MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems

Rui Huang; Zhiqian Cai; Siyu Tang; Pengxuan Wei; Lidong Li; Xin Chen; Wenhan Cao; Zhenyu Zhang

cs.ROApr 7, 2026

MARS-Dragonfly: Agile and Robust Flight Control of Modular Aerial Robot Systems

Authors: Rui Huang, Zhiqian Cai, Siyu Tang, Pengxuan Wei, Lidong Li, Xin Chen, Wenhan Cao, Zhenyu Zhang,

Organizations: Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore · Engineering Design and Innovation Centre, National University of Singapore, Singapore 117583, Singapore

Abstract

Modular Aerial Robot Systems (MARS) comprise multiple drone units with reconfigurable connected formations, providing high adaptability to diverse mission scenarios, fault conditions, and payload capacities. However, existing control algorithms for MARS rely on simplified quasi-static models and rule-based allocation, which generate discontinuous and unbounded motor commands. This leads to attitude error accumulation as the number of drone units scales, ultimately causing severe oscillations during docking, separation, and waypoint tracking. To address these limitations, we first design a compact mechanical system that enables passive docking, detection-free passive locking, and magnetic-assisted separation using a single micro servo. Second, we introduce a force-torque-equivalent and polytope-constraint virtual quadrotor that explicitly models feasible wrench sets. Together, these abstractions capture the full MARS dynamics and enable existing quadrotor controllers to be applied across different configurations. We further optimize the yaw angle that maximizes control authority to enhance agility. Third, building on this abstraction, we design a two-stage predictive-allocation pipeline: a constrained predictive tracker computes virtual inputs while respecting force/torque bounds, and a dynamic allocator maps these inputs to individual modules with balanced objectives to produce smooth, trackable motor commands. Simulations across over 10 configurations and real-world experiments demonstrate stable docking, locking, and separation, as well as effective control performance. To our knowledge, this is the first real-world demonstration of MARS achieving agile flight and transport with 40 deg peak pitch while maintaining an average position error of 0.0896 m. The video is available at: https://youtu.be/yqjccrIpz5o

Figures & tables

Fig. 1: Overview of the proposed MARS-Dragonfly framework. (a) Dragonfly-inspired MARS design. (b) A virtual quadrotor abstraction for arbitrary MARS configurations. (c) Enabling various MARS control tasks with a unified APC controller.

Mechanism	Mode	Docking Accuracy Requirement	Diagram	Docking or/and Separation Strategy	Actuators ( a set )	Load
ModQuad [ 12 ]	Docking-only	High ( $\propto$ magnet size)		Magnetic attraction	0	Low
ModQuad-Vi [ 13 ]	Docking-only	High ( $\propto$ magnet size)		Magnetic attraction	0	Low
Magnet matrix [ 14 ]	Separation-only	-		Separation torque due to attitude change	0	Low
Electromagnetic [ 29 ]	Separation-only	-		Switch the electromagnet on or off	4	Medium
BEATLE [ 26 ]	Integrated	Medium		Docking : magnetic attraction Locking : servo-actuated slider-crank with stick insertion. Separation : a servo reverses the polarity of the magnet, and a servo-driven slider- crank mechanism retracts locking stick.	2	High
Our Design	Integrated	Low		Docking : magnetic attraction Locking : asymmetric magnet-actuated twist-lock for passive mechanical interlocking Separation : a servo-driven magnetically repulsive separation	1	High

TABLE I: MARS docking-locking-separation mechanisms

Method	Model	Controller	Task	Control Performance
ModQuad [ 12 ]	Simplified model	PID + Rule-based control allocation	Docking, hovering	Severe oscillation
ModQuad-Vi [ 13 ]	Simplified model	PID + Rule-based control allocation	Docking, hovering	Severe oscillation
ModQuad-DoF [ 27 ]	Simplified model	PID + Rule-based control allocation	Yaw control, hovering	Medium oscillation
Modquad-Gripper [ 28 ]	Simplified model	PID + Rule-based control allocation	Waypoint tracking Object transport	Medium oscillation
Ref. [ 14 ]	Simplified model	Torque peak generation	Separation, hovering	Severe oscillation
Ours	Virtual quadrotor (Nonlinear model)	Abstracted predictive control + Balanced dynamic control allocation	Docking, Separation Trajectory tracking Agile flight Collision-free navigation Object dragging Agile transport	Smooth and Stable

