Tethered UAV autonomous knotting on environmental structures for transport

Beijing Institute of Technology Press Co., Ltd

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(A) Object transport demonstration. (i) The full process of lifting the target object. After the tethered UAV autonomously performs knotting on top of a tall building, the winch reels in the tether to lift the object. (ii) Autonomous knotting procedure. The UAV first performs object enclosing, then enters the tether search state, and finally executes tether binding to complete knotting. (B) Radar chart comparing the proposed system with other state-of-the-art aerial/cable-driven transport robots, including the commercial high-payload multicopter (DJI FC 30), commercial high-payload helicopter (Blowfish A2G), and a portable wire-driven parallel robot (Cubix). PWR (payload-to-weight ratio) is the metric describing the ratio of the maximum payload capacity to the weight of the robot. The outer ring of the radar chart is color-matched to the best-performing system for each corresponding metric.

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Credit: Lihua Xie, Nanyang Technological University, School of Electrical and Electronic Engineering

“Cable-driven systems excel at heavy-load transport but are limited by fixed anchoring points in unstructured environments,” explained study corresponding author Lihua Xie from Nanyang Technological University. The core innovations include (a) a human-in-the-loop knot planner integrating enclosing plane extraction, frontier-based path search, and knotting trajectory generation; (b) three key optimization metrics (enclosing planarity, tether visibility, tether clearance) ensuring task reliability; and (c) seamless integration of UAV mobility and winch load-bearing capability. “This system enables rapid deployment of transport tasks without pre-designed anchors, expanding robotic logistics to complex scenes.”

The system leverages key technical advancements: The knot planner interprets user sketches to extract enclosing planes, searches for paths around target structures via frontier clusters, and generates optimized trajectories. The UAV is equipped with LiDAR, RGB-D cameras, and an onboard computer for real-time perception, mapping, and tether detection. “The three metrics work synergistically—enclosing planarity prevents tether slippage, tether visibility maintains real-time monitoring, and tether clearance avoids collisions,” said co-first author Rui Jin.

The study authors validated the system through real-world and simulation experiments: In an urban outdoor environment, the system autonomously completed knotting on a linkway roof and lifted a 15.3-kg payload to 3.5 m in 42.1 s. Simulation tests confirmed shape-agnostic performance, with success rates exceeding 90% on four distinct structures (pipeline, archway, billboard, bridge) across 30 trials per target. Ablation experiments verified the necessity of the three metrics—removing enclosing planarity reduced success rate to 8%, while omitting tether visibility or clearance impaired binding robustness.

“While the system shows strong performance, it faces limitations: reliance on clear visual access to the tether, sensitivity to environmental disturbances, and the need for mechanical optimization of the winch-tether mechanism,” said co-first author Xinhang Xu. Future work will focus on advanced control algorithms to compensate for tether-induced disturbances, multimodal sensing for reliable tether detection, and explicit modeling of tether-environment interactions. Overall, this autonomous knotting system offers a novel solution for rapid-deployable heavy-load transport, unlocking new capabilities in robotic logistics for unstructured environments.

Authors of the paper include Rui Jin, Xinhang Xu, Yizhuo Yang, Jianping Li, Muqing Cao, and Lihua Xie.

This research was partially supported by the Ministry of Education, Singapore, under AcRF TIER 1 Grant RG64/23, and the National Research Foundation Medium-Sized Centre for Advanced Robotics Technology Innovation.

The paper, “Tethered UAV Autonomous Knotting on Environmental Structures for Transport” was published in the journal Cyborg and Bionic Systems on Dec. 26, 2025, at DOI: 10.34133/cbsystems.0450.