AeRSoM: An Aerial Rigid-Soft Integrated Manipulator for Contact-Rich Manipulation
Authors: Jiacheng Liang, Hang Zhong, Yaonan Wang, Ge Chen, Zhixing Zhang, Bocheng Tian, Hui Zhang, Li Wen
Organizations: School of Artificial Intelligence and Robotics, Hunan University, Changsha 410082, China and also with the National Engineering Research Center for Robot Visual Perception and Control Technology, Changsha 410082, China · School of Mechanical Engineering and Automation, Beihang University, Beijing 100191, China
Contact-rich aerial manipulation remains fundamentally challenging because interaction forces are directly transmitted to the aerial platform, often leading to instability and degraded task performance. While compliant manipulators can mitigate these effects, existing aerial manipulation systems typically struggle to reconcile interaction compliance with manipulation precision. To this end, this article presents an aerial rigid-soft integrated manipulator (AeRSoM) robot that realizes embodied compliance for aerial manipulation. The proposed system integrates a fully actuated aerial platform, a rigid-soft manipulator, and variable-stiffness regulation to simultaneously achieve stable flight, compliant interaction, and precise manipulation. By distributing compliance throughout the manipulation system, the proposed design leverages distributed embodied compliance to passively absorb contact disturbances while preserving sufficient stiffness for task execution. To fully exploit the mechanical design, a composite control framework is developed for precise end-effector trajectory tracking in the presence of uncertainties and external disturbances. Extensive real-world experiments are conducted in representative contact-rich aerial manipulation tasks, including dynamic transmission-line grasping, physical interaction with a wind turbine blade, peg-in-hole, and screwing operations. The results demonstrate that the proposed rigid-soft integration significantly improves interaction robustness and task adaptability while maintaining manipulation accuracy, highlighting that embodied compliance provides a promising design paradigm for enhancing the safety, robustness, and versatility of aerial manipulation.
Figures & tables
Fig. 1: Schematic diagram of manual operation at high altitude using helicopter (left), and using the presented AeRSoM robot to replace human for aerial manipulation (right).
Fig. 2: Design and implementation of the AeRSoM robot system. (a) Three-dimensional CAD model. (b) Real AeRSoM robot consisting of the fully-actuated aerial vehicle, the rotary mechanism, and the soft robotic arm. (c) Hardware architecture of the AeRSoM robot system.
Fig. 3: Fabrication process and assembly of the soft robotic arm. (i) Selection of the bellows. (ii) Design of the connectors and structural holders. (iii) Assembly of three soft actuators. (iv) Containing the soft actuators with a soft inner membrane. (v) Fabrication and assembly of the stiffening layer. (vi) Assembly of the soft robotic arm, where the stiffening layer is sealed with soft membranes.
Fig. 4: Schematic of the pneumatic control system for the soft robotic arm.
Fig. 5: Stiffness evaluation of the soft robotic arm at three different states: (a) free-hanging, (b) axial when 90 ∘ bending, and (c) tangential when 90 ∘ bending. Snapshots of the soft robotic arm under three different cases: with the jammed stiffening layer ((a1), (b1), and (c1)), with the unjammed stiffening layer ((a2), (b2), and (c2)), without the stiffening layer ((a3), (b3), and (c3)). Force-displacement curves at (a4) the free-hanging state, (b4) the axial state when 90 ∘ bending, and (c4) the tangential state when 90 ∘ bending.
Arms
Stiffening Principle
Stiffening Work Range
Maximum Stiffening Rate
This work
Chain-mail
All directions
1230%
[ 28 ]
Granular jamming
All directions
750%
[ 29 ]
Granular jamming
All directions
900%
[ 30 ]
Granular jamming
Mainly increase in-plane stiffness
36%
[ 31 ]
Layer jamming
All directions
700%
[ 32 ]
Layer jamming
Mainly increase in-plane stiffness
46%
TABLE I: Comparison of different soft robotic arms with variable stiffness capability
Fig. 6: Load capacity evaluation of the soft robotic arm. (a) The soft robotic arm is placed horizontally under the same actuation with different loads ((i) free load, (ii) 150 g, and (iii) 350 g), and the deviation of the soft robotic arm in the z -direction in the inertial frame FI slightly decreases after stiffening compared with the unjammed state ( Δh1=16 mm and Δh2=20 mm). (b) and (c) The relationship between the load weight and displacement in the z -direction in FI with and without stiffening, where the soft arm is place horizontally (b) and vertically (c).
Symbols
Definitions
Frames
FI
Inertial frame
FB
Body frame of the aerial vehicle
FM
Joint frame of the rotary mechanism
FS0
Base frame of the soft robotic arm
FS1
End frame of the soft robotic arm
TABLE II: Nomenclature
Fig. 7: (a) Frame transformation between the base and end of the soft robotic arm. (b) The mapping among the actuator space, joint space, configuration space, and task space. (c) Simulated workspace results of the soft robotic arm.
