Task-Driven Closed-Loop Design Optimization of Passively Morphing Quadrotors for Contact-Mediated Narrow Gap Traversal

Etor Arza; Luca Girardi; Gabriel Maquignaz; Javier Paez; Stefan Leutenegger; Stefano Mintchev; Kostas Alexis

cs.RONov 6, 2026

Task-Driven Closed-Loop Design Optimization of Passively Morphing Quadrotors for Contact-Mediated Narrow Gap Traversal

Authors: Etor Arza, Luca Girardi, Gabriel Maquignaz, Javier Paez, Stefan Leutenegger, Stefano Mintchev, Kostas Alexis

Organizations: Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. · Mobile Robotics Lab, Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland. · Agricultural Robotics, University of Bonn, Bonn, Germany.

Abstract

Aerial robots operating in cluttered environments may encounter openings narrower than their nominal width. Existing passively morphing drones reduce their width upon traversal, but they typically involve human-made designs, which can struggle to find the right balance among folding ratio, flight efficiency, and control tracking performance. We introduce a closed-loop design method that jointly optimizes quadrotor geometry and the torque-deflection response of two passively folding arms driven by the success rate in simulation for contact-mediated gap traversal. An optimization algorithm samples candidate designs that are evaluated in a contact-based simulation of the traversal task, where the task success rate is used as a feedback signal to sample progressively better designs over time. The resulting prototype passively reduces its width from 31.3 to 14.1 cm and traverses a 15 cm wide opening at a 2 m/s reference speed. It also tolerates gap approach-angle errors of up to 30 degrees and folds through a 40 cm long confined passage. Compared to a rigid twin, the soft configuration adds only 2 mm of position RMSE and less than 1% electrical power during contact-free flight. These results show that task-driven closed-loop design optimization can produce passively morphing airframes that are compact under contact, tolerant to approach error, and effectively rigid during nominal flight.

Figures & tables

Fig. 1: Task-driven closed-loop design optimization of passively morphing quadrotors for contact-mediated narrow-gap traversal. (a) Physical prototype in extended and folded configurations, with width reduced from $31.3$ to $14.1$ cm. (b) Airframe capabilities: contact-induced arm folding enables traversal through a long tube and narrow openings, including angled approaches. (c) Airframe geometry and passive joint responses are jointly optimized using simulated gap-traversal success rate, with repairs for control authority and joint realizability, followed by fabrication and real-world validation.

Panel of Figure 2 — Fig. 2: (a) Requested (dashed) and repaired (solid) ERC restoring-torque response parameterized by $(a,b,c,d)$ . (b) Geometry parameters on the optimized airframe.

Variables	Parameter	Symbols	Range	Optimized
$g_{1},g_{2}$	Front/Back arm length	$\ell_{\mathrm{F}},\ell_{\mathrm{B}}$	$[5,15]$ cm	$\phantom{-}8.51\phantom{-}/\phantom{-}8.99$
$g_{3},g_{4}$	Left/Right arm length	$\ell_{\mathrm{L}},\ell_{\mathrm{R}}$	$[5,15]$ cm	$\phantom{-}8.89\phantom{-}/\phantom{-}8.98$
$g_{5},g_{6}$	Front/Back propeller roll	$\phi_{\mathrm{F}},\phi_{\mathrm{B}}$	$[-45,45]^{\circ}$	$\phantom{-}0.42\phantom{-}/\phantom{-}0.44$
$g_{7},g_{8}$	Front/Back propeller pitch	$\vartheta_{\mathrm{F}},\vartheta_{\mathrm{B}}$	$[-45,45]^{\circ}$	$\phantom{-}16.57\phantom{-}/\phantom{-}17.21$
$g_{9},g_{10}$	Front/Back propeller yaw	$\psi_{\mathrm{F}},\psi_{\mathrm{B}}$	$[-45,45]^{\circ}$	$\phantom{-}0.00\phantom{-}/\phantom{-}0.00$
$g_{11},g_{12}$	Joint axis roll	$\phi_{\mathrm{j,L}},\phi_{\mathrm{j,R}}$	$[-30,30]^{\circ}$	$-24.15\phantom{-}/\phantom{-}30.00$

TABLE I: Geometry parameter ranges and optimized values.

Fig. 3: Repeated evaluation of four designs with

400

trials per gap width. Left: success probability. Right: mean maximum tilt during the first 0.2 seconds after traversal among successful trials where success exceeds

30\%

	Flight	Recovery
$\bm{e}_{p}$	$(0.05{,}30{,}10)$	$(2{,}2{,}10)$
$\bm{e}_{v}$	$(0.30{,}4{,}15)$	$(3{,}3{,}5)$
$\bm{e}_{R}$	$(4{,}2{,}2)$	$(4{,}2{,}2)$
$\bm{e}_{\omega}$	$(1{,}1{,}0.5)$	$(1{,}0.5{,}0.5)$
$\bm{u}$	$0.12$	$0.12$
$\Delta\bm{u}$	$35$	$80$

Table 5

Panel of Figure 6 — Fig. 4: Simulation optimization results. (a) Candidate success rates (gray), best-so-far curve (black), and BO–CMA-ES transition (dotted) over the $800$ evaluations. Optimized airframe (b) maximally folded and (c) overlaid with its extended configuration in top view; folding reduces its width from $31.3$ to $14.1$ cm ( $55\%$ ). (d) Requested and repaired left/right ERC restoring-torque responses; positive $\theta$ is the folding direction and negative $\theta$ is blocked.

Fig. 5: Success probability for the best designs optimized over the full and reduced design spaces.