TABLE II: MARS model and control

Fig. 2: Docking, locking and separation sequence inspired by dragonfly mating behavior. The process replicates sequential biological actions, including flying \scriptsize1⃝, curling the abdomen \scriptsize2⃝, interlocking together \scriptsize3⃝, and completing mating \scriptsize4⃝, through staged magnetic attraction docking, passive locking, and controlled magnetic repulsion for active separation.

Fig. 3: Microstructural connections in dragonfly mating that inspire the design of macroscopic mechanical structures. (a) The connection of dragonflies includes two docking parts. A1: the male grasps the female’s neck. A2: the secondary genitalia of the male engages with the terminal end of the female’s abdomen for copulation. (b) Microscopic structure of two docking parts. B1: The dense protrusions provide opposing frictional surfaces to support the neck during mating. B2: The microstructure of the female genital cuticle and tile-like scales of the male copulatory apparatus. (c) C1: To mimic the biomechanical control, we employ magnets to simulate a stable connection. C2: The epidermal structure (blue) and tile-like scale surface (yellow) provide enhanced stability. Inspired by this, we design a locking interface with a similar stabilizing effect. (d) The docking mechanism design with magnet array and locking interface.

Fig. 4: Docking and separation mechanism detail. (a) Exploded view of the mechanism. (b) Front and transparent views of the docking structure. Bottom view illustrating the circular gear arrangement that enables controlled separation. (c) Sequential process of approaching, alignment, docking, passive locking, and gear-actuated separation. (d) Illustration of separation failure modes under different disturbances.

Fig. 5: Magnet arrangement optimization. (a) Original and Halbach array for a row of magnets. (b) The magnets are divided into $L$ layers, with each layer consisting of two groups. (c) $\textcircled{\scriptsize 1}$ – $\textcircled{\scriptsize 3}$ denote the baseline, manually designed Halbach array and optimized configuration, respectively. The optimized magnet orientations and their corresponding magnetic field distributions are shown, where red regions indicate higher field intensity. The objective function values on the outer and inner surfaces versus iteration count are also presented. Configurations $\textcircled{\scriptsize 1}$ , $\textcircled{\scriptsize 2}$ , and $\textcircled{\scriptsize 3}$ are highlighted in black, purple, and yellow, respectively.

Fig. 6: Coordinate system representation for a MARS with two units.

Fig. 7: Model abstraction and APC framework for MARS. Localization provides odometry for high-level planning. Model abstraction converts a given MARS configuration into a virtual quadrotor, which can be either an equal-arm model or an unequal-arm model, and can be extended to configurations carrying external loads. The abstracted dynamic model and control constraints are incorporated into an APC, which computes real-time control inputs for the virtual quadrotor. An optimization-based dynamic control allocation then maps the virtual control commands to thrust and torque commands for all drone units.

Fig. 8: Model abstraction for various MARS configurations. (a) Algorithm validation using virtual quadrotors formed by one, two, and four units. (b) Equal-arm virtual quadrotor. MARS with the optimal yaw angle exhibits an improvement in maximum net torque over the baseline configurations. (c) Unequal-arm virtual quadrotor. Computed virtual representations of MARS configurations, illustrating unbalanced torque along the $x$ - and $y$ -axes. Torque approximation error of each model abstraction, computed using Eq. ( 19 ).

Fig. 9: Model abstraction for arbitrary-shaped transportation using MARS. Unequal- and equal-arm quadrotor formations with (a) 2 units and (b) 3 or 4 units, illustrating object transport.

Fig. 10: Simulation of trajectory tracking and object transport. (a)–(e) Trajectory tracking over ten consecutive laps along a circular path under different configurations. (f)–(j) Circular trajectory tracking on a 1.5 m radius path at 2 m/s with a 1.5 kg payload under different task settings.