Fig. 8: Block diagram of the proposed composite control framework of the AeRSoM robot containing the robust flight controller and the RL-Based adaptive soft robotic arm controller.
Fig. 9: Pressure-length hysteresis curves using the EUPI model.
Fig. 10: Offline measurement of task target point, which is constructed the desired end-effector trajectory.
Fig. 11: AeRSoM robot equipped with different end-effectors ((a) gripper, (b) camera probe, and (c) bolt installer) for various aerial manipulation tasks. The end-effectors can be connected modularly and altered rapidly.
Parameter
Value
M∗
diag(8.875I3,0.1560,0.1568,0.2908)
Kp
M−1diag(20I3,0.3I3)
Kv
M−1diag(12I3,0.2I3)
Ke
diag(1.2I3,0.25I3)
ki
0.15
kd
0.5
TABLE III: Selection of the controller gains and parameters
Fig. 12: Snapshots of robust flight tests, where the rotary mechanism rotates to different angles with 0.48 rad/s (from a) 0 ∘ to b) -45 ∘ to c) 45 ∘ back to d) 0 ∘ ) and then the aerial platform is imposed on random artificial disturbances.
Fig. 13: Experimental results of pose errors and estimated wrenches of the aerial vehicle under the coupling disturbances from the rotary mechanism and artificial disturbances.
ex
ey
ez
eroll
epitch
eyaw
κ
I
16.98
30.43
2.52
9.44
10.46
3.17
II
16.98
10.12
2.27
4.16
10.46
2.24
III
4.38
9.65
2.04
4.30
2.57
3.17
IV
12.75
30.43
2.52
9.44
5.47
2.69
V
1.84
2.20
0.49
1.97
1.04
1.69
μ
I
2.77
4.74
0.48
1.96
1.51
1.00
TABLE IV: Maximum κ , mean μ , and standard deviation σ of the absolute errors (Units: cm, deg)
Fig. 14: Motion test of the soft robotic arm from unjamming to jamming.
Fig. 15: Snapshots of the constant curvature model validation process. (a) Free bending. (b) Consider the influence by the motion of the aerial base. (c) Consider the effect of the interaction with the environment, where a thrust gauge is used to apply the interaction for the soft robotic arm, and the maximum interaction force is marked in the snapshots. (a1)-(a3), (b4)-(b6), and (c1)-(c3) The soft robotic arm is placed vertically. (a4)-(a6), (b1)-(b3), and (c4)-(c6) The soft robotic arm is placed horizontally.
Fig. 16: Bending angle errors of three cross-sections and the end face relative to the base plane based on calculations using the constant curvature model.
Cross- section
(a1)- (a3)
(a4)- (a6)
(b1)- (b3)
(b4)- (b6)
(c1)- (c3)
(c4)- (c6)
1st
1.72
2.55
3.25
2.65
2.06
3.88
2nd
2.39
3.10
3.37
3.83
3.70
2.98
3rd
1.56
3.45
3.22
2.63
2.22
3.16
TABLE V: Mean absolute error of the bending angle (Unit: deg)
Fig. 17: Experimental results of the static end tracking of the soft robotic arm via the ablation study. The comparisons among the proposed RL-based adaptive controller, PID with hysteresis model ( 27 ), and only PID.
Fig. 18: Experimental results of the dynamic end tracking of the soft robotic arm. (a) Snapshots of the soft robotic arm at the driven state or non-driven state when the rotary mechanism rotates to different angles (-60 ∘ , -30 ∘ , and 0 ∘ ). In the driven state, the vacuum pumps are active and drive the soft robotic arm using the proposed control strategy. Conversely, in the non-driven state, the vacuum pumps are inactive, and the soft robotic arm remains passive. (b) Trajectory of the end point tracking in the base frame of the soft robotic arm FS0 when the rotary mechanism is at the -90 ∘ , -60 ∘ , -30 ∘ , or 0 ∘ state.
Fig. 19: Setup of aerial dynamic grasping of the transmission line with comparisons. (a) The maximum opening size of the gripper is 42 mm and the width of the transmission line is 9 mm. (b) A thrust gauge is used to apply disturbances forces of different magnitudes to the line in the x -direction under the inertial frame FI . (c) Snapshots of the robot equipped with the soft robotic arm (left) and the purely rigid arm (right).
Fig. 20: Comparative results of aerial grasping. (a)-(c) The fabricated soft robotic arm or (d) the purely rigid arm with the gripper is used for grasping.