Panel of Figure 8 — Fig. 6: Best designs from four optimization runs with different seeds. Top: render of folded airframes. Below: geometry parameters and restoring torque. Colored lines show the parameters of the best found airframe for each seed (color matches render above), while gray lines show all evaluated airframes.

Fig. 7: Traversal of a $15\times 20$ cm opening. Top: video frames of the crossing. Bottom: logged gap-relative position, velocity, tilt, and rotor speeds. Dotted vertical lines mark the frame instants.

Fig. 8: Traversal of a $40$ cm-long, $22.5$ cm-diameter tube. Top: video frames of the traversal. Bottom: entrance-relative position, velocity, tilt, and rotor speeds. The origin is at the tube entrance center where $x=y=z=0$ . Dotted vertical lines mark the frame instants, and shading marks the interval in which the vehicle is inside the tube.

Approach angle	$0^{\circ}$	$10^{\circ}$	$20^{\circ}$	$30^{\circ}$	$35^{\circ}$
Arm dislocation	0	0	0	1	3
Loss of control	0	0	0	0	0
Altitude loss	0	0	0	0	1
Max. altitude loss [m]	$0.50$	$0.50$	$0.51$	$0.49$	$0.96$

TABLE III: Failures over

20

consecutive traversals per approach angle.

Fig. 9: Traversal of a $20$ cm opening at approach angles of $0$ – $35^{\circ}$ . Top: high-speed camera frames. Bottom: top view of the trajectory and boxplots over $20$ traversals per angle of the peak angular velocity and of the altitude loss $\Delta z$ on successful traversals.

Fig. 10: Nominal-flight cost of the passive joints on a

2.4

m-diameter circular trajectory at

1.5

m/s. Left: position error for soft-arm and rigid-arm flights and Morphy [ 9 ] . Right: battery voltage during the six

250

s comparison windows, where line color identifies the arm configuration and line style identifies the battery pack.

Fig. 11: Left: maximum angular velocity norm. Right: maximum tilt. Both are measured over the first

0.2

s after crossing the gap plane at a

20

cm opening.

Aerial Robotics

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

See all 350 papers

Mar 12, 2026

Flight through Narrow Gaps with Morphing-Wing Drones

The size of a narrow gap traversable by a fixed-wing drone is limited by its wingspan. Inspired by birds, here, we enable the traversal of a gap of sub-wingspan width and height using a morphing-wing drone capable of temporarily sweeping in its wings mid-flight. This maneuver poses control challenges due to sudden lift loss during gap-passage at low flight speeds and the need for precisely timed wing-sweep actuation ahead of the gap. To address these challenges, we first develop an aerodynamic model for general wing-sweep morphing drone flight including low flight speeds and post-stall angles of attack. We integrate longitudinal drone dynamics into an optimal reference trajectory generation and Nonlinear Model Predictive Control framework with runtime adaptive costs and constraints. Validated on a 130 g wing-sweep-morphing drone, our method achieves an average altitude error of 5 cm during narrow-gap passage at forward speeds between 5 and 7 m/s, whilst enforcing fully swept wings near the gap across variable threshold distances. Trajectory analysis shows that the drone can compensate for lift loss during gap-passage by accelerating and pitching upwards ahead of the gap to an extent that differs between reference trajectory optimization objectives. We show that our strategy also allows for accurate gap passage on hardware whilst maintaining a constant forward flight speed reference and near-constant altitude.

Julius Wanner, Hoang-Vu Phan, Charbel Toumieh +1

Laboratory of Intelligent Systems, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland · Avian-Insect Robotics in Motion Laboratory, University of Nevada, Reno, USA

Aerial Robotics Trajectory Optimization Nonlinear MPC

Ask PDF

Aug 22, 2026

Vision-Guided Morphing Quadcopter for Multi-Geometry Payload Transport through Narrow Passages

Aerial payload transport using multirotor unmanned aerial vehicles is challenging because payload geometry, contact interaction, grasp stability, flight control, and narrow-passage traversal are strongly coupled during pickup and transport. Object-specific grippers often cannot adapt their footprint or grasp geometry when the payload shape or passage width changes. This paper presents a vision-guided morphing quadcopter for multi-geometry payload transport through narrow passages. The proposed platform uses four hybrid arm-leg structures that function as both landing supports and grasping members. A centrally placed actuator drives a tendon-based morphing mechanism, enabling all four arms to synchronously retract or expand for object grasping, footprint reduction, and post-transport release. Onboard vision estimates the payload geometry and passage width, while endpoint force feedback is used to confirm grasp contact during payload engagement. A phase-wise mission planner, PID-based flight stabilization, and morphology-adaptive grasp controller are implemented in a MuJoCo simulation environment. The framework is evaluated using box, cylindrical, and spherical payloads, representing flat-faced, rolling-curved, and fully curved contact conditions. Across the three cases, the simulated system completes the pickup-transport-release sequence with a maximum RMS position error of 0.31 m, a final drop-zone error below 0.18 m, a compact grasp footprint of 0.09-0.21 m2, and a footprint reduction of 75.0-89.7 percent. The results demonstrate that a single-actuator morphing quadcopter can adapt its grasp footprint for the transport of payloads with different geometries while reducing its overall footprint for narrow-passage traversal.

Aashish Sahu, Shriram Hari, R. Prasanth Kumar

Department of Mechanical and Aerospace Engineering Indian Institute of Technology Hyderabad Hyderabad, India

Aerial Robotics Aerial Manipulation Robotic Grasping Robot Morphology-Control Co-Design

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

Flight through Narrow Gaps with Morphing-Wing Drones

Vision-Guided Morphing Quadcopter for Multi-Geometry Payload Transport through Narrow Passages

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