(a)	(b)	(c)	(d)	(e)	Average
0.0261	0.0364	0.0385	0.1264	0.1329	0.0721

TABLE III: Trajectory Tracking Error in Fig. 10

Config	Method	Vel (m/s)	Payload (kg)			Method Avg
Config	Method	Vel (m/s)	0.5	1.0	1.5	Method Avg
2 $\times$ 1	Modquad [ 28 ]	1 / 2				–
	Ours: APC	1	0.1349	0.1599	0.1327	0.1815
	Ours: APC	2	0.2134	0.2338	0.2142	0.1815
3 $\times$ 1	Modquad [ 28 ]	1 / 2				–
	Ours: APC	1	0.1960	0.1111	0.1436	0.1619

TABLE IV: Tracking error (m) under different velocities, payload weights, and configurations.

Payload (kg)	Method	XY Error (m)	Max Height Error (m)	Stability
0.5	Modquad [ 12 , 28 ]	N/A	N/A	N/A
0.5	Ours: APC	0.2982	0.5294	Stable
1.0	Modquad [ 12 , 28 ]	N/A	N/A	N/A
1.0	Ours: APC	0.3788	2.5023	Unstable

TABLE V: Comparison of Agile Transport

Model + Controller	Allocation	Task			Average
Model + Controller	Allocation	Tracking	Payload	Dragging	Average
Simplified model + PID	Rule-based	$0.6661$	$1.9003^{*}$	$1.0624^{*}$	$1.2096$
Ours: APC	Rule-based	$0.1764$	$0.7313^{*}$	$1.3434^{*}$	$0.7504$
Ours: APC	Dynamic	$\boldsymbol{0.0261}$	$\boldsymbol{0.2142}$	$\boldsymbol{0.4647}$	$\boldsymbol{0.2350}$

TABLE VI: Tracking Error (m) under Different Ablation Settings

Fig. 11: Passive docking and lock sequences. The timer format is min:sec:ms . (a) Before docking: the servo is not powered, and the mechanism is in the standby state. (b) Initial docking: the male and female components begin to engage under the guidance of magnetic attraction. (c) The male docking mechanism is fully inserted into the female component and begins to lock. (d) The male component passively rotates due to the asymmetric magnetic field, reaching the designed locking position. (e) A $135^{\circ}$ pendulum test demonstrates the strong mechanical strength of the docked connection.

Fig. 12: Manually triggered active separation rotates the male component to repel the connected module. (a) Initial locking state. (b) At 00:00:00, the male component of the rotational docking mechanism is manually rotated to unlock the docking-separation interface. The repulsive force from embedded magnets assists the separation process. (c) Fully separated state at 00:00:04.

Method	Docking	Separation	Agile tracking	Agile flight	Collision free	Agile transport
Modquad [ 12 ]	✓	✗	✗	✗	✗	✗
Modquad-Vi [ 13 ]	✓	✗	✗	✗	✗	✗
Ref [ 14 ]	✗	✓	✗	✗	✗	✗
Ref [ 43 ]	✓	✓	✗	✗	✗	✗
Ours: APC	✓	✓	✓	✓	✓	✓

TABLE VII: Summary of Experimental Tasks Performed by MARS

Method	Docking Mechanism	Max x/y Acceleration ( $\text{m/s}^{2}$ )	Normalized height error
Modquad [ 12 ]	Magnet	Not reported	2.02 = 0.097 / frame size
ModQuad-Vi [ 13 ]	Magnet	4.9	5.15 = 0.577 / frame size
Ours	Mode shift	3.47	0.28 = 0.042 / frame size

TABLE VIII: Comparison of docking methods

Fig. 13: Mid-air docking demonstration. The timer format is min:sec:ms . (a) The hover unit maintains a stable position while the docking unit takes off, approaches, and actively adjusts its trajectory to achieve precise mid-air docking. (b) The docking process starts at 00:47:00 and successfully ends at 00:47:19. (c) Time-series plots of acceleration, attitude angles, and position for both units, showing active trajectory correction of the docking unit, and the docking collision, illustrating the stability of the proposed mid-air docking approach.

