduce the Multi-size-Aware Configuration-space Iterative Regional Inflation (MACIRI) algorithm, which generates safe flight corridors while accounting for varying UAV and payload geometries. A spatio-temporal, corridor-constrained trajectory optimization scheme is then developed to ensure dynamically feasible and collision-free trajectories. Finally, a nonlinear model predictive controller (NMPC) augmented with cable-bending constraints provides robust whole-body safety during execution. Simulation and experimental results validate the effectiveness of Acetrans, demonstrating substantial improvements in perception accuracy, planning efficiency, and control safety compared to state-of-the-art methods.","short_abstract":"Unmanned aerial vehicles (UAVs) with suspended payloads offer significant advantages for aerial transportation in complex and cluttered environments. However, existing systems face critical limitations, including unreliable perception of the cable-payload dynamics, inefficient planning in large-scale environments, and...","url_abs":"https://arxiv.org/abs/2509.10349","url_pdf":"https://arxiv.org/pdf/2509.10349v1","authors":"[\"Weiyan Lu\",\"Huizhe Li\",\"Yuhao Fang\",\"Zhexuan Zhou\",\"Junda Wu\",\"Yude Li\",\"Youmin Gong\",\"Jie Mei\"]","published":"2025-09-12T15:36:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}