AFD-CAMLs: Agile Force-Distribution-Aware Planning and Control for Cable-Suspended Aerial Multi-Lifting Systems

Antreas Kourris; Sihao Sun

cs.ROOct 1, 2026

AFD-CAMLs: Agile Force-Distribution-Aware Planning and Control for Cable-Suspended Aerial Multi-Lifting Systems

Authors: Antreas Kourris, Sihao Sun

Organizations: Faculty of Mechanical Engineering, Delft University of Technology

Abstract

Multiple UAVs can cooperatively transport heavy payloads while controlling their position and orientation. Trajectory-based methods offer high agility while satisfying system constraints, but can produce uneven force distributions when the tension-to-wrench allocation is redundant or ill-conditioned, particularly under geometric mismatch and low-level tracking errors. We propose a hybrid planning-and-control framework to address this problem. A global planner generates payload trajectories and cable-force references by exploring the allocation null space under a prescribed internal-force setting. These references augment the cost of a centralized local planner, promoting feasible force distributions while generating trajectories for all UAVs. An admittance filter then compares the planned forces with onboard cable-tension estimates and adjusts the kinematic references to improve force tracking in degenerate or near-degenerate configurations. Simulations and experiments involving four to ten UAVs demonstrate more balanced tension distributions during both hovering and demanding agile maneuvers, without compromising agility or payload-tracking performance.

Figures & tables

Fig. 1 : Snapshot of four UAVs manipulating a cable-suspended payload containing an additional unknown load using the proposed AFD-CAMLs framework.

Fig. 2 : Information flow of the proposed framework. The centralized global planner provides payload and cable references to the NMPC. Using the load estimator’s feedback the NMPC computes dynamically feasible system trajectories, which are then mapped to individual quadrotor trajectory references. These references are adjusted by the admittance filter using the estimated cable tensions before being tracked by the distributed onboard trajectory controllers.

Parameter	Value	Parameter	Value
$m_{L}$ (4 UAVs)	1.4 kg	$m_{L}$ (10 UAVs)	4 kg
$m_{i}$ (4 UAVs)	0.65 kg	$m_{i}$ (10 UAVs)	0.6 kg
$l$	1 m	$K_{a}$	60 N/m
$D_{a}$	50 Ns/m	$M_{a}$	10 kg
$k_{r}$	1	$k_{f}$	2.5
$k_{n}$	7	$Q_{t}$	1.0

TABLE I : System Parameters

Fig. 3 : Effect of the radial preload magnitude $F_{\mathrm{int,min}}$ on the quadrotor formation.

Fig. 4 : Tension tracking without and with null-space optimization.

Fig. 5 : Effect of the allocation-matrix conditioning on cable-tension tracking for the four- and ten-drone configurations, with and without the admittance filter.

Panel of Figure 7 — Fig. 6 : Tension- and payload-tracking performance with and without the admittance filter.

Fig. 7 : Ten UAVs lifting a payload in the simulated level configuration.

Fig. 8 : Sensitivity of the geometric controller and the NMPC-based framework to payload mass and inertia mismatch during stationary hovering.

Mean Absolute Error (m)	X	Y	Z	3D
No mismatch, without admittance filter	0.0864	0.1099	0.0439	0.1608
No mismatch, with admittance filter	0.0827	0.1207	0.0455	0.1674
1 cm mismatch, without admittance filter	0.092	0.090	0.132	0.221
1 cm mismatch, with admittance filter	0.086	0.069	0.129	0.199

TABLE II : Payload Trajectory Tracking With and Without Cable-Length Mismatch

Fig. 9 : Cable-tension errors and allocation conditioning during agile trajectory tracking, with and without cable-length mismatch and admittance filtering.

Fig. 10 : Tension tracking with and without the admittance filter during hovering.

Mean Absolute Error (m)	X	Y	Z	3D
Baseline [ 8 ]	0.0713	0.1339	0.0506	0.1787
Ours	0.0711	0.1106	0.0429	0.1525
Ours (with unknown load)	0.0860	0.1339	0.0628	0.1974

TABLE III : Payload Trajectory Tracking Performance During Agile Flight

Fig. 11 : Cable-tension errors during agile maneuvering.

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Multi-Uav Ground Reaction Forces Control Architecture

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Cooperative payload transportation using multiple Unmanned Aerial Vehicles (UAVs) poses challenges in stability, coordination, and robustness, especially under external disturbances and unmodeled dynamics. This work proposes a dual-UAV payload transportation framework supported by a compact, custom-designed force sensor measuring the interaction force at the UAV cable anchor point. The sensor design and mathematical model are presented, and its performance is characterized through static and dynamic tests evaluating linearity, hysteresis, repeatability, and crossload. The control architecture follows a cascade structure: fast inner loops handle vehicle stabilization, while outer loops are designed to compensate for the measured forces. The approach is validated through simulations and indoor experiments under position uncertainty. Payload-drop and constrained-space tests assess the proposed sensing and control architecture against literature-based distributed references, showing improved stabilization, coordination, and disturbance rejection. A video of the experiments is available at: https://youtu.be/rIw9-fvV8Qw.

Abstract

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