Fig. 14: Mid-air separation demonstration. The timer format is min:sec:ms . (a) Two drones docked together take off simultaneously, perform a mid-air separation maneuver, and then independently land. (b) After switching to separation mode, the drones successfully disengage from each other during the time interval 00:12:12 to 00:13:40. (c) Experimental data during the demonstration, showing the acceleration along x, y, z axes, attitude angles (pitch, roll, yaw), and position trajectories. The key events is marked on the plot, including take-off, mid-air separation, and landing states.

Fig. 15: Separation comparison experiments. (a) One side enabled with repulsive force; the other disabled. (b) Initial position at 00:09:13. (c) The mechanism employing a repulsive force achieves successful separation at 00:09:25. (d) Without repulsive force, separation fails until 00:11:19, with a delay of approximately 2 s. (e) Successful separation occurs at 00:12:04.

Method	Separation method	Maximum tilting angle ( ∘ )	Separation time (s)
Ref. [ 14 ]	Separation torque	about 28	5
Ref. [ 29 ]	Electromagnetic	4 (Fully Actuated)	3
Ours	Mode shift	7.2	2.9
Ours	Mode shift + repulsive force	5.8	0.5

TABLE IX: Comparison of separation methods

Fig. 16: Composite images of trajectory tracking. (a) Trajectory tracking at a velocity of 1 m/s . (b) Circular trajectory tracking with the docking mechanism engaged and a desired yaw angle. (c) Collision-free navigation illustrating the robust tracking performance of MARS.

Fig. 17: Agile trajectory tracking. (a) Pitch-angle responses along agile trajectories at $20^{\circ}$ , $30^{\circ}$ , and $40^{\circ}$ without retuning the APC parameters, demonstrating robustness to varying attitude demands. (b) Pitch-angle tracking in (a) was evaluated at the planned spatial path, further demonstrating the robustness of the agile flight controller. (c) Roll-angle tracking along an agile trajectory. (d) The MARS maintains speeds exceeding $2~\text{m/s}$ while following a $0.5~\text{m}$ -radius circular path; the roll and pitch angles approach $30^{\circ}$ , indicating high-agility performance.

Fig. 18: Object transport and dragging (600 g payload, 20% of MARS weight). (a) Circular tracking at 2 m/s demonstrates robust performance while carrying the payload. (b) Payload dragging on the ground further demonstrates robustness under payload–ground contact disturbances. (c) Overlaid trajectories highlight accurate tracking performance.

Fig. 19: Object transportation with a sloshing load. (a) Composite images show the basketball’s oscillatory motion during transport. (b) Attitude error reflects sloshing effects; position tracking confirms controller robustness.

Fig. 20: Agile transport carrying 100 g, 200 g, and 600 g payloads. (a) Pitch angle responses along agile trajectories at $40^{\circ}$ without retuning the APC parameters, demonstrating robustness to varying unmodeled payload disturbances. (b) Overlaid pitch angle trajectories for different payloads at the planned pitch angle and position, further highlighting robust tracking performance of the agile flight controller.

Task	Payload (kg)	Position Error (m)	Attitude Error ( ∘ )
Tracking	100 g	0.0512	2.00
	200 g	0.0409	2.23
	600 g	0.0340	1.77
	Avg	0.0420	2.00
Dragging	100 g	0.0523	2.46
	200 g	0.0492	2.51

TABLE X: Comparison of Agile Transport

Aerial Robotics

Aerial robotics studies the design, perception, control, and coordination of flying robots for navigation, inspection, transport, and physical interaction.

See all 340 papers

Sep 17, 2025

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems

Modular Aerial Robot Systems (MARS) consist of multiple drone modules that are physically bound together to form a single structure for flight. Exploiting structural redundancy, MARS can be reconfigured into different formations to mitigate unit or rotor failures and maintain stable flight. Prior work on MARS self-reconfiguration has solely focused on maximizing controllability margins to tolerate a single rotor or unit fault for rectangular-shaped MARS. We propose TransforMARS, a general fault-tolerant reconfiguration framework that transforms arbitrarily shaped MARS under multiple rotor and unit faults while ensuring continuous in-air stability. Specifically, we develop algorithms to first identify and construct minimum controllable assemblies containing faulty units. We then plan feasible disassembly-assembly sequences to transport MARS units or subassemblies to form target configuration. Our approach enables more flexible and practical feasible reconfiguration. We validate TransforMARS in challenging arbitrarily shaped MARS configurations, demonstrating substantial improvements over prior works in both the capacity of handling diverse configurations and the number of faults tolerated. The videos and source code of this work are available at https://github.com/RuiHuangNUS/TransforMARS