Fig. 21: Comparisons of aerial dynamic horizontal grasping of the transmission line. (a1) and (b1) Diagrams of applied force and grasping points. The disturbances originate from the horizontal pull force applied by the servo motor (a2) and the vertical quantitative pull force measured by the ruler markings (b2). (a3) and (b3) Snapshots of the robot equipped with the soft robotic arm (top) and the purely rigid arm (bottom). Time trajectories of attitude errors for the soft robotic arm (a4, b4) and the purely rigid arm (a5, b5). Violin plots show the attitude error of the aerial vehicle under horizontal pull force (a6) and vertical pull force (b6).
Fig. 22: Wind turbine blade employed for experiments (left), and 20 random points are drawn on the surface marked with red dots and their normals are plotted as black arrows (right).
Fig. 23: Snapshots of aerial physical interaction with different points on the wind turbine blade featuring an unstructured surface (top). Violin plots show the position tracking error of the aerial vehicle (middle) and the attitude tracking error of the end-effector (bottom) for three trials.
Fig. 24: Setup of aerial peg-in-hole and screwing task (a) and key snapshots during this manipulation, including approaching (b1), aerial peg-in-hole and screwing (b1), and mission completion and return (b3).
Fig. 25: Trajectory of the AeRSoM during aerial peg-in-hole and screwing a bolt.
In recent years, aerial manipulation has attracted increasing attention as a key to expand the application of aerial robots. In this work, we focus on two major research directions for achieving versatile aerial manipulation: (i) acquiring high environmental adaptability using soft manipulators, and (ii) expanding the feasible wrench space by distributing thrusters along the manipulator. However, no aerial robot has simultaneously satisfied these two requirements. Therefore, in this paper, we propose a morphing rotor-distributed aerial robot with flexible continuum links that achieves both high shape adaptability and an expanded wrench space. The flexible continuum links function as soft manipulators, passively conforming to the shape of the environment, while the thrusters distributed along the continuum links expand the feasible thrust wrench space and enable the end-effector to exert large interaction forces. To realize the proposed robot, it is essential to suppress vibrations of the lightweight continuum links. Thus, we develop a composite leaf-spring structure that provides both high torsional and vertical stiffness, and vibration-suppressing control methods. Using these implementations, we demonstrate stable flight and a variety of aerial manipulation tasks. To the best of our knowledge, this is the first work to realize aerial manipulations using flexible links with an integrated thruster.
Eri Sawada, Kazuki Sugihara, Ayano Miyamichi +2
Department of Mechano-Infomatics, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan
Aerial robots are increasingly moving from remote observation toward physical interaction with objects, surfaces, structures, loads, and surrounding flows. This review argues that aerial manipulation cannot be understood as classical manipulation simply mounted on a flying base. Because flying agents remain aloft through continuous momentum and energy exchange with the surrounding medium, support, locomotion, stabilization, and task-directed interaction are intrinsically coupled. Building on broad views of manipulation as intentional environmental regulation through physical interaction, we propose a medium-aware interpretation of aerial manipulation in which interaction may be mediated by contact, by the surrounding fluid, or by both. The review organizes biological and robotic examples into a repertoire of interaction modes and a capability ladder, then develops an actuation-geometric viewpoint in which redundancy induces task-equivalent fibers. Internal motion along these fibers can trade energy for active readiness, aerodynamic promptness, and passive medium coupling. This perspective clarifies why aerial manipulation is difficult, why biological flyers remain broader than robotic systems, and how future platforms may command forces while also shaping how the medium acts back on them.
Antonio Franchi
Robotics and Mechatronics lab, Faculty of Electrical Engineering, Mathematics & Computer Science, University of Twente, 7522 NB Enschede, The Netherlands · Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy
Multi-link aerial robots can actively deform their articulated structures during flight, giving them strong potential for aerial manipulation. However, they still face substantial challenges in contact-rich aerial manipulation tasks such as surface sliding, which requires both disturbance robustness and compliance to uncertain surface geometry. Force-control strategies such as impedance and admittance control are commonly employed to address these requirements. Although impedance control can provide disturbance-resistant interaction and admittance control can offer compliant adaptation, their opposite force--motion causalities prevent their simultaneous implementation when applied through the same actuation source, such as the rotor thrusts used by conventional aerial robots. To overcome this limitation, we propose a hybrid impedance--admittance control strategy for a multi-link aerial robot. The articulated morphology enables a functional separation of force and motion regulation across joint and rotor actuation sources. In this framework, admittance behavior is generated through joint angle regulation to enhance adaptive interaction, while impedance behavior is achieved by modulating rotor thrust to regulate the sliding motion. This structural coordination allows the robot to leverage the complementary strengths of both control paradigms. As a result, the multi-link aerial robot achieves resilient and adaptive surface sliding. Experimental results demonstrate robust and compliant sliding performance on unknown surfaces.
Zicheng Luo, Maolin Lei, Jinjie Li +3
Department of Mechanical-Engineering, The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan