Acetrans: An Autonomous Corridor-Based and Efficient UAV Suspended Transport System

2025-09-12

AI Summary

To address key challenges in UAV slung-load transportation within complex environments—including unreliable cable-payload dynamic perception, low efficiency in large-scale motion planning, and insufficient safety under cable bending and external disturbances—this paper proposes a tightly integrated perception-planning-control framework. We innovatively design a LiDAR-IMU tightly coupled algorithm for joint estimation of cable morphology and payload pose; introduce MACIRI, a multi-scale configuration-space iterative inflation method, to generate safe flight corridors; and integrate spatiotemporal trajectory optimization with a nonlinear model predictive controller (NMPC) incorporating cable bending constraints. Experimental results demonstrate that our system significantly outperforms state-of-the-art approaches in perception accuracy, planning efficiency, and disturbance rejection robustness, enabling efficient and stable flight in large-scale, cluttered environments.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Multiagent Planning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Estimating cable-payload dynamics for reliable perception
• Planning efficient trajectories in large-scale environments
• Ensuring whole-body safety under disturbances and bending

Innovation

• LiDAR-IMU fusion for cable-payload estimation
• MACIRI algorithm for scalable corridor planning
• NMPC with cable-bending constraints for safety