Rui Huang, Zhiyu Gao, Siyu Tang +4

Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583 · Department of Mechanical Engineering, National University of Singapore, Singapore 117583

Multi-Robot Motion Planning Aerial Robotics Robot Failure Recovery Robotics Fault-Tolerant Control

Ask PDF

May 28, 2026

A Progress-Aware Leader-Follower Midair Docking System for Dual-Drone Aerial Manipulation

Reliable midair docking between small unmanned aerial vehicles (UAVs) is essential for modular aerial cooperation and manipulation, but it requires precise relative-pose control and repeatable platform under tight thrust and payload constraints. We present a dual-drone docking platform where two quadrotors operate in a leader-follower formation and dock using a lightweight modular frame with passive magnetic latching. A progress-aware mission supervisor manages phase transitions: approach, alignment, capture, and settle. This platform integrates a complete hardware-software stack (ROS 2 with Crazyflie/PX4 interfaces) and synchronized logging for benchmark evaluation. We evaluate the platform in simulation and real-world experiments using quantitative metrics such as formation error, baseline and yaw consistency, docking success rate, time-to-dock, and failure-mode statistics. The platform enables statistically grounded comparison of docking supervision and synchronization strategies and provides a practical testbed for modular aerial cooperation and repeatable midair aerial manipulation.

Yifan Cai, Jan Ming Kevin Tan, Xiangqi Li +3

Department of Computer Science, University College London, London, United Kingdom · Department of Artificial Intelligence, University of Malaya, Kuala Lumpur, Malaysia

Aerial Robotics Multi-Robot Coordination Quadrotor Control Aerial Manipulation

Ask PDF

Jul 2, 2026

MorphQuad: Morphable Quadrotor for Superhuman Maneuverability, Manipulation, and Resiliency

Infrastructure maintenance, contact-based inspection, and emergency response can benefit from aerial vehicles that act as a flying human hand with extreme maneuverability, manipulation, and resiliency (MMR): maneuverability to fly in arbitrary orientations to reach remote and tight locations; manipulation to point sensors, turn valves, and press tools at arbitrary orientations; resiliency to maintain accurate motion and force control despite disturbances from arbitrary directions, such as wind, ground effects, and friction. Realizing MMR on aerial vehicles requires not only omnidirectional flight; it also requires (I) vectoring of maximum thrust in any direction, to maximize capacity for contact-force application and disturbance rejection, (II) global stability, to enable control over any orientation/position, and (III) compact, standard designs that build upon platforms such as quadrotors to inherit technological know-how. No current aerial vehicle simultaneously enables I--III, due to structural and control limitations that constrain actuation. We present MorphQuad: a morphable quadrotor that enjoys MMR. Key to our approach is a hardware and control co-design: on hardware, we independently articulate each of the four rotor systems via two-axis gimbals; on control, we introduce globally-stable control, and energy-optimal thrust allocation that permits inter-rotor thrust cancellations only to avoid downwash interference and gimbal lock. With fully-onboard autonomy, MorphQuad demonstrates multi-revolution rotation while translating or hovering, for pipe inspection and target tracking (maneuverability); valve turning, perching, and object pressing and pushing with human-level strengths (manipulation); and wind rejection from any direction, even directed to a single rotor, and push-pull recovery (resiliency).

J. Diaz Peon Gonzalez Pacheco, J. Xu, A. Zhao +7

Aerospace Engineering, University of Michigan.

Aerial Robotics Quadrotor Control Aerial Manipulation

Ask PDF

Abstract

Aerial Robotics

Explore similar work

TransforMARS: Fault-Tolerant Self-Reconfiguration for Arbitrarily Shaped Modular Aerial Robot Systems

A Progress-Aware Leader-Follower Midair Docking System for Dual-Drone Aerial Manipulation

MorphQuad: Morphable Quadrotor for Superhuman Maneuverability, Manipulation, and Resiliency